[TOSA] Retag resource literals to signless constants (#4367)

- Extend ValueTensorLiteral lowering so DenseResourceElementsAttr
integers are rebuilt with signless element types before emitting
tosa.const, matching the converted tensor type.
- Add lit coverage for resource-backed i32/i64 vtensor literals.
- Add FX importer e2e tests that return constant int32/int64 tensors.

Author: Lallapallooza

lib/Conversion/TorchToTosa/TorchToTosa.cpp

@@ -13,6 +13,7 @@
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tosa/IR/TosaOps.h"
 #include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
+#include "mlir/IR/DialectResourceBlobManager.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/Pass/Pass.h"
 #include "mlir/Transforms/DialectConversion.h"
@@ -3161,7 +3162,21 @@ LogicalResult ConvertAtenOp<ValueTensorLiteralOp>::matchAndRewrite(
       return success();
     }
   }
-  rewriter.replaceOpWithNewOp<tosa::ConstOp>(op, outputTy, adaptor.getValue());
+  ElementsAttr attr = cast<ElementsAttr>(adaptor.getValue());
+  if (auto res = dyn_cast<DenseResourceElementsAttr>(attr)) {
+    // Resource blobs preserve the producer's signedness, so retag them here to
+    // keep TOSA constants signless and avoid downstream type mismatches.
+    auto shapedAttrTy = cast<ShapedType>(res.getType());
+    if (auto intTy = dyn_cast<IntegerType>(shapedAttrTy.getElementType())) {
+      if (!intTy.isSignless()) {
+        auto signlessTy =
+            IntegerType::get(rewriter.getContext(), intTy.getWidth());
+        auto newTy = RankedTensorType::get(shapedAttrTy.getShape(), signlessTy);
+        attr = DenseResourceElementsAttr::get(newTy, res.getRawHandle());
+      }
+    }
+  }
+  rewriter.replaceOpWithNewOp<tosa::ConstOp>(op, outputTy, attr);
   return success();
 }
 

projects/pt1/e2e_testing/xfail_sets.py

@@ -680,6 +680,8 @@
     "ChannelShuffleTrailingOnes_basic",
     "ChannelShuffleDynamicDims_basic",
     "ConstantBoolParameterModule_basic",
+    "ConstantInt32ParameterModule_basic",
+    "ConstantInt64ParameterModule_basic",
     "ContainsIntList_False",
     "ContainsIntList_True",
     "Conv2dFP16NoBiasModule_basic",
@@ -2882,6 +2884,8 @@
     "ColumnStack1dModule_basic",
     "ColumnStack0dModule_basic",
     "ConstantBoolParameterModule_basic",
+    "ConstantInt32ParameterModule_basic",
+    "ConstantInt64ParameterModule_basic",
     "ContainsIntList_False",
     "ContainsIntList_True",
     "Conv1dModule_basic",
@@ -3671,7 +3675,6 @@
     "BoolIntTrueModule_basic",
     "BroadcastDynamicDimModule_basic",
     "CeilFloatModule_basic",
-    "ConstantBoolParameterModule_basic",
     "ContainsIntList_False",
     "ContainsIntList_True",
     "Conv1dModule_basic",

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

@@ -2976,6 +2976,49 @@ def TensorIntModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ConstantInt32ParameterModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.tensor = torch.tensor([0, 10, 128, 17000], dtype=torch.int32)
+
+    @export
+    @annotate_args(
+        [
+            None,
+        ]
+    )
+    def forward(self):
+        return self.tensor
+
+
+@register_test_case(module_factory=lambda: ConstantInt32ParameterModule())
+def ConstantInt32ParameterModule_basic(module, tu: TestUtils):
+    module.forward()
+
+
+class ConstantInt64ParameterModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.tensor = torch.tensor([1, -2, 3, -4], dtype=torch.int64)
+
+    @export
+    @annotate_args(
+        [
+            None,
+        ]
+    )
+    def forward(self):
+        return self.tensor
+
+
+@register_test_case(module_factory=lambda: ConstantInt64ParameterModule())
+def ConstantInt64ParameterModule_basic(module, tu: TestUtils):
+    module.forward()
+
+
+# ==============================================================================
+
+
 class tensorFloatModule(torch.nn.Module):
     def __init__(self):
         super().__init__()

test/Conversion/TorchToTosa/basic.mlir

@@ -1051,6 +1051,44 @@ func.func @torch.vtensor.literal_si32$basic() -> !torch.vtensor<[1,512],si32> {
 
 // -----
 
+// CHECK-LABEL: @torch.vtensor.literal_resource_si32$basic(
+// CHECK: %[[CST:.*]] = "tosa.const"() <{values = dense_resource<torch_resource_i32> : tensor<4xi32>}>
+// CHECK: %[[RET:.*]] = torch_c.from_builtin_tensor %[[CST]] : tensor<4xi32> -> !torch.vtensor<[4],si32>
+// CHECK: return %[[RET]] : !torch.vtensor<[4],si32>
+func.func @torch.vtensor.literal_resource_si32$basic() -> !torch.vtensor<[4],si32> {
+  %0 = torch.vtensor.literal(dense_resource<torch_resource_i32> : tensor<4xsi32>) : !torch.vtensor<[4],si32>
+  return %0 : !torch.vtensor<[4],si32>
+}
+
+{-#
+  dialect_resources: {
+    builtin: {
+      torch_resource_i32: "0x08000000000000000a0000008000000068420000"
+    }
+  }
+#-}
+
+// -----
+
+// CHECK-LABEL: @torch.vtensor.literal_resource_si64$basic(
+// CHECK: %[[CST:.*]] = "tosa.const"() <{values = dense_resource<torch_resource_i64> : tensor<3xi64>}>
+// CHECK: %[[RET:.*]] = torch_c.from_builtin_tensor %[[CST]] : tensor<3xi64> -> !torch.vtensor<[3],si64>
+// CHECK: return %[[RET]] : !torch.vtensor<[3],si64>
+func.func @torch.vtensor.literal_resource_si64$basic() -> !torch.vtensor<[3],si64> {
+  %0 = torch.vtensor.literal(dense_resource<torch_resource_i64> : tensor<3xsi64>) : !torch.vtensor<[3],si64>
+  return %0 : !torch.vtensor<[3],si64>
+}
+
+{-#
+  dialect_resources: {
+    builtin: {
+      torch_resource_i64: "0x08000000010000000000000002000000000000000300000000000000"
+    }
+  }
+#-}
+
+// -----
+
 // CHECK-LABEL:   func.func @torch.aten.arange.start_step() -> !torch.vtensor<[5],si64> {
 // CHECK:           %[[VAL_0:.*]] = torch.constant.none
 // CHECK:           %[[VAL_1:.*]] = torch.constant.int 0