Handle unsinged integers

src/include/migraphx/op/convolution.hpp

@@ -20,7 +20,7 @@ namespace op {
 
 struct convolution
 {
-    std::vector<std::size_t> padding  = {0, 0};
+    std::vector<std::size_t> padding  = {0, 0, 0, 0};
     std::vector<std::size_t> stride   = {1, 1};
     std::vector<std::size_t> dilation = {1, 1};
 

src/targets/gpu/mlir.cpp

@@ -185,8 +185,11 @@ struct mlir_program
     {
         return mlirIntegerAttrGet(mlirIntegerTypeSignedGet(ctx.get(), 64), i);
     }
-    MlirAttribute attribute(std::uint64_t i) const { return attribute(std::int64_t(i)); }
-    MlirAttribute attribute(unsigned char i) const { return attribute(std::int64_t(i)); }
+    MlirAttribute attribute(std::uint64_t i) const
+    {
+        return mlirIntegerAttrGet(mlirIntegerTypeUnsignedGet(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); }
     MlirAttribute attribute(double d) const
     {

test/gpu/mlir.cpp

@@ -33,8 +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 : si64, 1 : si64], group = 1 : si64, padding = [0 : si64, 0 : si64], padding_mode = 0 : si64, stride = [1 : si64, 1 :
-si64]} : (tensor<1x8x4x4xf32>, 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>
   }
 }
@@ -48,6 +48,7 @@ si64]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
     // Skip test if MLIR is not enabled
     if(s.empty())
         return;
+    std::cout << s << std::endl;
     EXPECT(encode(s) == encode(mlir_output));
     auto op = migraphx::gpu::compile_mlir(m);
 }
@@ -57,7 +58,7 @@ 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 : si64, 1 : si64], group = 1 : si64, padding = [0 : si64, 0 : si64], padding_mode = 0 : si64, stride = [1 : si64, 1 : si64]} : (tensor<1x8x4x4xf32>, 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 = migraphx.add(%0, %arg2) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
     %2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
     %3 = return %2 : tensor<1x2x2x2xf32>
@@ -76,6 +77,7 @@ module  {
     // Skip test if MLIR is not enabled
     if(s.empty())
         return;
+    std::cout << s << std::endl;
     EXPECT(encode(s) == encode(mlir_output));
 }