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<h1>Source code for torch.profiler.profiler</h1><div class="highlight"><pre>
<span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">gzip</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">shutil</span>
<span class="kn">import</span> <span class="nn">tempfile</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">Self</span>
<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.autograd.profiler</span> <span class="k">as</span> <span class="nn">prof</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">_get_privateuse1_backend_name</span>
<span class="kn">from</span> <span class="nn">torch._C._profiler</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">_add_execution_trace_observer</span><span class="p">,</span>
<span class="n">_disable_execution_trace_observer</span><span class="p">,</span>
<span class="n">_enable_execution_trace_observer</span><span class="p">,</span>
<span class="n">_ExperimentalConfig</span><span class="p">,</span>
<span class="n">_remove_execution_trace_observer</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">kineto_available</span><span class="p">,</span> <span class="n">ProfilerActivity</span>
<span class="kn">from</span> <span class="nn">torch.profiler._memory_profiler</span> <span class="kn">import</span> <span class="n">MemoryProfile</span><span class="p">,</span> <span class="n">MemoryProfileTimeline</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"supported_activities"</span><span class="p">,</span>
<span class="s2">"ProfilerAction"</span><span class="p">,</span>
<span class="s2">"schedule"</span><span class="p">,</span>
<span class="s2">"tensorboard_trace_handler"</span><span class="p">,</span>
<span class="s2">"profile"</span><span class="p">,</span>
<span class="s2">"ExecutionTraceObserver"</span><span class="p">,</span>
<span class="p">]</span>
<span class="n">PROFILER_STEP_NAME</span> <span class="o">=</span> <span class="s2">"ProfilerStep"</span>
<span class="k">class</span> <span class="nc">_NumpyEncoder</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">JSONEncoder</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Json encoder for numpy types (np.int, np.float, np.array etc.)</span>
<span class="sd"> Returns default encoder if numpy is not available</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">default</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obj</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Encode NumPy types to JSON"""</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">JSONEncoder</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obj</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">return</span> <span class="n">obj</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">JSONEncoder</span><span class="o">.</span><span class="n">default</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obj</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">supported_activities</span><span class="p">():</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns a set of supported profiler tracing activities.</span>
<span class="sd"> Note: profiler uses CUPTI library to trace on-device CUDA kernels.</span>
<span class="sd"> In case when CUDA is enabled but CUPTI is not available, passing</span>
<span class="sd"> ``ProfilerActivity.CUDA`` to profiler results in using the legacy CUDA</span>
<span class="sd"> profiling code (same as in the legacy ``torch.autograd.profiler``).</span>
<span class="sd"> This, in turn, results in including CUDA time in the profiler table output,</span>
<span class="sd"> but not in the JSON trace.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">_supported_activities</span><span class="p">()</span>
<span class="k">class</span> <span class="nc">_ITraceObserver</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Abstract interface for a Trace observer.</span>
<span class="sd"> This satisfies 3 methods: start, stop and cleanup"""</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">start</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">stop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">cleanup</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<div class="viewcode-block" id="_KinetoProfile"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile">[docs]</a><span class="k">class</span> <span class="nc">_KinetoProfile</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Low-level profiler wrap the autograd profile</span>
<span class="sd"> Args:</span>
<span class="sd"> activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:</span>
<span class="sd"> ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``,</span>
<span class="sd"> ``torch.profiler.ProfilerActivity.XPU``.</span>
<span class="sd"> Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA</span>
<span class="sd"> or (when available) ProfilerActivity.XPU.</span>
<span class="sd"> record_shapes (bool): save information about operator's input shapes.</span>
<span class="sd"> profile_memory (bool): track tensor memory allocation/deallocation (see ``export_memory_timeline``</span>
<span class="sd"> for more details).</span>
<span class="sd"> with_stack (bool): record source information (file and line number) for the ops.</span>
<span class="sd"> with_flops (bool): use formula to estimate the FLOPS of specific operators</span>
<span class="sd"> (matrix multiplication and 2D convolution).</span>
<span class="sd"> with_modules (bool): record module hierarchy (including function names)</span>
<span class="sd"> corresponding to the callstack of the op. e.g. If module A's forward call's</span>
<span class="sd"> module B's forward which contains an aten::add op,</span>
<span class="sd"> then aten::add's module hierarchy is A.B</span>
<span class="sd"> Note that this support exist, at the moment, only for TorchScript models</span>
<span class="sd"> and not eager mode models.</span>
<span class="sd"> experimental_config (_ExperimentalConfig) : A set of experimental options</span>
<span class="sd"> used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.</span>
<span class="sd"> execution_trace_observer (ExecutionTraceObserver) : A PyTorch Execution Trace Observer object.</span>
<span class="sd"> `PyTorch Execution Traces <https://arxiv.org/pdf/2305.14516.pdf>`__ offer a graph based</span>
<span class="sd"> representation of AI/ML workloads and enable replay benchmarks, simulators, and emulators.</span>
<span class="sd"> When this argument is included the observer start() and stop() will be called for the</span>
<span class="sd"> same time window as PyTorch profiler.</span>
<span class="sd"> acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles</span>
<span class="sd"> .. note::</span>
<span class="sd"> This API is experimental and subject to change in the future.</span>
<span class="sd"> Enabling shape and stack tracing results in additional overhead.</span>
<span class="sd"> When record_shapes=True is specified, profiler will temporarily hold references to the tensors;</span>
<span class="sd"> that may further prevent certain optimizations that depend on the reference count and introduce</span>
<span class="sd"> extra tensor copies.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">activities</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Iterable</span><span class="p">[</span><span class="n">ProfilerActivity</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">record_shapes</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">profile_memory</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">with_stack</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">with_flops</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">with_modules</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">experimental_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_ExperimentalConfig</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">execution_trace_observer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_ITraceObserver</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">acc_events</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">custom_trace_id_callback</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[],</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">activities</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">activities</span><span class="p">)</span> <span class="k">if</span> <span class="n">activities</span> <span class="k">else</span> <span class="n">supported_activities</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">record_shapes</span> <span class="o">=</span> <span class="n">record_shapes</span>
<span class="bp">self</span><span class="o">.</span><span class="n">with_flops</span> <span class="o">=</span> <span class="n">with_flops</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profile_memory</span> <span class="o">=</span> <span class="n">profile_memory</span>
<span class="bp">self</span><span class="o">.</span><span class="n">with_stack</span> <span class="o">=</span> <span class="n">with_stack</span>
<span class="bp">self</span><span class="o">.</span><span class="n">with_modules</span> <span class="o">=</span> <span class="n">with_modules</span>
<span class="bp">self</span><span class="o">.</span><span class="n">experimental_config</span> <span class="o">=</span> <span class="n">experimental_config</span>
<span class="bp">self</span><span class="o">.</span><span class="n">execution_trace_observer</span> <span class="o">=</span> <span class="n">execution_trace_observer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">acc_events</span> <span class="o">=</span> <span class="n">acc_events</span>
<span class="bp">self</span><span class="o">.</span><span class="n">custom_trace_id_callback</span> <span class="o">=</span> <span class="n">custom_trace_id_callback</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">prof</span><span class="o">.</span><span class="n">profile</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mem_tl</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">MemoryProfileTimeline</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CUDA</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activities</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="o">=</span> <span class="s2">"cuda"</span>
<span class="k">elif</span> <span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">XPU</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activities</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="o">=</span> <span class="s2">"xpu"</span>
<span class="k">elif</span> <span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">MTIA</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activities</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="o">=</span> <span class="s2">"mtia"</span>
<span class="k">elif</span> <span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">PrivateUse1</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activities</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="o">=</span> <span class="n">_get_privateuse1_backend_name</span><span class="p">()</span>
<span class="c1"># user-defined metadata to be amended to the trace</span>
<span class="bp">self</span><span class="o">.</span><span class="n">preset_metadata</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">def</span> <span class="nf">start</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">prepare_trace</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">start_trace</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">stop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stop_trace</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">prepare_trace</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">profiler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">acc_events</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler</span> <span class="o">=</span> <span class="n">prof</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span>
<span class="n">use_cpu</span><span class="o">=</span><span class="p">(</span><span class="n">ProfilerActivity</span><span class="o">.</span><span class="n">CPU</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">activities</span><span class="p">),</span>
<span class="n">use_device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_device</span><span class="p">,</span>
<span class="n">record_shapes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">record_shapes</span><span class="p">,</span>
<span class="n">with_flops</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">with_flops</span><span class="p">,</span>
<span class="n">profile_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">profile_memory</span><span class="p">,</span>
<span class="n">with_stack</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">with_stack</span><span class="p">,</span>
<span class="n">with_modules</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">with_modules</span><span class="p">,</span>
<span class="n">use_kineto</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">experimental_config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">experimental_config</span><span class="p">,</span>
<span class="n">acc_events</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">acc_events</span><span class="p">,</span>
<span class="n">custom_trace_id_callback</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">custom_trace_id_callback</span><span class="p">,</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">_prepare_trace</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">start_trace</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">execution_trace_observer</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">execution_trace_observer</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">_start_trace</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">profile_memory</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span><span class="s2">"profile_memory"</span><span class="p">,</span> <span class="s2">"1"</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">with_stack</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span><span class="s2">"with_stack"</span><span class="p">,</span> <span class="s2">"1"</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">record_shapes</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span><span class="s2">"record_shapes"</span><span class="p">,</span> <span class="s2">"1"</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">with_modules</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span><span class="s2">"with_modules"</span><span class="p">,</span> <span class="s2">"1"</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">with_flops</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span><span class="s2">"with_flops"</span><span class="p">,</span> <span class="s2">"1"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kineto_available</span><span class="p">():</span>
<span class="n">dist_info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_distributed_info</span><span class="p">()</span>
<span class="k">if</span> <span class="n">dist_info</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span>
<span class="s2">"distributedInfo"</span><span class="p">,</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">dist_info</span><span class="p">,</span> <span class="bp">cls</span><span class="o">=</span><span class="n">_NumpyEncoder</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">torch</span><span class="p">,</span> <span class="s2">"_inductor"</span><span class="p">):</span>
<span class="kn">import</span> <span class="nn">torch._inductor.config</span> <span class="k">as</span> <span class="nn">inductor_config</span>
<span class="k">if</span> <span class="n">inductor_config</span><span class="o">.</span><span class="n">triton</span><span class="o">.</span><span class="n">cudagraphs</span><span class="p">:</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"DISABLE_CUPTI_LAZY_REINIT"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"1"</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span><span class="s2">"DISABLE_CUPTI_LAZY_REINIT"</span><span class="p">,</span> <span class="s2">"1"</span><span class="p">)</span>
<span class="c1"># FIXME: CUDA Graph does not work well with CUPTI teardown.</span>
<span class="c1"># 1) crashes on 1st lazy CUPTI re-init after teardown (CUDA 11)</span>
<span class="c1"># 2) crashes on 2nd non-lazy CUPTI re-init after teardown (CUDA 12)</span>
<span class="c1"># Workaround: turn off CUPTI teardown when using CUDA Graphs.</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"TEARDOWN_CUPTI"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"0"</span>
<span class="c1"># Insert the preset user metadata to the trace</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">preset_metadata</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_metadata_json</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">stop_trace</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">execution_trace_observer</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">execution_trace_observer</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="fm">__exit__</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<div class="viewcode-block" id="_KinetoProfile.export_chrome_trace"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.export_chrome_trace">[docs]</a> <span class="k">def</span> <span class="nf">export_chrome_trace</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Exports the collected trace in Chrome JSON format. If kineto is enabled, only</span>
<span class="sd"> last cycle in schedule is exported.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span>
<span class="k">if</span> <span class="n">path</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">".gz"</span><span class="p">):</span>
<span class="n">fp</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">NamedTemporaryFile</span><span class="p">(</span><span class="s2">"w+b"</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">".json"</span><span class="p">,</span> <span class="n">delete</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">fp</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">retvalue</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="s2">"rb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
<span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
<span class="n">fout</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">fin</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="k">return</span> <span class="n">retvalue</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
<div class="viewcode-block" id="_KinetoProfile.export_stacks"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.export_stacks">[docs]</a> <span class="k">def</span> <span class="nf">export_stacks</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">metric</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">"self_cpu_time_total"</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Save stack traces to a file</span>
<span class="sd"> Args:</span>
<span class="sd"> path (str): save stacks file to this location;</span>
<span class="sd"> metric (str): metric to use: "self_cpu_time_total" or "self_cuda_time_total"</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">export_stacks</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">metric</span><span class="p">)</span></div>
<div class="viewcode-block" id="_KinetoProfile.toggle_collection_dynamic"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.toggle_collection_dynamic">[docs]</a> <span class="k">def</span> <span class="nf">toggle_collection_dynamic</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">enable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> <span class="n">activities</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">ProfilerActivity</span><span class="p">]</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">"""Toggle collection of activities on/off at any point of collection. Currently supports toggling Torch Ops</span>
<span class="sd"> (CPU) and CUDA activity supported in Kineto</span>
<span class="sd"> Args:</span>
<span class="sd"> activities (iterable): list of activity groups to use in profiling, supported values:</span>
<span class="sd"> ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``</span>
<span class="sd"> Examples:</span>
<span class="sd"> .. code-block:: python</span>
<span class="sd"> with torch.profiler.profile(</span>
<span class="sd"> activities=[</span>
<span class="sd"> torch.profiler.ProfilerActivity.CPU,</span>
<span class="sd"> torch.profiler.ProfilerActivity.CUDA,</span>
<span class="sd"> ]</span>
<span class="sd"> ) as p:</span>
<span class="sd"> code_to_profile_0()</span>
<span class="sd"> // turn off collection of all CUDA activity</span>
<span class="sd"> p.toggle_collection_dynamic(False, [torch.profiler.ProfilerActivity.CUDA])</span>
<span class="sd"> code_to_profile_1()</span>
<span class="sd"> // turn on collection of all CUDA activity</span>
<span class="sd"> p.toggle_collection_dynamic(True, [torch.profiler.ProfilerActivity.CUDA])</span>
<span class="sd"> code_to_profile_2()</span>
<span class="sd"> print(p.key_averages().table(</span>
<span class="sd"> sort_by="self_cuda_time_total", row_limit=-1))</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="p">:</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">toggle_collection_dynamic</span><span class="p">(</span><span class="n">enable</span><span class="p">,</span> <span class="n">activities</span><span class="p">)</span></div>
<div class="viewcode-block" id="_KinetoProfile.key_averages"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.key_averages">[docs]</a> <span class="k">def</span> <span class="nf">key_averages</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">group_by_input_shape</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">group_by_stack_n</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span>
<span class="p">):</span>
<span class="w"> </span><span class="sd">"""Averages events, grouping them by operator name and (optionally) input shapes and</span>
<span class="sd"> stack.</span>
<span class="sd"> .. note::</span>
<span class="sd"> To use shape/stack functionality make sure to set record_shapes/with_stack</span>
<span class="sd"> when creating profiler context manager.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">key_averages</span><span class="p">(</span><span class="n">group_by_input_shape</span><span class="p">,</span> <span class="n">group_by_stack_n</span><span class="p">)</span></div>
<div class="viewcode-block" id="_KinetoProfile.events"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.events">[docs]</a> <span class="k">def</span> <span class="nf">events</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Returns the list of unaggregated profiler events,</span>
<span class="sd"> to be used in the trace callback or after the profiling is finished</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">function_events</span></div>
<div class="viewcode-block" id="_KinetoProfile.add_metadata"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.add_metadata">[docs]</a> <span class="k">def</span> <span class="nf">add_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Adds a user defined metadata with a string key and a string value</span>
<span class="sd"> into the trace file</span>
<span class="sd"> """</span>
<span class="n">wrapped_value</span> <span class="o">=</span> <span class="s1">'"'</span> <span class="o">+</span> <span class="n">value</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'"'</span><span class="p">,</span> <span class="s1">'</span><span class="se">\\</span><span class="s1">"'</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'"'</span>
<span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">_add_metadata_json</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">wrapped_value</span><span class="p">)</span></div>
<div class="viewcode-block" id="_KinetoProfile.add_metadata_json"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.add_metadata_json">[docs]</a> <span class="k">def</span> <span class="nf">add_metadata_json</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Adds a user defined metadata with a string key and a valid json value</span>
<span class="sd"> into the trace file</span>
<span class="sd"> """</span>
<span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">_add_metadata_json</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="_KinetoProfile.preset_metadata_json"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.preset_metadata_json">[docs]</a> <span class="k">def</span> <span class="nf">preset_metadata_json</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Preset a user defined metadata when the profiler is not started</span>
<span class="sd"> and added into the trace file later.</span>
<span class="sd"> Metadata is in the format of a string key and a valid json value</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">preset_metadata</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span></div>
<span class="k">def</span> <span class="nf">_get_distributed_info</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">():</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">backend</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_backend</span><span class="p">()</span>
<span class="n">dist_info</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"backend"</span><span class="p">:</span> <span class="n">backend</span><span class="p">,</span>
<span class="s2">"rank"</span><span class="p">:</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">(),</span>
<span class="s2">"world_size"</span><span class="p">:</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_world_size</span><span class="p">(),</span>
<span class="s2">"pg_count"</span><span class="p">:</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_pg_count</span><span class="p">(),</span>
<span class="s2">"pg_config"</span><span class="p">:</span> <span class="n">dist</span><span class="o">.</span><span class="n">distributed_c10d</span><span class="o">.</span><span class="n">_get_all_pg_configs</span><span class="p">(),</span>
<span class="p">}</span>
<span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="s2">"nccl"</span><span class="p">:</span>
<span class="n">nccl_version</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">nccl</span><span class="o">.</span><span class="n">version</span><span class="p">()</span>
<span class="n">dist_info</span><span class="p">[</span><span class="s2">"nccl_version"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"."</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">nccl_version</span><span class="p">)</span>
<span class="k">return</span> <span class="n">dist_info</span>
<span class="k">def</span> <span class="nf">_memory_profile</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">MemoryProfile</span><span class="p">:</span>
<span class="n">required</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"record_shapes"</span><span class="p">,</span> <span class="s2">"profile_memory"</span><span class="p">,</span> <span class="s2">"with_stack"</span><span class="p">)</span>
<span class="n">missing</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">=True"</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">required</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">)]</span>
<span class="k">if</span> <span class="n">missing</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">"</span><span class="si">{</span><span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">missing</span><span class="p">)</span><span class="si">}</span><span class="s2"> required for memory profiling."</span><span class="p">)</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">kineto_results</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">return</span> <span class="n">MemoryProfile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">kineto_results</span><span class="p">)</span>
<div class="viewcode-block" id="_KinetoProfile.export_memory_timeline"><a class="viewcode-back" href="../../../profiler.html#torch.profiler._KinetoProfile.export_memory_timeline">[docs]</a> <span class="k">def</span> <span class="nf">export_memory_timeline</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""Export memory event information from the profiler collected</span>
<span class="sd"> tree for a given device, and export a timeline plot. There are 3</span>
<span class="sd"> exportable files using ``export_memory_timeline``, each controlled by the</span>
<span class="sd"> ``path``'s suffix.</span>
<span class="sd"> - For an HTML compatible plot, use the suffix ``.html``, and a memory timeline</span>
<span class="sd"> plot will be embedded as a PNG file in the HTML file.</span>
<span class="sd"> - For plot points consisting of ``[times, [sizes by category]]``, where</span>
<span class="sd"> ``times`` are timestamps and ``sizes`` are memory usage for each category.</span>
<span class="sd"> The memory timeline plot will be saved a JSON (``.json``) or gzipped JSON</span>
<span class="sd"> (``.json.gz``) depending on the suffix.</span>
<span class="sd"> - For raw memory points, use the suffix ``.raw.json.gz``. Each raw memory</span>
<span class="sd"> event will consist of ``(timestamp, action, numbytes, category)``, where</span>
<span class="sd"> ``action`` is one of ``[PREEXISTING, CREATE, INCREMENT_VERSION, DESTROY]``,</span>
<span class="sd"> and ``category`` is one of the enums from</span>
<span class="sd"> ``torch.profiler._memory_profiler.Category``.</span>
<span class="sd"> Output: Memory timeline written as gzipped JSON, JSON, or HTML.</span>
<span class="sd"> """</span>
<span class="c1"># Default to device 0, if unset. Fallback on cpu.</span>
<span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="o">!=</span> <span class="s2">"cuda"</span><span class="p">:</span>
<span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_device</span> <span class="o">+</span> <span class="s2">":0"</span>
<span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">device</span> <span class="o">=</span> <span class="s2">"cuda:0"</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"cpu"</span>
<span class="c1"># Construct the memory timeline plot data</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mem_tl</span> <span class="o">=</span> <span class="n">MemoryProfileTimeline</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_memory_profile</span><span class="p">())</span>
<span class="c1"># Depending on the file suffix, save the data as json.gz or json.</span>
<span class="c1"># For html, we can embed the image into an HTML file.</span>
<span class="k">if</span> <span class="n">path</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">".html"</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mem_tl</span><span class="o">.</span><span class="n">export_memory_timeline_html</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">path</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">".gz"</span><span class="p">):</span>
<span class="n">fp</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">NamedTemporaryFile</span><span class="p">(</span><span class="s2">"w+t"</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s2">".json"</span><span class="p">,</span> <span class="n">delete</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">fp</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">if</span> <span class="n">path</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">"raw.json.gz"</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mem_tl</span><span class="o">.</span><span class="n">export_memory_timeline_raw</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mem_tl</span><span class="o">.</span><span class="n">export_memory_timeline</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
<span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"wt"</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
<span class="n">fout</span><span class="o">.</span><span class="n">writelines</span><span class="p">(</span><span class="n">fin</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">fp</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mem_tl</span><span class="o">.</span><span class="n">export_memory_timeline</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span></div></div>