Import all initialized tensors as dense constants (#30)

* Import initialized tensor as dense attribute

* Import all initialize tensors as dense constants

* Remove unintentional code

* Fix value attribute format in shape inference tests of reshape

* Readd rank check for reshape's shape inference

* Remove a redundant variable

Co-authored-by: Gheorghe-Teodor Bercea <gt.bercea@gmail.com>

src/builder/frontend_dialect_helper.cpp

@@ -129,10 +129,16 @@ mlir::Value InitializedTensorMapping::EmitInitializerForInputTensor(
   // Initializer for input.
   onnx::TensorProto initializer = GetInitializedTensor(name);
 
+  // Tensor dimensions.
+  llvm::ArrayRef<int64_t> tensorDims(initializer.dims().data(),
+      initializer.dims().size());
+
   // Emit ConstantOp and record the mapping between the input and
   // the constant value.
-  mlir::ArrayAttr constantArrayAttribute;
+  // Create value attribute.
+  mlir::DenseElementsAttr constantDenseAttribute;
   mlir::Type elementType;
+  mlir::ShapedType tensorType;
   int length;
   switch (initializer.data_type()) {
     case (onnx::TensorProto::FLOAT): {
@@ -141,8 +147,9 @@ mlir::Value InitializedTensorMapping::EmitInitializerForInputTensor(
       std::vector<float> arrayAttrInitializer(
       	typeArray, typeArray + length);
       llvm::ArrayRef<float> array(typeArray, length);
-      constantArrayAttribute = builder.getF32ArrayAttr(array);
       elementType = builder.getF32Type();
+      tensorType = mlir::RankedTensorType::get(tensorDims, elementType);
+      constantDenseAttribute = mlir::DenseElementsAttr::get(tensorType, array);
       break;
     }
     case (onnx::TensorProto::INT32): {
@@ -151,8 +158,9 @@ mlir::Value InitializedTensorMapping::EmitInitializerForInputTensor(
       std::vector<int32_t> arrayAttrInitializer(
       	typeArray, typeArray + length);
       llvm::ArrayRef<int32_t> array(typeArray, length);
-      constantArrayAttribute = builder.getI32ArrayAttr(array);
       elementType = builder.getIntegerType(32);
+      tensorType = mlir::RankedTensorType::get(tensorDims, elementType);
+      constantDenseAttribute = mlir::DenseElementsAttr::get(tensorType, array);
       break;
     }
     case (onnx::TensorProto::INT64): {
@@ -161,25 +169,16 @@ mlir::Value InitializedTensorMapping::EmitInitializerForInputTensor(
       std::vector<int64_t> arrayAttrInitializer(
       	typeArray, typeArray + length);
       llvm::ArrayRef<int64_t> array(typeArray, length);
-      constantArrayAttribute = builder.getI64ArrayAttr(array);
       elementType = builder.getIntegerType(64);
+      tensorType = mlir::RankedTensorType::get(tensorDims, elementType);
+      constantDenseAttribute = mlir::DenseElementsAttr::get(tensorType, array);
       break;
     }
   }
 
-  // Create empty sparse_value attribute.
+  // Create ConstantOp for dense array.
-  llvm::ArrayRef<int64_t> array;
-  auto sparseValueAttribute = builder.getI64ArrayAttr(array);
-
-  // Create value attribute.
-  llvm::ArrayRef<int64_t> tensorDims(initializer.dims().data(),
-      initializer.dims().size());
-  mlir::Type tensorType =
-      mlir::RankedTensorType::get(tensorDims, elementType);
-
   return builder.create<mlir::ONNXConstantOp>(
-      loc, tensorType, sparseValueAttribute,
+      loc, tensorType, nullptr, constantDenseAttribute);
-      constantArrayAttribute);
 }
 
 } // namespace onnf

src/builder/frontend_dialect_transformer.cpp

@@ -274,8 +274,12 @@ private:
                       int expectedNumResults = -1) {
     std::vector<mlir::Value> inputs;
     for (const auto &item : node.input())
-      if (frontend_symbols_.ContainKey(legalize_name(item)))
+      if (initializedTensors.ContainKey(legalize_name(item))) {
+        inputs.push_back(initializedTensors.EmitInitializerForInputTensor(
+                              UnknownLoc(), builder_, legalize_name(item)));
+      } else if (frontend_symbols_.ContainKey(legalize_name(item))) {
         inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
+      }
 
     buildOutputAndOperation<T>(node, inputs, expectedNumOperands,
         expectedNumResults);
@@ -287,7 +291,7 @@ private:
     for (int i = 0; i < node.input().size(); ++i) {
       item = node.input()[i];
       // For the second argument, check if there exists an initializer.
-      if (i == 1 && initializedTensors.ContainKey(legalize_name(item))) {
+      if (initializedTensors.ContainKey(legalize_name(item))) {
           inputs.push_back(
                 initializedTensors.EmitInitializerForInputTensor(
                     UnknownLoc(), builder_, legalize_name(item)));
@@ -412,8 +416,9 @@ private:
     //  * maintain a list of the defined graph
     llvm::SmallVector<mlir::Type, 4> arg_types;
 
-    // Import the input tensor types that are not constant.
+    // Import the input tensor types that are not constant and not initialized.
     for (const auto &input : graph.input())
+      if (!initializedTensors.ContainKey(legalize_name(input.name())))
         arg_types.emplace_back(ImportInputTensorType(input));
 
     // Create the main function.
@@ -438,8 +443,9 @@ private:
 
     // Map graph inputs to entry block arguments.
     for (int i = 0; i < graph.input().size(); ++i)
-      ImportInputTensorSymbol(
+      if (!initializedTensors.ContainKey(
-          graph.input()[i], entryBlock.getArguments()[i]);
+              legalize_name(graph.input()[i].name())))
+        ImportInputTensorSymbol(graph.input()[i], entryBlock.getArguments()[i]);
 
     // Create a NoneTyped constant to be used for optional operation inputs
     // which are not used.

src/dialect/onnx/onnx_ops.cpp

@@ -876,12 +876,18 @@ void ONNXReshapeOp::inferShapes() {
 
   SmallVector<int64_t, 2> dims(outputRank, -1);
   if (constantOp) {
-    // Cast attribute to ArrayAttr.
+    DenseElementsAttr valueAttribute =
-    ArrayAttr valueAttribute = constantOp.valueAttr().dyn_cast<ArrayAttr>();
+        constantOp.valueAttr().dyn_cast<DenseElementsAttr>();
-    if (!valueAttribute)
-      emitError("ArrayAttr expected");
 
-    if (ArrayAttrSize(valueAttribute) != outputRank)
+    if (!valueAttribute)
+      emitError("DenseElementsAttr expected");
+
+    // Get dims from valueAttribute.
+    auto valueIt = valueAttribute.getValues<IntegerAttr>().begin();
+    for (int i=0; i<outputRank; ++i)
+      dims[i] = (*valueIt++).cast<IntegerAttr>().getInt();
+
+    if (valueIt != valueAttribute.getValues<IntegerAttr>().end())
       emitError("Constant value must have same rank as output");
 
     int64_t numberOfDynamicInputs = 0;
@@ -889,7 +895,6 @@ void ONNXReshapeOp::inferShapes() {
     int64_t dynamicValueIndex = -1;
     for (int i=0; i<outputRank; ++i) {
       // Set output dimension.
-      dims[i] = ArrayAttrIntVal(valueAttribute, i);
       if (dims[i] == 0)
         dims[i] = inputTensorTy.getShape()[i];
 

test/mlir/onnx/onnx_shape_inference.mlir

@@ -435,7 +435,7 @@ func @test_reshape_dynamic(%arg0 : tensor<5x5x1x32xf32>, %arg1 : tensor<4xi32>)
 }
 
 func @test_reshape_1(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
-  %0 = "onnx.Constant"() {sparse_value = [], value = [5, 5, 16, 2] } : () -> tensor<4xi32>
+  %0 = "onnx.Constant"() {value = dense<[5, 5, 16, 2]> : tensor<4xi32> } : () -> tensor<4xi32>
   %1 = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<4xi32>) -> tensor<*xf32>
   "std.return"(%1) : (tensor<*xf32>) -> ()
 
@@ -445,7 +445,7 @@ func @test_reshape_1(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
 }
 
 func @test_reshape_2(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
-  %0 = "onnx.Constant"() {sparse_value = [], value = [-1, 16, 2] } : () -> tensor<3xi32>
+  %0 = "onnx.Constant"() {value = dense<[-1, 16, 2]> : tensor<3xi32> } : () -> tensor<3xi32>
   %1 = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<3xi32>) -> tensor<*xf32>
   "std.return"(%1) : (tensor<*xf32>) -> ()
 
@@ -455,7 +455,7 @@ func @test_reshape_2(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
 }
 
 func @test_reshape_3(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
-  %0 = "onnx.Constant"() {sparse_value = [], value = [-1, 0, 2] } : () -> tensor<3xi32>
+  %0 = "onnx.Constant"() {value = dense<[-1, 0, 2]> : tensor<3xi32> } : () -> tensor<3xi32>
   %1 = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<3xi32>) -> tensor<*xf32>
   "std.return"(%1) : (tensor<*xf32>) -> ()