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<h1>Source code for torch.jit</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</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">Iterator</span>
<span class="kn">import</span> <span class="nn">torch._C</span>
<span class="c1"># These are imported so users can access them from the `torch.jit` module</span>
<span class="kn">from</span> <span class="nn">torch._jit_internal</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">_Await</span><span class="p">,</span>
<span class="n">_drop</span><span class="p">,</span>
<span class="n">_IgnoreContextManager</span><span class="p">,</span>
<span class="n">_isinstance</span><span class="p">,</span>
<span class="n">_overload</span><span class="p">,</span>
<span class="n">_overload_method</span><span class="p">,</span>
<span class="n">export</span><span class="p">,</span>
<span class="n">Final</span><span class="p">,</span>
<span class="n">Future</span><span class="p">,</span>
<span class="n">ignore</span><span class="p">,</span>
<span class="n">is_scripting</span><span class="p">,</span>
<span class="n">unused</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.jit._async</span> <span class="kn">import</span> <span class="n">fork</span><span class="p">,</span> <span class="n">wait</span>
<span class="kn">from</span> <span class="nn">torch.jit._await</span> <span class="kn">import</span> <span class="n">_awaitable</span><span class="p">,</span> <span class="n">_awaitable_nowait</span><span class="p">,</span> <span class="n">_awaitable_wait</span>
<span class="kn">from</span> <span class="nn">torch.jit._decomposition_utils</span> <span class="kn">import</span> <span class="n">_register_decomposition</span>
<span class="kn">from</span> <span class="nn">torch.jit._freeze</span> <span class="kn">import</span> <span class="n">freeze</span><span class="p">,</span> <span class="n">optimize_for_inference</span><span class="p">,</span> <span class="n">run_frozen_optimizations</span>
<span class="kn">from</span> <span class="nn">torch.jit._fuser</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">fuser</span><span class="p">,</span>
<span class="n">last_executed_optimized_graph</span><span class="p">,</span>
<span class="n">optimized_execution</span><span class="p">,</span>
<span class="n">set_fusion_strategy</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.jit._ir_utils</span> <span class="kn">import</span> <span class="n">_InsertPoint</span>
<span class="kn">from</span> <span class="nn">torch.jit._script</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">_ScriptProfile</span><span class="p">,</span>
<span class="n">_unwrap_optional</span><span class="p">,</span>
<span class="n">Attribute</span><span class="p">,</span>
<span class="n">CompilationUnit</span><span class="p">,</span>
<span class="n">interface</span><span class="p">,</span>
<span class="n">RecursiveScriptClass</span><span class="p">,</span>
<span class="n">RecursiveScriptModule</span><span class="p">,</span>
<span class="n">script</span><span class="p">,</span>
<span class="n">script_method</span><span class="p">,</span>
<span class="n">ScriptFunction</span><span class="p">,</span>
<span class="n">ScriptModule</span><span class="p">,</span>
<span class="n">ScriptWarning</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.jit._serialization</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">jit_module_from_flatbuffer</span><span class="p">,</span>
<span class="n">load</span><span class="p">,</span>
<span class="n">save</span><span class="p">,</span>
<span class="n">save_jit_module_to_flatbuffer</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.jit._trace</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">_flatten</span><span class="p">,</span>
<span class="n">_get_trace_graph</span><span class="p">,</span>
<span class="n">_script_if_tracing</span><span class="p">,</span>
<span class="n">_unique_state_dict</span><span class="p">,</span>
<span class="n">is_tracing</span><span class="p">,</span>
<span class="n">ONNXTracedModule</span><span class="p">,</span>
<span class="n">TopLevelTracedModule</span><span class="p">,</span>
<span class="n">trace</span><span class="p">,</span>
<span class="n">trace_module</span><span class="p">,</span>
<span class="n">TracedModule</span><span class="p">,</span>
<span class="n">TracerWarning</span><span class="p">,</span>
<span class="n">TracingCheckError</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.utils</span> <span class="kn">import</span> <span class="n">set_module</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"Attribute"</span><span class="p">,</span>
<span class="s2">"CompilationUnit"</span><span class="p">,</span>
<span class="s2">"Error"</span><span class="p">,</span>
<span class="s2">"Future"</span><span class="p">,</span>
<span class="s2">"ScriptFunction"</span><span class="p">,</span>
<span class="s2">"ScriptModule"</span><span class="p">,</span>
<span class="s2">"annotate"</span><span class="p">,</span>
<span class="s2">"enable_onednn_fusion"</span><span class="p">,</span>
<span class="s2">"export_opnames"</span><span class="p">,</span>
<span class="s2">"fork"</span><span class="p">,</span>
<span class="s2">"freeze"</span><span class="p">,</span>
<span class="s2">"ignore"</span><span class="p">,</span>
<span class="s2">"isinstance"</span><span class="p">,</span>
<span class="s2">"load"</span><span class="p">,</span>
<span class="s2">"onednn_fusion_enabled"</span><span class="p">,</span>
<span class="s2">"optimize_for_inference"</span><span class="p">,</span>
<span class="s2">"save"</span><span class="p">,</span>
<span class="s2">"script"</span><span class="p">,</span>
<span class="s2">"script_if_tracing"</span><span class="p">,</span>
<span class="s2">"set_fusion_strategy"</span><span class="p">,</span>
<span class="s2">"strict_fusion"</span><span class="p">,</span>
<span class="s2">"trace"</span><span class="p">,</span>
<span class="s2">"trace_module"</span><span class="p">,</span>
<span class="s2">"unused"</span><span class="p">,</span>
<span class="s2">"wait"</span><span class="p">,</span>
<span class="p">]</span>
<span class="c1"># For backwards compatibility</span>
<span class="n">_fork</span> <span class="o">=</span> <span class="n">fork</span>
<span class="n">_wait</span> <span class="o">=</span> <span class="n">wait</span>
<span class="n">_set_fusion_strategy</span> <span class="o">=</span> <span class="n">set_fusion_strategy</span>
<span class="k">def</span> <span class="nf">export_opnames</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Generate new bytecode for a Script module.</span>
<span class="sd"> Returns what the op list would be for a Script Module based off the current code base.</span>
<span class="sd"> If you have a LiteScriptModule and want to get the currently present</span>
<span class="sd"> list of ops call _export_operator_list instead.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_export_opnames</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">_c</span><span class="p">)</span>
<span class="c1"># torch.jit.Error</span>
<span class="n">Error</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">JITException</span>
<span class="n">set_module</span><span class="p">(</span><span class="n">Error</span><span class="p">,</span> <span class="s2">"torch.jit"</span><span class="p">)</span>
<span class="c1"># This is not perfect but works in common cases</span>
<span class="n">Error</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="s2">"Error"</span>
<span class="n">Error</span><span class="o">.</span><span class="vm">__qualname__</span> <span class="o">=</span> <span class="s2">"Error"</span>
<span class="c1"># for use in python if using annotate</span>
<div class="viewcode-block" id="annotate"><a class="viewcode-back" href="../../generated/torch.jit.annotate.html#torch.futures.annotate">[docs]</a><span class="k">def</span> <span class="nf">annotate</span><span class="p">(</span><span class="n">the_type</span><span class="p">,</span> <span class="n">the_value</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Use to give type of `the_value` in TorchScript compiler.</span>
<span class="sd"> This method is a pass-through function that returns `the_value`, used to hint TorchScript</span>
<span class="sd"> compiler the type of `the_value`. It is a no-op when running outside of TorchScript.</span>
<span class="sd"> Though TorchScript can infer correct type for most Python expressions, there are some cases where</span>
<span class="sd"> type inference can be wrong, including:</span>
<span class="sd"> - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`</span>
<span class="sd"> - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume</span>
<span class="sd"> it is type `T` rather than `Optional[T]`</span>
<span class="sd"> Note that `annotate()` does not help in `__init__` method of `torch.nn.Module` subclasses because it</span>
<span class="sd"> is executed in eager mode. To annotate types of `torch.nn.Module` attributes,</span>
<span class="sd"> use :meth:`~torch.jit.Annotate` instead.</span>
<span class="sd"> Example:</span>
<span class="sd"> .. testcode::</span>
<span class="sd"> import torch</span>
<span class="sd"> from typing import Dict</span>
<span class="sd"> @torch.jit.script</span>
<span class="sd"> def fn():</span>
<span class="sd"> # Telling TorchScript that this empty dictionary is a (str -> int) dictionary</span>
<span class="sd"> # instead of default dictionary type of (str -> Tensor).</span>
<span class="sd"> d = torch.jit.annotate(Dict[str, int], {})</span>
<span class="sd"> # Without `torch.jit.annotate` above, following statement would fail because of</span>
<span class="sd"> # type mismatch.</span>
<span class="sd"> d["name"] = 20</span>
<span class="sd"> .. testcleanup::</span>
<span class="sd"> del fn</span>
<span class="sd"> Args:</span>
<span class="sd"> the_type: Python type that should be passed to TorchScript compiler as type hint for `the_value`</span>
<span class="sd"> the_value: Value or expression to hint type for.</span>
<span class="sd"> Returns:</span>
<span class="sd"> `the_value` is passed back as return value.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">the_value</span></div>
<div class="viewcode-block" id="script_if_tracing"><a class="viewcode-back" href="../../generated/torch.jit.script_if_tracing.html#torch.futures.script_if_tracing">[docs]</a><span class="k">def</span> <span class="nf">script_if_tracing</span><span class="p">(</span><span class="n">fn</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Compiles ``fn`` when it is first called during tracing.</span>
<span class="sd"> ``torch.jit.script`` has a non-negligible start up time when it is first called due to</span>
<span class="sd"> lazy-initializations of many compiler builtins. Therefore you should not use</span>
<span class="sd"> it in library code. However, you may want to have parts of your library work</span>
<span class="sd"> in tracing even if they use control flow. In these cases, you should use</span>
<span class="sd"> ``@torch.jit.script_if_tracing`` to substitute for</span>
<span class="sd"> ``torch.jit.script``.</span>
<span class="sd"> Args:</span>
<span class="sd"> fn: A function to compile.</span>
<span class="sd"> Returns:</span>
<span class="sd"> If called during tracing, a :class:`ScriptFunction` created by `torch.jit.script` is returned.</span>
<span class="sd"> Otherwise, the original function `fn` is returned.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_script_if_tracing</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span></div>
<span class="c1"># for torch.jit.isinstance</span>
<div class="viewcode-block" id="isinstance"><a class="viewcode-back" href="../../generated/torch.jit.isinstance.html#torch.futures.isinstance">[docs]</a><span class="k">def</span> <span class="nf">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">target_type</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Provide container type refinement in TorchScript.</span>
<span class="sd"> It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,</span>
<span class="sd"> ``Dict[str, List[torch.Tensor]]``, ``Optional[Tuple[int,str,int]]``. It can also</span>
<span class="sd"> refine basic types such as bools and ints that are available in TorchScript.</span>
<span class="sd"> Args:</span>
<span class="sd"> obj: object to refine the type of</span>
<span class="sd"> target_type: type to try to refine obj to</span>
<span class="sd"> Returns:</span>
<span class="sd"> ``bool``: True if obj was successfully refined to the type of target_type,</span>
<span class="sd"> False otherwise with no new type refinement</span>
<span class="sd"> Example (using ``torch.jit.isinstance`` for type refinement):</span>
<span class="sd"> .. testcode::</span>
<span class="sd"> import torch</span>
<span class="sd"> from typing import Any, Dict, List</span>
<span class="sd"> class MyModule(torch.nn.Module):</span>
<span class="sd"> def __init__(self):</span>
<span class="sd"> super().__init__()</span>
<span class="sd"> def forward(self, input: Any): # note the Any type</span>
<span class="sd"> if torch.jit.isinstance(input, List[torch.Tensor]):</span>
<span class="sd"> for t in input:</span>
<span class="sd"> y = t.clamp(0, 0.5)</span>
<span class="sd"> elif torch.jit.isinstance(input, Dict[str, str]):</span>
<span class="sd"> for val in input.values():</span>
<span class="sd"> print(val)</span>
<span class="sd"> m = torch.jit.script(MyModule())</span>
<span class="sd"> x = [torch.rand(3,3), torch.rand(4,3)]</span>
<span class="sd"> m(x)</span>
<span class="sd"> y = {"key1":"val1","key2":"val2"}</span>
<span class="sd"> m(y)</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">target_type</span><span class="p">)</span></div>
<div class="viewcode-block" id="strict_fusion"><a class="viewcode-back" href="../../generated/torch.jit.strict_fusion.html#torch.futures.strict_fusion">[docs]</a><span class="k">class</span> <span class="nc">strict_fusion</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Give errors if not all nodes have been fused in inference, or symbolically differentiated in training.</span>
<span class="sd"> Example:</span>
<span class="sd"> Forcing fusion of additions.</span>
<span class="sd"> .. code-block:: python</span>
<span class="sd"> @torch.jit.script</span>
<span class="sd"> def foo(x):</span>
<span class="sd"> with torch.jit.strict_fusion():</span>
<span class="sd"> return x + x + x</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="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_jit_internal</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Only works in script mode"</span><span class="p">)</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">tb</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="k">pass</span></div>
<span class="c1"># Context manager for globally hiding source ranges when printing graphs.</span>
<span class="c1"># Note that these functions are exposed to Python as static members of the</span>
<span class="c1"># Graph class, so mypy checks need to be skipped.</span>
<span class="nd">@contextmanager</span>
<span class="k">def</span> <span class="nf">_hide_source_ranges</span><span class="p">()</span> <span class="o">-></span> <span class="n">Iterator</span><span class="p">[</span><span class="kc">None</span><span class="p">]:</span>
<span class="n">old_enable_source_ranges</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Graph</span><span class="o">.</span><span class="n">global_print_source_ranges</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Graph</span><span class="o">.</span><span class="n">set_global_print_source_ranges</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="k">yield</span>
<span class="k">finally</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Graph</span><span class="o">.</span><span class="n">set_global_print_source_ranges</span><span class="p">(</span><span class="n">old_enable_source_ranges</span><span class="p">)</span> <span class="c1"># type: ignore[attr-defined]</span>
<div class="viewcode-block" id="enable_onednn_fusion"><a class="viewcode-back" href="../../generated/torch.jit.enable_onednn_fusion.html#torch.futures.enable_onednn_fusion">[docs]</a><span class="k">def</span> <span class="nf">enable_onednn_fusion</span><span class="p">(</span><span class="n">enabled</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Enable or disables onednn JIT fusion based on the parameter `enabled`."""</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_set_llga_enabled</span><span class="p">(</span><span class="n">enabled</span><span class="p">)</span></div>
<div class="viewcode-block" id="onednn_fusion_enabled"><a class="viewcode-back" href="../../generated/torch.jit.onednn_fusion_enabled.html#torch.futures.onednn_fusion_enabled">[docs]</a><span class="k">def</span> <span class="nf">onednn_fusion_enabled</span><span class="p">():</span>
<span class="w"> </span><span class="sd">"""Return whether onednn JIT fusion is enabled."""</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_llga_enabled</span><span class="p">()</span></div>
<span class="k">del</span> <span class="n">Any</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_init</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"JIT initialization failed"</span><span class="p">)</span>
</pre></div>
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