Reject dictionary inputs when tracing.

The underlying error message was misleading.  See https://github.com/llvm/torch-mlir/issues/1425

python/test/compile_api/tracing.py

@@ -9,9 +9,8 @@ import torch
 
 import torch_mlir
 
+
 class TanhModule(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
     def forward(self, x):
         return torch.ops.aten.tanh(x)
 
@@ -52,3 +51,22 @@ try:
     torch_mlir.compile(TanhModule(), [placeholder], use_tracing=True)
 except Exception as e:
     print(e)
+
+
+class DictModule(torch.nn.Module):
+    def forward(self, x):
+        return x['a'] * 2.0
+
+
+try:
+    # CHECK: Only Tensors, TensorPlaceholders, or a sequences of Tensors and TensorPlaceholders are supported as inputs.
+    torch_mlir.compile(DictModule(), {'a': torch.tensor(3.0)}, use_tracing=True)
+except Exception as e:
+    print(e)
+
+
+try:
+    # CHECK: Only Tensors, TensorPlaceholders, or a sequences of Tensors and TensorPlaceholders are supported as inputs.
+    torch_mlir.compile(DictModule(), [{'a': torch.tensor(3.0)}], use_tracing=True)
+except Exception as e:
+    print(e)

python/torch_mlir/__init__.py

@@ -175,6 +175,13 @@ def compile(model: torch.nn.Module,
     # tensor to a list of a single tensor to make the API more ergonomic.
     if isinstance(example_args, (torch.Tensor, TensorPlaceholder)):
         example_args = (example_args,)
+    
+    # If users passed in anything other than tensors or a list of tensors (e.g.
+    # a dictionary), we can't handle it.
+    if not isinstance(example_args, Sequence):
+        raise Exception(
+            "Only Tensors, TensorPlaceholders, or a sequences of Tensors and "
+            "TensorPlaceholders are supported as inputs.")
 
     # TODO: Don't hardcode "forward". See `torch.onnx.export` and
     # `torch.jit.trace_module` for API inspiration.
@@ -197,8 +204,12 @@ def compile(model: torch.nn.Module,
                 shape = [s if s != -1 else 7 for s in arg.shape]
                 example_args_for_trace.append(
                     torch.ones(*shape, dtype=arg.dtype))
-            else:
+            elif isinstance(arg, torch.Tensor):
                 example_args_for_trace.append(arg)
+            else:
+                raise Exception(
+                    "Only Tensors, TensorPlaceholders, or a sequences of "
+                    "Tensors and TensorPlaceholders are supported as inputs.")
         scripted = torch.jit.trace(model, tuple(example_args_for_trace))
     else:
         scripted = torch.jit.script(model)