merge main branch; format code

Author: xysmlx

artifacts/README.md

@@ -1,5 +1,8 @@
 # OSDI'20 Artifacts Evaluation
 
-OSDI'20 Artifact Evaluation of paper #292, titled "[Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks](https://www.usenix.org/conference/osdi20/presentation/ma)". 
+- OSDI'20 Artifact Evaluation of paper #292, titled "[Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks](https://www.usenix.org/conference/osdi20/presentation/ma)". 
+Please refer to the [osdi20_artifact branch](https://github.com/microsoft/nnfusion/tree/osdi20_artifact/artifacts)**
 
-**Please refer to the [osdi20_artifact branch](https://github.com/microsoft/nnfusion/tree/osdi20_artifact/artifacts)**
+
+- OSDI'22 Artifact Evaluation of paper #158, titled "[Roller: Fast and Efficient Tensor Compilation for Deep Learning](https://www.usenix.org/conference/osdi22/presentation/zhu)". 
+Please refer to the [osdi22_artifact branch](https://github.com/microsoft/nnfusion/tree/osdi22_artifact/artifacts)**

src/nnfusion/frontend/onnx_import/util/util.hpp

@@ -149,47 +149,6 @@ namespace nnfusion
                     {
                         return __get_raw_data<half_float::half>(tensor.raw_data());
                     }
-                    else
-                    {
-                        NNFUSION_LOG(NNFUSION_WARNING) << "Have no raw data" << endl;
-                    }
-
-                    if (tensor.data_type() == onnx::TensorProto_DataType_FLOAT16)
-                    {
-                        nnfusion::Shape shape{std::begin(tensor.dims()), std::end(tensor.dims())};
-                        size_t num_element = shape_size(shape);
-                        std::vector<int32_t> raw_data = __get_data<int32_t>(tensor.int32_data());
-                        std::vector<half_float::half> ret((num_element + 1) / 2);
-                        uint32_t* src_p = (uint32_t*)raw_data.data();
-                        uint16_t* dst_p = (uint16_t*)ret.data();
-                        for (size_t i = 0; i < num_element; i++)
-                        {
-                            NNFUSION_CHECK((src_p[i] & 0xFFFF0000) == 0);
-                            dst_p[i] = src_p[i] & 0x0000FFFF;
-                        }
-                        if (num_element % 2 == 1)
-                        {
-                            dst_p[num_element] = 0;
-                        }
-
-                        return ret;
-                    }
-                    if (tensor.data_type() == onnx::TensorProto_DataType_FLOAT)
-                    {
-                        return __get_data<half_float::half>(tensor.float_data());
-                    }
-                    if (tensor.data_type() == onnx::TensorProto_DataType_INT32)
-                    {
-                        return __get_data<half_float::half>(tensor.int32_data());
-                    }
-                    if (tensor.data_type() == onnx::TensorProto_DataType_INT64)
-                    {
-                        return __get_data<half_float::half>(tensor.int64_data());
-                    }
-                    if (tensor.data_type() == onnx::TensorProto_DataType_UINT64)
-                    {
-                        return __get_data<half_float::half>(tensor.uint64_data());
-                    }
                     NNFUSION_CHECK_FAIL()
                         << "invalid data type: "
                         << onnx::TensorProto_DataType_Name(