| |
| // Copyright (c) 2020-2021, 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 "scatter_gather.h" |
| #include "quant_util.h" |
| |
| using namespace TosaReference; |
| using namespace Eigen; |
| using namespace tosa; |
| |
| template <DType Dtype> |
| OpGather<Dtype>::OpGather(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| TosaQuantInfoBase* qinfo_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_GATHER, id_) |
| { |
| setRequiredOperands(2, 1); |
| } |
| |
| template <DType Dtype> |
| OpGather<Dtype>::~OpGather() |
| {} |
| |
| template <DType Dtype> |
| int OpGather<Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (inputs[0]->getRank() != 3) |
| { |
| printNodeValidationError("OpGather: values needs to be rank 3 tensor"); |
| return 1; |
| } |
| |
| if (inputs[1]->getRank() != 2) |
| { |
| printNodeValidationError("OpGather: indices needs to be rank 2 tensor"); |
| return 1; |
| } |
| |
| if (outputs[0]->getRank() != 3) |
| { |
| printNodeValidationError("OpGather: output needs to be rank 3 tensor"); |
| return 1; |
| } |
| |
| K = inputs[0]->getShape()[1]; |
| N = outputs[0]->getShape()[0]; |
| W = outputs[0]->getShape()[1]; |
| C = outputs[0]->getShape()[2]; |
| |
| if (N != inputs[0]->getShape()[0] || N != inputs[1]->getShape()[0]) |
| { |
| printNodeValidationError("OpGather: dimension N mismatch"); |
| return 1; |
| } |
| |
| if (W != inputs[1]->getShape()[1]) |
| { |
| printNodeValidationError("OpGather: dimension W mismatch"); |
| return 1; |
| } |
| |
| if (C != inputs[0]->getShape()[2]) |
| { |
| printNodeValidationError("OpGather: dimension C mismatch"); |
| return 1; |
| } |
| |
| // output and input must be the same types |
| if (inputs[0]->matchType(*outputs[0])) |
| { |
| printNodeValidationError("Failure to match input and output type"); |
| return 1; |
| } |
| |
| values = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[0]); |
| indices = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOutput>*>(outputs[0]); |
| |
| ASSERT_MEM(values && indices && output); |
| |
| return 0; |
| } |
| |
| template <DType Dtype> |
| int OpGather<Dtype>::eval() |
| { |
| for (int32_t n = 0; n < N; n++) |
| { |
| for (int32_t w = 0; w < W; w++) |
| { |
| int32_t k = this->indices->getTensor()(n, w); |
| REQUIRE(k >= 0 && k < K, "OpGather: index(%d, %d)=%d exceed valid range [0, %d]", n, w, k, K); |
| for (int32_t c = 0; c < C; c++) |
| { |
| EigenType value = this->values->getTensor()(n, k, c); |
| this->output->getTensor()(n, w, c) = value; |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| OpScatter<Dtype>::OpScatter(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| TosaQuantInfoBase* qinfo_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_SCATTER, id_) |
| { |
| setRequiredOperands(3, 1); |
| } |
| |
| template <DType Dtype> |
| OpScatter<Dtype>::~OpScatter() |
| {} |
| |
| template <DType Dtype> |
| int OpScatter<Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (inputs[0]->getRank() != 3) |
| { |
| printNodeValidationError("OpGather: values_in needs to be rank 3 tensor"); |
| return 1; |
| } |
| |
| if (inputs[1]->getRank() != 2) |
| { |
| printNodeValidationError("OpGather: indices needs to be rank 2 tensor"); |
| return 1; |
| } |
| |
| if (inputs[2]->getRank() != 3) |
| { |
| printNodeValidationError("OpGather: input needs to be rank 3 tensor"); |
| return 1; |
| } |
| |
| if (outputs[0]->getRank() != 3) |
| { |
| printNodeValidationError("OpGather: values_out needs to be rank 3 tensor"); |
| return 1; |
| } |
| |
| W = inputs[2]->getShape()[1]; |
| N = outputs[0]->getShape()[0]; |
| K = outputs[0]->getShape()[1]; |
| C = outputs[0]->getShape()[2]; |
| |
| if (N != inputs[0]->getShape()[0] || N != inputs[1]->getShape()[0] || N != inputs[2]->getShape()[0]) |
| { |
| printNodeValidationError("OpScatter: dimension N mismatch"); |
| return 1; |
| } |
| |
| if (W != inputs[1]->getShape()[1]) |
| { |
| printNodeValidationError("OpGather: dimension W mismatch"); |
| return 1; |
| } |
| |
| if (C != inputs[0]->getShape()[2] || C != inputs[2]->getShape()[2]) |
| { |
| printNodeValidationError("OpGather: dimension C mismatch"); |
| return 1; |
| } |
| |
| // output and input must be the same types |
| if (inputs[0]->matchType(*outputs[0])) |
| { |
| printNodeValidationError("Failure to match input and output type"); |
| return 1; |
| } |
| |
| values_in = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[0]); |
| indices = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]); |
| input = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[2]); |
| values_out = dynamic_cast<TosaReference::TensorTemplate<TOutput>*>(outputs[0]); |
| |
| ASSERT_MEM(values_in && indices && input && values_out); |
| |
| return 0; |
| } |
| |
| template <DType Dtype> |
| int OpScatter<Dtype>::eval() |
| { |
| // Initializes the output tensor with the input value for values that are unchanged by the scatter operation. |
| this->values_out->getTensor() = this->values_in->getTensor(); |
| |
| for (int n = 0; n < N; n++) |
| { |
| for (int w = 0; w < W; w++) |
| { |
| int32_t k = this->indices->getTensor()(n, w); |
| REQUIRE(k >= 0 && k < K, "OpScatter: index(%d, %d)=%d exceed valid range [0, %d]", n, w, k, K); |
| for (int c = 0; c < C; c++) |
| { |
| EigenType value = this->input->getTensor()(n, w, c); |
| this->values_out->getTensor()(n, k, c) = value; |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| // template explicit instantiation |
| DEF_INSTANTIATE_ONE_TYPE(OpGather, INT8); |
| DEF_INSTANTIATE_ONE_TYPE(OpGather, INT16); |
| DEF_INSTANTIATE_ONE_TYPE(OpGather, INT32); |
| DEF_INSTANTIATE_ONE_TYPE(OpGather, FLOAT); |
| |
| DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT8); |
| DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT16); |
| DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT32); |
| DEF_INSTANTIATE_ONE_TYPE(OpScatter, FLOAT); |