// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include #include #include #include "glog/logging.h" #include "llvm/Support/ErrorHandling.h" #include "mlir/IR/BuiltinAttributes.h" #include "paddle/phi/common/data_type.h" #include "paddle/phi/core/dense_tensor.h" namespace infrt { namespace kernel { namespace tensorrt { static nvinfer1::DataType TensorTypeToWeightType(::phi::DataType tensor_type) { switch (tensor_type) { case ::phi::DataType::FLOAT32: return nvinfer1::DataType::kFLOAT; case ::phi::DataType::INT32: return nvinfer1::DataType::kINT32; case ::phi::DataType::FLOAT16: return nvinfer1::DataType::kHALF; default: llvm_unreachable("should not reach here"); } } static nvinfer1::Dims ArrayAttrToNvDims(const mlir::ArrayAttr& int_array_attr) { nvinfer1::Dims dims; dims.nbDims = int_array_attr.size(); CHECK(!int_array_attr.empty()); CHECK(int_array_attr[0].getType().isIntOrIndex()); for (int i = 0; i < dims.nbDims; ++i) { dims.d[i] = int_array_attr[i].cast().getInt(); } return dims; } static nvinfer1::Weights TensorToWeights(::phi::DenseTensor* tensor) { CHECK_NOTNULL(tensor); nvinfer1::Weights ret; ret.type = TensorTypeToWeightType(tensor->dtype()); ret.count = tensor->numel(); ret.values = tensor->data(); return ret; } } // namespace tensorrt } // namespace kernel } // namespace infrt