HFTracer.trace can now take callables and torch.nn.Module (#18457)

* Enable HFTracer to trace with custom dummy inputs instead of pre-computed ones

* Add HFTracer.trace docstring, and make it possible to handle callable and torch.nn.Module in general

* Remove pdb comment

* Apply suggestions

src/transformers/utils/fx.py

@@ -882,11 +882,51 @@ class HFTracer(Tracer):
     def proxy(self, node):
         return HFProxy(node, self)
 
-    def trace(self, root: PreTrainedModel, concrete_args: Optional[Dict[str, Any]] = None) -> Graph:
+    def trace(
+        self,
+        root: Union[torch.nn.Module, Callable[..., Any]],
+        concrete_args: Optional[Dict[str, Any]] = None,
+        dummy_inputs: Optional[Dict[str, Any]] = None,
+        complete_concrete_args_with_inputs_not_in_dummy_inputs: bool = True,
+    ) -> Graph:
+        """
+        Traces `root` and returns the corresponding FX `torch.fx.Graph` representation. `root` can either be a
+        `torch.nn.Module` instance or a Python callable. Note that after this call, `self.root` may be different from
+        the `root` passed in here. For example, when a free function is passed to `trace()`, we will create a
+        `torch.nn.Module` instance to use as the root and add embedded constants to.
+
+        Args:
+            root (`torch.nn.Module` or  `Callable`):
+                Either a `torch.nn.Module`` or a function to be traced through. If root is not a
+                [`~transformers.PreTrainedModel`], then `dummy_inputs` must be passed, otherwise tracing will fail.
+            concrete_args (`Dict[str, Any], *optional*):
+                Concrete arguments that should not be treated as Proxies
+            dummy_inputs (`Dict[str, Any]`, *optional*):
+                The dummy inputs needed to handle data-dependent control-flow if `root` is not a
+                [`~transformers.PreTrainedModel`]. It can also be used when `root` is a
+                [`~transformers.PreTrainedModel`] to specify custom dummy inputs for a subset or all the model inputs.
+            complete_concrete_args_with_inputs_not_in_dummy_inputs (`bool`, *optional*, defaults to `True`):
+                If `True`, and `dummy_inputs` is specified, every argument that `root` can take that is not in
+                `dummy_inputs` and not in `concrete_args` will be added to `concrete_args`, otherwise does nothing.
+
+        Returns:
+            `torch.fx.Graph`:
+                A FX `torch.fx.Graph` representing the semantics of the passed-in `root`.
+
+        """
+        sig = inspect.signature(root.forward if isinstance(root, torch.nn.Module) else root)
+
         if concrete_args is None:
             concrete_args = {}
 
-        sig = inspect.signature(root.forward)
+        if dummy_inputs is not None and complete_concrete_args_with_inputs_not_in_dummy_inputs:
+            for param in sig.parameters.values():
+                if param.name in dummy_inputs:
+                    continue
+                if param.default is inspect.Parameter.empty:
+                    raise ValueError(f"You need to specify a default value for the parameter {param.name}.")
+            concrete_args.update({p.name: p.default for p in sig.parameters.values() if p.name not in dummy_inputs})
+
         input_names = sig.parameters.keys() - concrete_args.keys()
 
         # Creating a random input shape to generate dummy inputs.
@@ -898,11 +938,24 @@ class HFTracer(Tracer):
             num_choices = _generate_random_int(low=2, high=5)
             shape.insert(1, num_choices)
 
-        inputs = {}
+        inputs = dict(dummy_inputs) if dummy_inputs is not None else {}
         for input_name in input_names:
+            if input_name in inputs:
+                continue
+            # We enforce that root must either be a PreTrainedModel or deserialized from a serialized traced model to
+            # be able to use HFTracer._generate_dummy_input.
+            if isinstance(root, PreTrainedModel) or type(root).__qualname__.startswith("_deserialize_graph_module"):
                 inputs.update(self._generate_dummy_input(root, input_name, shape))
+            else:
+                raise RuntimeError(
+                    f"Could not generate input named {input_name} for because root is not a"
+                    " transformers.PreTrainedModel."
+                )
 
-        concrete_metas = {input_name: input_.to("meta") for input_name, input_ in inputs.items()}
+        concrete_metas = {
+            input_name: input_.to("meta") if isinstance(input_, torch.Tensor) else input_
+            for input_name, input_ in inputs.items()
+        }
         for param in sig.parameters.values():
             if param.kind == inspect.Parameter.VAR_KEYWORD and param.name not in input_names:
                 concrete_metas[f"**{param.name}"] = {}