Jupyter walkthrough trace_models

[BillyGun27]

          can fixed with this patch

diff --git a/python/tvm/relax/frontend/torch/fx_translator.py b/python/tvm/relax/frontend/torch/fx_translator.py
index fde31af60..392ff2b39 100644
--- a/python/tvm/relax/frontend/torch/fx_translator.py
+++ b/python/tvm/relax/frontend/torch/fx_translator.py
@@ -177,6 +177,23 @@ class TorchFXImporter:
             return self._call_binary_op(
                 relax.op.add, relax.const(lhs, dtype=rhs.struct_info.dtype), rhs
             )
+        elif isinstance(lhs, int):
+            return self._call_binary_op(
+                relax.op.add, relax.const(lhs, dtype="int64"), rhs
+            )
+        elif isinstance(rhs, int):
+            return self._call_binary_op(
+                relax.op.add, lhs, relax.const(rhs, dtype="int64")
+            )
+        elif isinstance(lhs, float):
+            return self._call_binary_op(
+                relax.op.add, relax.const(lhs, dtype="float32"), rhs
+            )
+        elif isinstance(rhs, float):
+            return self._call_binary_op(
+                relax.op.add, lhs, relax.const(rhs, dtype="float32")
+            )
+
         return lhs + rhs
 
     def _max(self, node: fx.node.Node) -> relax.Expr:

Originally posted by @haili-tian in #36 (comment)

after adding this code patch, it could fix the clip_to_text_embeddings(pipe) function, but when execute vae_to_image(pipe) encountered another error

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
File /opt/anaconda/envs/venv-mlc/lib/python3.10/site-packages/torch/_dynamo/output_graph.py:670, in OutputGraph.call_user_compiler(self, gm)
    669 else:
--> 670     compiled_fn = compiler_fn(gm, self.fake_example_inputs())
    671 _step_logger()(logging.INFO, f"done compiler function {name}")

File /opt/anaconda/envs/venv-mlc/lib/python3.10/site-packages/torch/_dynamo/debug_utils.py:1055, in wrap_backend_debug.<locals>.debug_wrapper(gm, example_inputs, **kwargs)
   1054 else:
-> 1055     compiled_gm = compiler_fn(gm, example_inputs)
   1057 return compiled_gm

File /opt/anaconda/envs/venv-mlc/lib/python3.10/site-packages/tvm/relax/frontend/torch/dynamo.py:161, in dynamo_capture_subgraphs.<locals>._capture(graph_module, example_inputs)
    160 input_info = [(tuple(tensor.shape), str(tensor.dtype)) for tensor in example_inputs]
--> 161 mod_ = from_fx(
    162     graph_module,
    163     input_info,
    164     keep_params_as_input=keep_params_as_input,
    165     unwrap_unit_return_tuple=True,
    166 )
    167 new_name = f"subgraph_{len(mod.get_global_vars())}"

File /opt/anaconda/envs/venv-mlc/lib/python3.10/site-packages/tvm/relax/frontend/torch/fx_translator.py:1492, in from_fx(model, input_info, keep_params_as_input, unwrap_unit_return_tuple, no_bind_return_tuple)
   1404 """Convert a PyTorch FX GraphModule to a Relax program
   1405 
   1406 Parameters
   (...)
   1490 check the placeholder rows in the beginning of the tabular.
   1491 """
-> 1492 return TorchFXImporter().from_fx(
   1493     model, input_info, keep_params_as_input, unwrap_unit_return_tuple, no_bind_return_tuple
   1494 )

File /opt/anaconda/envs/venv-mlc/lib/python3.10/site-packages/tvm/relax/frontend/torch/fx_translator.py:1377, in TorchFXImporter.from_fx(self, model, input_info, keep_params_as_input, unwrap_unit_return_tuple, no_bind_return_tuple)
   1375 func_name = node.name.rstrip("0123456789_")
   1376 assert (
-> 1377     func_name in self.convert_map
   1378 ), f"Unsupported function type {func_name}"
   1379 self.env[node] = self.convert_map[func_name](node)

AssertionError: Unsupported function type conv2d

The above exception was the direct cause of the following exception:

BackendCompilerFailed                     Traceback (most recent call last)
Cell In[13], line 1
----> 1 vae = vae_to_image(pipe)

Cell In[10], line 22, in vae_to_image(pipe)
     19 vae_to_image = VAEModelWrapper(vae)
     21 z = torch.rand((1, 4, 64, 64), dtype=torch.float32)
---> 22 mod = dynamo_capture_subgraphs(
     23     vae_to_image.forward,
     24     z,
     25     keep_params_as_input=True,
     26 )
     27 assert len(mod.functions) == 1
     29 return tvm.IRModule({"vae": mod["subgraph_0"]})

File /opt/anaconda/envs/venv-mlc/lib/python3.10/site-packages/tvm/relax/frontend/torch/dynamo.py:175, in dynamo_capture_subgraphs(model, *params, **kwargs)
    172 compiled_model = torch.compile(model, backend=_capture)
    174 with torch.no_grad():
--> 175     compiled_model(*params, **kwargs)
    177 return mod

it seems conv2d is considered call_function instead of call_module causing it to not exist in self.convert_map but I do not understand why conv2d become a function instead of module.