Dont provide output for return instruction

src/targets/gpu/mlir.cpp

@@ -183,11 +183,15 @@ struct mlir_program
 
     MlirAttribute attribute(std::int64_t i) const
     {
-        return mlirIntegerAttrGet(mlirIntegerTypeSignedGet(ctx.get(), 64), i);
+        if (i < 0)
+            MIGRAPHX_THROW("MLIR cant handle negative values since they are ambiguous");
+        return mlirIntegerAttrGet(mlirIntegerTypeGet(ctx.get(), 64), i);
     }
     MlirAttribute attribute(std::uint64_t i) const
     {
-        return mlirIntegerAttrGet(mlirIntegerTypeUnsignedGet(ctx.get(), 64), i);
+        if (i > (std::numeric_limits<std::uint64_t>::max() / 2))
+            MIGRAPHX_THROW("MLIR cant handle large integer values since they are ambiguous");
+        return mlirIntegerAttrGet(mlirIntegerTypeGet(ctx.get(), 64), i);
     }
     MlirAttribute attribute(unsigned char i) const { return attribute(std::uint64_t(i)); }
     MlirAttribute attribute(bool b) const { return mlirBoolAttrGet(ctx.get(), b ? 1 : 0); }
@@ -433,7 +437,8 @@ struct mlir_program
             auto name = get_name(ins);
             auto ops  = create_operation_state(name);
             ops.add_attribute_value(ins->get_operator().to_value());
-            ops.add_results({get_shape(ins)});
+            if(ins->name() != "@return")
+                ops.add_results({get_shape(ins)});
 
             std::vector<MlirValue> inputs;
             transform(
@@ -441,8 +446,11 @@ struct mlir_program
             ops.add_operands(inputs);
 
             auto outputs = insert(fbody, std::move(ops));
-            assert(outputs.size() == 1);
-            ins_map[ins] = outputs.front();
+            if(ins->name() != "@return")
+            {
+                assert(outputs.size() == 1);
+                ins_map[ins] = outputs.front();
+            }
         }
     }
 

test/gpu/mlir.cpp

@@ -33,9 +33,8 @@ TEST_CASE(conv)
     const std::string mlir_output = R"__migraphx__(
 module  {
   func @main(%arg0: tensor<1x8x4x4xf32>, %arg1: tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32> {
-    %0 = migraphx.convolution(%arg0, %arg1) {dilation = [1 : ui64, 1 : ui64], group = 1 : si64, padding = [0 : ui64, 0 : ui64, 0 : ui64, 0 : ui64], padding_mode = 0 : ui64, stride
- = [1 : ui64, 1 : ui64]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
-    %1 = return %0 : tensor<1x2x2x2xf32>
+    %0 = migraphx.convolution(%arg0, %arg1) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
+    return %0 : tensor<1x2x2x2xf32>
   }
 }
 )__migraphx__";
@@ -58,10 +57,10 @@ TEST_CASE(conv_add_relu)
     const std::string mlir_output = R"__migraphx__(
 module  {
   func @main(%arg0: tensor<1x8x4x4xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32> {
-    %0 = migraphx.convolution(%arg0, %arg1) {dilation = [1 : ui64, 1 : ui64], group = 1 : si64, padding = [0 : ui64, 0 : ui64, 0 : ui64, 0 : ui64], padding_mode = 0 : ui64, stride = [1 : ui64, 1 : ui64]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
+    %0 = migraphx.convolution(%arg0, %arg1) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
     %1 = migraphx.add(%0, %arg2) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
     %2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
-    %3 = return %2 : tensor<1x2x2x2xf32>
+    return %2 : tensor<1x2x2x2xf32>
   }
 }
 )__migraphx__";