diff options
Diffstat (limited to 'src/TosaDeserialize.cpp')
| -rw-r--r-- | src/TosaDeserialize.cpp | 14 |
1 files changed, 3 insertions, 11 deletions
diff --git a/src/TosaDeserialize.cpp b/src/TosaDeserialize.cpp index 2421d79..5956bc8 100644 --- a/src/TosaDeserialize.cpp +++ b/src/TosaDeserialize.cpp @@ -1029,6 +1029,7 @@ template <> std::vector<mlir::Value> TosaMlirOperatorBuilder::build<Op_PAD>(TosaSerializationOperator *op) const { mlir::Value input_val = tensor_map->at(op->GetInputTensorNames()[0]); + mlir::Value padding_val = tensor_map->at(op->GetInputTensorNames()[1]); mlir::RankedTensorType input_type = tensor_type_map->at(op->GetInputTensorNames()[0]); mlir::RankedTensorType output_type = @@ -1038,15 +1039,6 @@ TosaMlirOperatorBuilder::build<Op_PAD>(TosaSerializationOperator *op) const { Attribute_PadAttribute); // double check attribute type TosaPadAttribute *attr = static_cast<TosaPadAttribute *>(op->GetAttribute()); - // padding has shape {rank(input_type), 2} - auto padding_type = mlir::RankedTensorType::get({input_type.getRank(), 2}, - op_builder->getI32Type()); - auto padding_attr = - BuildDenseI32ElementsAttr(op_builder, padding_type, attr->padding()); - mlir::Operation *mlir_const_op = - op_builder->create<mlir::tosa::ConstOp>(loc, padding_type, padding_attr); - block->push_back(mlir_const_op); - auto padding_value = mlir_const_op->getResult(0); auto pad_const_int = attr->pad_const_int(); auto pad_const_fp = attr->pad_const_fp(); // todo: int input_zp = attr->pad_input_zp(); @@ -1055,7 +1047,7 @@ TosaMlirOperatorBuilder::build<Op_PAD>(TosaSerializationOperator *op) const { 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_value); + padding_val); } else { // create a const value for pad_const input const auto input_element_type = @@ -1083,7 +1075,7 @@ TosaMlirOperatorBuilder::build<Op_PAD>(TosaSerializationOperator *op) const { pad_const_value = pad_const_fp_op->getResult(0); } mlir_op = op_builder->create<mlir::tosa::PadOp>( - loc, output_type, input_val, padding_value, pad_const_value); + loc, output_type, input_val, padding_val, pad_const_value); } block->push_back(mlir_op); return std::vector<mlir::Value>({mlir_op->getResult(0)}); |