[cleanup] Make diagnostics better

Also remove some unused imports.

lib/RefBackend/RefBackend.cpp

@@ -142,9 +142,12 @@ static LogicalResult mungeFunction(
   SmallVector<Type> newArgTypes;
   for (auto arg : func.getArguments()) {
     auto type = arg.getType();
-    if (!isArgMemRefTypeValid(type))
-      return emitError(arg.getLoc(),
-                       "argument must be a memref of f32, f64, i32, i64, i8, i1");
+    if (!isArgMemRefTypeValid(type)) {
+      return emitError(arg.getLoc())
+          .append("argument must be a memref of f32, f64, i32, i64, i8, i1 but "
+                  "got ",
+                  type);
+    }
     auto cast = b.create<memref::CastOp>(arg.getLoc(), type, arg);
     arg.replaceAllUsesExcept(cast, cast);
     arg.setType(getAbiTypeForMemRef(type));

python/torch_mlir_e2e_test/configs/native_torch.py

@@ -3,9 +3,6 @@
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 # Also available under a BSD-style license. See LICENSE.
 
-import copy
-from typing import Any
-
 import torch
 
 from torch_mlir_e2e_test.framework import TestConfig, Trace, TraceItem

python/torch_mlir_e2e_test/linalg_on_tensors_backends/refbackend.py

@@ -22,7 +22,7 @@ __all__ = [
 
 def assert_arg_type_is_supported(ty):
     SUPPORTED = [np.float32, np.float64, np.uint8, np.int8, np.int32, np.int64, np.bool_]
-    assert ty in SUPPORTED, f"Only numpy arrays with dtypes in {SUPPORTED} are supported"
+    assert ty in SUPPORTED, f"Only numpy arrays with dtypes in {SUPPORTED} are supported, but got {ty}"
 
 
 memref_type_to_np_dtype = {

test/RefBackend/munge-calling-conventions.mlir

@@ -1,4 +1,4 @@
-// RUN: torch-mlir-opt %s -refback-munge-calling-conventions -split-input-file | FileCheck %s
+// RUN: torch-mlir-opt %s -refback-munge-calling-conventions -split-input-file -verify-diagnostics | FileCheck %s
 
 // CHECK-LABEL:   func.func @f(
 // CHECK-SAME:            %[[ARG0:.*]]: memref<*xf32>) attributes {llvm.emit_c_interface} {
@@ -70,3 +70,10 @@ func.func @multiple_return_values(%arg0: memref<?xf32>, %arg1: memref<?xf32>, %a
 func.func @two_return_values(%arg0: memref<?xf32>, %arg1: memref<?xi64>) -> (memref<?xf32>, memref<?xi64>) {
   return %arg0 ,%arg1 : memref<?xf32>, memref<?xi64>
 }
+
+// -----
+
+// expected-error-re @+1 {{argument must be a memref of {{.*}} but got 'tensor<?xf32>'}}
+func.func @f(%arg0: tensor<?xf32>) {
+  return
+}