| |
| // Copyright (c) 2020-2022, ARM Limited. |
| // |
| // 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. |
| |
| #include "reduction.h" |
| #include "quant_util.h" |
| |
| using namespace TosaReference; |
| using namespace Eigen; |
| using namespace tosa; |
| |
| template <int Rank, DType Dtype> |
| ReduceNode<Rank, Dtype>::ReduceNode(SubgraphTraverser* sgt_, const Op& op_, TosaAttributeBase* attribute_, uint64_t id_) |
| : GraphNode(sgt_, op_, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(0, 4); |
| |
| INIT_ATTRIBUTE(Axis); |
| } |
| |
| template <int Rank, DType Dtype> |
| ReduceNode<Rank, Dtype>::~ReduceNode() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <int Rank, DType Dtype> |
| int ReduceNode<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| if (attribute->axis() < 0 || attribute->axis() >= inputs[0]->getRank()) |
| { |
| printNodeValidationError("ReduceOp: axis must between [0, input_rank - 1]"); |
| return 1; |
| } |
| |
| if (inputs[0]->matchRankType(*outputs[0])) |
| { |
| printNodeValidationError("ReduceOp: Input and output tensor ranks must match"); |
| return 1; |
| } |
| |
| if (outputs[0]->getShape()[attribute->axis()] != 1) |
| { |
| printNodeValidationError("ReduceOp: Output tensor shape[axis] needs to be 1."); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| if ((!in) || (!out)) |
| { |
| printNodeValidationError("ReduceOp: Input or output fail to cast to Eigen tensor since rank/type not expected"); |
| return 1; |
| } |
| |
| dims[0] = this->attribute->axis(); |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReduceAll<Rank, Dtype>::eval() |
| { |
| this->out->getTensor() = this->in->getTensor().all(this->dims).reshape(this->out->getTensor().dimensions()); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReduceAny<Rank, Dtype>::eval() |
| { |
| this->out->getTensor() = this->in->getTensor().any(this->dims).reshape(this->out->getTensor().dimensions()); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReduceMax<Rank, Dtype>::eval() |
| { |
| this->out->getTensor() = this->in->getTensor().maximum(this->dims).reshape(this->out->getTensor().dimensions()); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReduceMin<Rank, Dtype>::eval() |
| { |
| this->out->getTensor() = this->in->getTensor().minimum(this->dims).reshape(this->out->getTensor().dimensions()); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReduceProduct<Rank, Dtype>::eval() |
| { |
| this->out->getTensor() = this->in->getTensor().prod(this->dims).reshape(this->out->getTensor().dimensions()); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpReduceSum<Rank, Dtype>::eval() |
| { |
| this->out->getTensor() = this->in->getTensor().sum(this->dims).reshape(this->out->getTensor().dimensions()); |
| |
| return GraphNode::eval(); |
| } |
| |
| struct SumRequiresReducer { |
| static const bool PacketAccess = false; |
| SumRequiresReducer(SubgraphTraverser* parent_sgt) : parent_sgt(parent_sgt) {} |
| void reduce(const int32_t val, int32_t* accum) { |
| int64_t res_in_64 = static_cast<int64_t>(*accum) + val; |
| int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max()); |
| int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min()); |
| REQUIRE(res_in_64 <= i32_max_in_64 && res_in_64 >= i32_min_in_64, "OpReduceSum: result not in i32 range"); |
| *accum = static_cast<int32_t>(res_in_64); |
| } |
| int32_t initialize() const { return 0; } |
| int32_t finalize(const int32_t accum) const { return accum; } |
| |
| private: |
| SubgraphTraverser* parent_sgt; |
| }; |
| |
| template <int Rank, DType Dtype> |
| int OpReduceSumInt<Rank, Dtype>::eval() |
| { |
| this->out->getTensor() = this->in->getTensor().reduce(this->dims, SumRequiresReducer(this->parent_sgt)).reshape(this->out->getTensor().dimensions()); |
| |
| return GraphNode::eval(); |
| } |
| |
| // template explicit instantiation |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceAll, BOOL); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceAny, BOOL); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, INT32); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, INT16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, INT32); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceProduct, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceProduct, FP32); |
| |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceSum, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceSum, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceSumInt, INT32); |