[TorchToStableHLO] Fix unsigned integer conversion in ValueTensorLiteralOp.

lib/Conversion/TorchToStablehlo/Basic.cpp

@@ -876,14 +876,17 @@ LogicalResult ConvertAtenOp<ValueTensorLiteralOp>::matchAndRewrite(
   // Tensors with integer types need to be converted to signless integer
   // element type. All tensors with element types other than integer can reuse
   // existing elements attribute.
-  // TODO: what about unsigned integer?
   if (auto elements = dyn_cast<DenseIntElementsAttr>(op.getValueAttr())) {
     Type builtinTensorElemTy = resultType.getElementType();
     unsigned bitWidth = builtinTensorElemTy.getIntOrFloatBitWidth();
+    bool isUnsigned =
+        cast<IntegerType>(builtinTensorElemTy).isUnsignedInteger();
 
     DenseElementsAttr valueAttr =
         elements.mapValues(builtinTensorElemTy, [&](const APInt &v) {
-          return APInt(bitWidth, v.getSExtValue());
+          APInt intValue =
+              isUnsigned ? v.zextOrTrunc(bitWidth) : v.sextOrTrunc(bitWidth);
+          return intValue;
         });
     rewriter.replaceOpWithNewOp<stablehlo::ConstantOp>(op, resultType,
                                                        valueAttr);

projects/pt1/e2e_testing/xfail_sets.py

@@ -1771,6 +1771,8 @@
     #     raise TimeoutError(self.error_message)
     # TimeoutError: Timeout
     "BertModule_basic",
+    "UInt8Tensor_basic",
+    "BoolTensor_basic",
 }
 
 # Write the TOSA set as a "passing" set as it is very early in development

projects/pt1/python/torch_mlir_e2e_test/test_suite/constant_alloc.py

@@ -2102,3 +2102,46 @@ def forward(self, a):
     @register_test_case(module_factory=lambda: AtenDiagEmbedNonDefault4DDiag())
     def AtenDiagEmbedNonDefault4DDiag_basic(module, tu: TestUtils):
         module.forward(tu.rand(2, 3, 4, 5))
+
+
+# ==============================================================================
+
+
+class UInt8Tensor(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+        ]
+    )
+    def forward(self):
+        x = torch.tensor([128], dtype=torch.uint8)
+        return torch.ops.aten.to(x, dtype=torch.float32)
+
+
+@register_test_case(module_factory=lambda: UInt8Tensor())
+def UInt8Tensor_basic(module, tu: TestUtils):
+    module.forward()
+
+
+class BoolTensor(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+        ]
+    )
+    def forward(self):
+        x = torch.tensor([True], dtype=torch.bool)
+        return torch.ops.aten.to(x, dtype=torch.float32)
+
+
+@register_test_case(module_factory=lambda: BoolTensor())
+def BoolTensor_basic(module, tu: TestUtils):
+    module.forward()