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<div class="section" id="torch-profiler">
<h1>torch.profiler<a class="headerlink" href="#torch-profiler" title="Permalink to this heading">¶</a></h1>
<div class="section" id="module-torch.profiler">
<span id="overview"></span><h2>Overview<a class="headerlink" href="#module-torch.profiler" title="Permalink to this heading">¶</a></h2>
<p>PyTorch Profiler is a tool that allows the collection of performance metrics during training and inference.
Profiler’s context manager API can be used to better understand what model operators are the most expensive,
examine their input shapes and stack traces, study device kernel activity and visualize the execution trace.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>An earlier version of the API in <a class="reference internal" href="autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.autograd</span></code></a> module is considered legacy and will be deprecated.</p>
</div>
</div>
<div class="section" id="api-reference">
<h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">_KinetoProfile</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">activities</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="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</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="n"><span class="pre">execution_trace_observer</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/profiler/profiler.html#_KinetoProfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile" title="Permalink to this definition">¶</a></dt>
<dd><p>Low-level profiler wrap the autograd profile</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>activities</strong> (<em>iterable</em>) – list of activity groups (CPU, CUDA) to use in profiling, supported values:
<code class="docutils literal notranslate"><span class="pre">torch.profiler.ProfilerActivity.CPU</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.profiler.ProfilerActivity.CUDA</span></code>.
Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.</p></li>
<li><p><strong>record_shapes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – save information about operator’s input shapes.</p></li>
<li><p><strong>profile_memory</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – track tensor memory allocation/deallocation (see <code class="docutils literal notranslate"><span class="pre">export_memory_timeline</span></code>
for more details).</p></li>
<li><p><strong>with_stack</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – record source information (file and line number) for the ops.</p></li>
<li><p><strong>with_flops</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – use formula to estimate the FLOPS of specific operators
(matrix multiplication and 2D convolution).</p></li>
<li><p><strong>with_modules</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – record module hierarchy (including function names)
corresponding to the callstack of the op. e.g. If module A’s forward call’s
module B’s forward which contains an aten::add op,
then aten::add’s module hierarchy is A.B
Note that this support exist, at the moment, only for TorchScript models
and not eager mode models.</p></li>
<li><p><strong>experimental_config</strong> (<em>_ExperimentalConfig</em>) – A set of experimental options
used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.</p></li>
<li><p><strong>execution_trace_observer</strong> (<em>ExecutionTraceObserver</em>) – A PyTorch Execution Trace Observer object.
<a class="reference external" href="https://arxiv.org/pdf/2305.14516.pdf">PyTorch Execution Traces</a> offer a graph based
representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
When this argument is included the observer start() and stop() will be called for the
same time window as PyTorch profiler.</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This API is experimental and subject to change in the future.</p>
<p>Enabling shape and stack tracing results in additional overhead.
When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
that may further prevent certain optimizations that depend on the reference count and introduce
extra tensor copies.</p>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.add_metadata">
<span class="sig-name descname"><span class="pre">add_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a user defined metadata with a string key and a string value
into the trace file</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.add_metadata_json">
<span class="sig-name descname"><span class="pre">add_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata_json" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds a user defined metadata with a string key and a valid json value
into the trace file</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.events">
<span class="sig-name descname"><span class="pre">events</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.events"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.events" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the list of unaggregated profiler events,
to be used in the trace callback or after the profiling is finished</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_chrome_trace">
<span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
<dd><p>Exports the collected trace in Chrome JSON format.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_memory_timeline">
<span class="sig-name descname"><span class="pre">export_memory_timeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</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/profiler/profiler.html#_KinetoProfile.export_memory_timeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_memory_timeline" title="Permalink to this definition">¶</a></dt>
<dd><p>Export memory event information from the profiler collected
tree for a given device, and export a timeline plot. There are 3
exportable files using <code class="docutils literal notranslate"><span class="pre">export_memory_timeline</span></code>, each controlled by the
<code class="docutils literal notranslate"><span class="pre">path</span></code>’s suffix.</p>
<ul class="simple">
<li><p>For an HTML compatible plot, use the suffix <code class="docutils literal notranslate"><span class="pre">.html</span></code>, and a memory timeline
plot will be embedded as a PNG file in the HTML file.</p></li>
<li><p>For plot points consisting of <code class="docutils literal notranslate"><span class="pre">[times,</span> <span class="pre">[sizes</span> <span class="pre">by</span> <span class="pre">category]]</span></code>, where
<code class="docutils literal notranslate"><span class="pre">times</span></code> are timestamps and <code class="docutils literal notranslate"><span class="pre">sizes</span></code> are memory usage for each category.
The memory timeline plot will be saved a JSON (<code class="docutils literal notranslate"><span class="pre">.json</span></code>) or gzipped JSON
(<code class="docutils literal notranslate"><span class="pre">.json.gz</span></code>) depending on the suffix.</p></li>
<li><p>For raw memory points, use the suffix <code class="docutils literal notranslate"><span class="pre">.raw.json.gz</span></code>. Each raw memory
event will consist of <code class="docutils literal notranslate"><span class="pre">(timestamp,</span> <span class="pre">action,</span> <span class="pre">numbytes,</span> <span class="pre">category)</span></code>, where
<code class="docutils literal notranslate"><span class="pre">action</span></code> is one of <code class="docutils literal notranslate"><span class="pre">[PREEXISTING,</span> <span class="pre">CREATE,</span> <span class="pre">INCREMENT_VERSION,</span> <span class="pre">DESTROY]</span></code>,
and <code class="docutils literal notranslate"><span class="pre">category</span></code> is one of the enums from
<code class="docutils literal notranslate"><span class="pre">torch.profiler._memory_profiler.Category</span></code>.</p></li>
</ul>
<p>Output: Memory timeline written as gzipped JSON, JSON, or HTML.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_stacks">
<span class="sig-name descname"><span class="pre">export_stacks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'self_cpu_time_total'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_stacks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_stacks" title="Permalink to this definition">¶</a></dt>
<dd><p>Save stack traces in a file in a format suitable for visualization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – save stacks file to this location;</p></li>
<li><p><strong>metric</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – metric to use: “self_cpu_time_total” or “self_cuda_time_total”</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Example of using FlameGraph tool:</p>
<ul class="simple">
<li><p>git clone <a class="reference external" href="https://github.com/brendangregg/FlameGraph">https://github.com/brendangregg/FlameGraph</a></p></li>
<li><p>cd FlameGraph</p></li>
<li><p>./flamegraph.pl –title “CPU time” –countname “us.” profiler.stacks > perf_viz.svg</p></li>
</ul>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.key_averages">
<span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.key_averages" title="Permalink to this definition">¶</a></dt>
<dd><p>Averages events, grouping them by operator name and (optionally) input shapes and
stack.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To use shape/stack functionality make sure to set record_shapes/with_stack
when creating profiler context manager.</p>
</div>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler._KinetoProfile.preset_metadata_json">
<span class="sig-name descname"><span class="pre">preset_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.preset_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.preset_metadata_json" title="Permalink to this definition">¶</a></dt>
<dd><p>Preset a user defined metadata when the profiler is not started
and added into the trace file later.
Metadata is in the format of a string key and a valid json value</p>
<dl class="field-list simple">
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.profiler.profile">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</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">activities</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="n"><span class="pre">schedule</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="n"><span class="pre">on_trace_ready</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="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</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="n"><span class="pre">execution_trace_observer</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="n"><span class="pre">use_cuda</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/profiler/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile" title="Permalink to this definition">¶</a></dt>
<dd><p>Profiler context manager.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>activities</strong> (<em>iterable</em>) – list of activity groups (CPU, CUDA) to use in profiling, supported values:
<code class="docutils literal notranslate"><span class="pre">torch.profiler.ProfilerActivity.CPU</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.profiler.ProfilerActivity.CUDA</span></code>.
Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.</p></li>
<li><p><strong>schedule</strong> (<em>Callable</em>) – callable that takes step (int) as a single parameter and returns
<code class="docutils literal notranslate"><span class="pre">ProfilerAction</span></code> value that specifies the profiler action to perform at each step.</p></li>
<li><p><strong>on_trace_ready</strong> (<em>Callable</em>) – callable that is called at each step when <code class="docutils literal notranslate"><span class="pre">schedule</span></code>
returns <code class="docutils literal notranslate"><span class="pre">ProfilerAction.RECORD_AND_SAVE</span></code> during the profiling.</p></li>
<li><p><strong>record_shapes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – save information about operator’s input shapes.</p></li>
<li><p><strong>profile_memory</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – track tensor memory allocation/deallocation.</p></li>
<li><p><strong>with_stack</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – record source information (file and line number) for the ops.</p></li>
<li><p><strong>with_flops</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – use formula to estimate the FLOPs (floating point operations) of specific operators
(matrix multiplication and 2D convolution).</p></li>
<li><p><strong>with_modules</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – record module hierarchy (including function names)
corresponding to the callstack of the op. e.g. If module A’s forward call’s
module B’s forward which contains an aten::add op,
then aten::add’s module hierarchy is A.B
Note that this support exist, at the moment, only for TorchScript models
and not eager mode models.</p></li>
<li><p><strong>experimental_config</strong> (<em>_ExperimentalConfig</em>) – A set of experimental options
used for Kineto library features. Note, backward compatibility is not guaranteed.</p></li>
<li><p><strong>execution_trace_observer</strong> (<em>ExecutionTraceObserver</em>) – A PyTorch Execution Trace Observer object.
<a class="reference external" href="https://arxiv.org/pdf/2305.14516.pdf">PyTorch Execution Traces</a> offer a graph based
representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.
When this argument is included the observer start() and stop() will be called for the
same time window as PyTorch profiler. See the examples section below for a code sample.</p></li>
<li><p><strong>use_cuda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – <div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 1.8.1: </span>use <code class="docutils literal notranslate"><span class="pre">activities</span></code> instead.</p>
</div>
</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Use <a class="reference internal" href="#torch.profiler.schedule" title="torch.profiler.schedule"><code class="xref py py-func docutils literal notranslate"><span class="pre">schedule()</span></code></a> to generate the callable schedule.
Non-default schedules are useful when profiling long training jobs
and allow the user to obtain multiple traces at the different iterations
of the training process.
The default schedule simply records all the events continuously for the
duration of the context manager.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Use <a class="reference internal" href="#torch.profiler.tensorboard_trace_handler" title="torch.profiler.tensorboard_trace_handler"><code class="xref py py-func docutils literal notranslate"><span class="pre">tensorboard_trace_handler()</span></code></a> to generate result files for TensorBoard:</p>
<p><code class="docutils literal notranslate"><span class="pre">on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)</span></code></p>
<p>After profiling, result files can be found in the specified directory. Use the command:</p>
<p><code class="docutils literal notranslate"><span class="pre">tensorboard</span> <span class="pre">--logdir</span> <span class="pre">dir_name</span></code></p>
<p>to see the results in TensorBoard.
For more information, see
<a class="reference external" href="https://github.com/pytorch/kineto/tree/master/tb_plugin">PyTorch Profiler TensorBoard Plugin</a></p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Enabling shape and stack tracing results in additional overhead.
When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
that may further prevent certain optimizations that depend on the reference count and introduce
extra tensor copies.</p>
</div>
<p>Examples:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span>
<span class="n">activities</span><span class="o">=</span><span class="p">[</span>
<span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CUDA</span><span class="p">,</span>
<span class="p">]</span>
<span class="p">)</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
<span class="n">code_to_profile</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span>
<span class="n">sort_by</span><span class="o">=</span><span class="s2">"self_cuda_time_total"</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
</pre></div>
</div>
<p>Using the profiler’s <code class="docutils literal notranslate"><span class="pre">schedule</span></code>, <code class="docutils literal notranslate"><span class="pre">on_trace_ready</span></code> and <code class="docutils literal notranslate"><span class="pre">step</span></code> functions:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Non-default profiler schedule allows user to turn profiler on and off</span>
<span class="c1"># on different iterations of the training loop;</span>
<span class="c1"># trace_handler is called every time a new trace becomes available</span>
<span class="k">def</span> <span class="nf">trace_handler</span><span class="p">(</span><span class="n">prof</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span>
<span class="n">sort_by</span><span class="o">=</span><span class="s2">"self_cuda_time_total"</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
<span class="c1"># prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json")</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span>
<span class="n">activities</span><span class="o">=</span><span class="p">[</span>
<span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span><span class="p">,</span>
<span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CUDA</span><span class="p">,</span>
<span class="p">],</span>
<span class="c1"># In this example with wait=1, warmup=1, active=2, repeat=1,</span>
<span class="c1"># profiler will skip the first step/iteration,</span>
<span class="c1"># start warming up on the second, record</span>
<span class="c1"># the third and the forth iterations,</span>
<span class="c1"># after which the trace will become available</span>
<span class="c1"># and on_trace_ready (when set) is called;</span>
<span class="c1"># the cycle repeats starting with the next step</span>
<span class="n">schedule</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">schedule</span><span class="p">(</span>
<span class="n">wait</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">warmup</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">active</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="n">repeat</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="n">on_trace_ready</span><span class="o">=</span><span class="n">trace_handler</span>
<span class="c1"># on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')</span>
<span class="c1"># used when outputting for tensorboard</span>
<span class="p">)</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
<span class="k">for</span> <span class="nb">iter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">code_iteration_to_profile</span><span class="p">(</span><span class="nb">iter</span><span class="p">)</span>
<span class="c1"># send a signal to the profiler that the next iteration has started</span>
<span class="n">p</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>The following sample shows how to setup up an Execution Trace Observer (<cite>execution_trace_observer</cite>)</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span>
<span class="o">...</span>
<span class="n">execution_trace_observer</span><span class="o">=</span><span class="p">(</span>
<span class="n">ExecutionTraceObserver</span><span class="p">()</span><span class="o">.</span><span class="n">register_callback</span><span class="p">(</span><span class="s2">"./execution_trace.json"</span><span class="p">)</span>
<span class="p">),</span>
<span class="p">)</span> <span class="k">as</span> <span class="n">p</span><span class="p">:</span>
<span class="k">for</span> <span class="nb">iter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span>
<span class="n">code_iteration_to_profile</span><span class="p">(</span><span class="nb">iter</span><span class="p">)</span>
<span class="n">p</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
</pre></div>
</div>
<p>You can also refer to test_execution_trace_with_kineto() in tests/profiler/test_profiler.py.
Note: One can also pass any object satisfying the _ITraceObserver interface.</p>
<dl class="py method">
<dt class="sig sig-object py" id="torch.profiler.profile.step">
<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.step" title="Permalink to this definition">¶</a></dt>
<dd><p>Signals the profiler that the next profiling step has started.</p>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.profiler.ProfilerAction">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">ProfilerAction</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 internal" href="_modules/torch/profiler/profiler.html#ProfilerAction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.ProfilerAction" title="Permalink to this definition">¶</a></dt>
<dd><p>Profiler actions that can be taken at the specified intervals</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.profiler.ProfilerActivity">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">ProfilerActivity</span></span><a class="headerlink" href="#torch.profiler.ProfilerActivity" title="Permalink to this definition">¶</a></dt>
<dd><p>Members:</p>
<p>CPU</p>
<p>XPU</p>
<p>MTIA</p>
<p>CUDA</p>
<dl class="py property">
<dt class="sig sig-object py" id="torch.profiler.ProfilerActivity.name">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><a class="headerlink" href="#torch.profiler.ProfilerActivity.name" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.profiler.schedule">
<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">schedule</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">wait</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmup</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#schedule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.schedule" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a callable that can be used as profiler <code class="docutils literal notranslate"><span class="pre">schedule</span></code> argument. The profiler will skip
the first <code class="docutils literal notranslate"><span class="pre">skip_first</span></code> steps, then wait for <code class="docutils literal notranslate"><span class="pre">wait</span></code> steps, then do the warmup for the next <code class="docutils literal notranslate"><span class="pre">warmup</span></code> steps,
then do the active recording for the next <code class="docutils literal notranslate"><span class="pre">active</span></code> steps and then repeat the cycle starting with <code class="docutils literal notranslate"><span class="pre">wait</span></code> steps.
The optional number of cycles is specified with the <code class="docutils literal notranslate"><span class="pre">repeat</span></code> parameter, the zero value means that
the cycles will continue until the profiling is finished.</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.Callable" title="(in Python v3.12)"><em>Callable</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.profiler.tensorboard_trace_handler">
<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">tensorboard_trace_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_name</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="n"><span class="pre">use_gzip</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/profiler/profiler.html#tensorboard_trace_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.tensorboard_trace_handler" title="Permalink to this definition">¶</a></dt>
<dd><p>Outputs tracing files to directory of <code class="docutils literal notranslate"><span class="pre">dir_name</span></code>, then that directory can be
directly delivered to tensorboard as logdir.
<code class="docutils literal notranslate"><span class="pre">worker_name</span></code> should be unique for each worker in distributed scenario,
it will be set to ‘[hostname]_[pid]’ by default.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
</div>
<div class="section" id="intel-instrumentation-and-tracing-technology-apis">
<h2>Intel Instrumentation and Tracing Technology APIs<a class="headerlink" href="#intel-instrumentation-and-tracing-technology-apis" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.profiler.itt.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</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/profiler/itt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Check if ITT feature is available or not</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.profiler.itt.mark">
<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.mark" title="Permalink to this definition">¶</a></dt>
<dd><p>Describe an instantaneous event that occurred at some point.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>msg</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – ASCII message to associate with the event.</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.profiler.itt.range_push">
<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_push" title="Permalink to this definition">¶</a></dt>
<dd><p>Pushes a range onto a stack of nested range span. Returns zero-based
depth of the range that is started.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>msg</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – ASCII message to associate with range</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.profiler.itt.range_pop">
<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_pop" title="Permalink to this definition">¶</a></dt>
<dd><p>Pops a range off of a stack of nested range spans. Returns the
zero-based depth of the range that is ended.</p>
</dd></dl>
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<li><a class="reference internal" href="#torch.profiler.ProfilerAction"><code class="docutils literal notranslate"><span class="pre">ProfilerAction</span></code></a></li>
<li><a class="reference internal" href="#torch.profiler.ProfilerActivity"><code class="docutils literal notranslate"><span class="pre">ProfilerActivity</span></code></a><ul>
<li><a class="reference internal" href="#torch.profiler.ProfilerActivity.name"><code class="docutils literal notranslate"><span class="pre">ProfilerActivity.name</span></code></a></li>
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<li><a class="reference internal" href="#torch.profiler.schedule"><code class="docutils literal notranslate"><span class="pre">schedule()</span></code></a></li>
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<li><a class="reference internal" href="#intel-instrumentation-and-tracing-technology-apis">Intel Instrumentation and Tracing Technology APIs</a><ul>
<li><a class="reference internal" href="#torch.profiler.itt.is_available"><code class="docutils literal notranslate"><span class="pre">is_available()</span></code></a></li>
<li><a class="reference internal" href="#torch.profiler.itt.mark"><code class="docutils literal notranslate"><span class="pre">mark()</span></code></a></li>
<li><a class="reference internal" href="#torch.profiler.itt.range_push"><code class="docutils literal notranslate"><span class="pre">range_push()</span></code></a></li>
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