Cast integer pad value to boolean in boolean pad operation

In the type conversion, zero integer is equal to `false`, and any
non-zero integner evaluates to `true`.

Change-Id: I78f395f8cfa17f77d72c4f18a65d9848c5811ddc
Signed-off-by: TatWai Chong <tatwai.chong@arm.com>

1 files changed, 53 insertions, 28 deletions

diff --git a/src/TosaDeserialize.cpp b/src/TosaDeserialize.cpp
index 301d0da..5ad7e6e 100644
--- a/src/TosaDeserialize.cpp
+++ b/src/TosaDeserialize.cpp
@@ -1056,39 +1056,64 @@ TosaMlirOperatorBuilder::build<Op_PAD>(TosaSerializationOperator *op) const {
   // todo: int input_zp = attr->pad_input_zp();
 
   mlir::Operation *mlir_op;
+  mlir::Value pad_const_value;
+  const auto element_type =
+      input_val.getType().cast<mlir::ShapedType>().getElementType();
+
+  bool isBoolType = element_type.isInteger(1);
+  // First handle boolean type.
+  if (isBoolType) {
+    mlir::Type boolType = op_builder->getIntegerType(1);
+    auto pad_const_type = mlir::RankedTensorType::get({}, boolType);
+    // Treat zero integer is `false`, and any non-zero integner evaluates to
+    // `true`.
+    bool pad_const = pad_const_int == 0 ? false : true;
+    auto pad_const_attr =
+        mlir::DenseElementsAttr::get(pad_const_type, {pad_const});
+    mlir::Operation *pad_const_op = op_builder->create<mlir::tosa::ConstOp>(
+        loc, pad_const_type, pad_const_attr);
+
+    block->push_back(pad_const_op);
+    pad_const_value = pad_const_op->getResult(0);
+    mlir_op = op_builder->create<mlir::tosa::PadOp>(
+        loc, output_type, input_val, padding_val, pad_const_value);
+
+    block->push_back(mlir_op);
+    return std::vector<mlir::Value>({mlir_op->getResult(0)});
+  }
+
+  // Second handle the cases where no explicit pad_const input.
   if (pad_const_int == 0 && pad_const_fp == 0.0f) {
-    // no pad_const input
     mlir_op = op_builder->create<mlir::tosa::PadOp>(loc, output_type, input_val,
                                                     padding_val);
+    block->push_back(mlir_op);
+    return std::vector<mlir::Value>({mlir_op->getResult(0)});
+  }
+
+  // Then handle explicit numerical pad_const cases.
+  if (pad_const_int != 0) {
+    assert(pad_const_fp == 0.0f && llvm::isa<IntegerType>(element_type));
+    auto pad_const_int_type = mlir::RankedTensorType::get({}, element_type);
+    auto pad_const_int_attr =
+        mlir::DenseElementsAttr::get(pad_const_int_type, {pad_const_int});
+    mlir::Operation *pad_const_int_op = op_builder->create<mlir::tosa::ConstOp>(
+        loc, pad_const_int_type, pad_const_int_attr);
+    block->push_back(pad_const_int_op);
+    pad_const_value = pad_const_int_op->getResult(0);
   } else {
-    // create a const value for pad_const input
-    const auto input_element_type =
-        input_val.getType().cast<mlir::ShapedType>().getElementType();
-    mlir::Value pad_const_value;
-    if (pad_const_int != 0) {
-      auto pad_const_int_type =
-          mlir::RankedTensorType::get({}, input_element_type);
-      auto pad_const_int_attr =
-          mlir::DenseElementsAttr::get(pad_const_int_type, {pad_const_int});
-      mlir::Operation *pad_const_int_op =
-          op_builder->create<mlir::tosa::ConstOp>(loc, pad_const_int_type,
-                                                  pad_const_int_attr);
-      block->push_back(pad_const_int_op);
-      pad_const_value = pad_const_int_op->getResult(0);
-    } else if (pad_const_fp != 0) {
-      auto pad_const_fp_type =
-          mlir::RankedTensorType::get({}, input_element_type);
-      auto pad_const_fp_attr =
-          mlir::DenseElementsAttr::get(pad_const_fp_type, {pad_const_fp});
-      mlir::Operation *pad_const_fp_op =
-          op_builder->create<mlir::tosa::ConstOp>(loc, pad_const_fp_type,
-                                                  pad_const_fp_attr);
-      block->push_back(pad_const_fp_op);
-      pad_const_value = pad_const_fp_op->getResult(0);
-    }
-    mlir_op = op_builder->create<mlir::tosa::PadOp>(
-        loc, output_type, input_val, padding_val, pad_const_value);
+    assert(pad_const_fp != 0 && llvm::isa<FloatType>(element_type));
+    auto pad_const_fp_type = mlir::RankedTensorType::get({}, element_type);
+    auto pad_const_fp_attr =
+        mlir::DenseElementsAttr::get(pad_const_fp_type, {pad_const_fp});
+    mlir::Operation *pad_const_fp_op = op_builder->create<mlir::tosa::ConstOp>(
+        loc, pad_const_fp_type, pad_const_fp_attr);
+    block->push_back(pad_const_fp_op);
+    pad_const_value = pad_const_fp_op->getResult(0);
   }
+
+  mlir_op = op_builder->create<mlir::tosa::PadOp>(loc, output_type, input_val,
+                                                  padding_val, pad_const_value);
+
   block->push_back(mlir_op);
   return std::vector<mlir::Value>({mlir_op->getResult(0)});
 }