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Diffstat (limited to 'compiler/locomotiv/src/Node/MatrixEncode.cpp')
-rw-r--r-- | compiler/locomotiv/src/Node/MatrixEncode.cpp | 112 |
1 files changed, 112 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/MatrixEncode.cpp b/compiler/locomotiv/src/Node/MatrixEncode.cpp new file mode 100644 index 000000000..e3554e15a --- /dev/null +++ b/compiler/locomotiv/src/Node/MatrixEncode.cpp @@ -0,0 +1,112 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. 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. + */ + +#include "NodeExecution.h" + +#include "NodeDataImpl.h" +#include "NodeDomain.h" +#include "Validation.h" + +#include <nncc/core/ADT/tensor/LexicalLayout.h> +#include <nncc/core/ADT/tensor/IndexEnumerator.h> + +#include <stdexcept> +#include <cassert> + +namespace +{ + +using nncc::core::ADT::tensor::Buffer; +using nncc::core::ADT::tensor::make_buffer; +using nncc::core::ADT::tensor::LexicalLayout; +using nncc::core::ADT::tensor::Shape; +using nncc::core::ADT::tensor::IndexEnumerator; + +template <typename T> +std::unique_ptr<locomotiv::NodeData> matrix_encode(const loco::MatrixEncode *node, + const Buffer<T> *input_buf) +{ + auto encoder = node->encoder(); + + // Make TensorShape from input + loco::TensorShape input_shape; + input_shape.rank(input_buf->shape().rank()); + assert(input_shape.rank() == 2); + for (uint32_t i = 0; i < input_shape.rank(); ++i) + { + input_shape.dim(i) = input_buf->shape().dim(i); + } + + loco::MatrixShape node_shape = encoder->shape(input_shape); + + // Make HW buffer from MatrixShape + Buffer<T> node_buf = + make_buffer<T, LexicalLayout>(Shape{node_shape.height().value(), node_shape.width().value()}); + + // Copy buffer in an order arranged by encoder + for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance()) + { + loco::MatrixIndex index; + index.row() = e.current().at(0); + index.column() = e.current().at(1); + + node_buf.at(e.current()) = input_buf->at(encoder->value(index)); + } + + return locomotiv::make_data(node_buf); +} + +} // namespace + +namespace locomotiv +{ + +void NodeExecution::execute(loco::MatrixEncode *matrix_enc) +{ + auto input_data = annot_data(matrix_enc->input()); + + validate(input_data, "Input not ready"); + validate(annot_domain(matrix_enc->input()) == loco::Domain::Tensor, + "Input domain should be Tensor"); + validate(input_data->shape()->rank() == 2, "Input data rank must be 2"); + + std::unique_ptr<NodeData> matrix_enc_data = nullptr; + + switch (input_data->dtype()) + { + case loco::DataType::S32: + { + auto input_buf = input_data->as_s32_bufptr(); + matrix_enc_data = matrix_encode<int32_t>(matrix_enc, input_buf); + break; + } + case loco::DataType::FLOAT32: + { + auto input_buf = input_data->as_f32_bufptr(); + matrix_enc_data = matrix_encode<float>(matrix_enc, input_buf); + break; + } + default: + throw std::runtime_error("NYI for this DataType"); + } + + assert(matrix_enc_data != nullptr); + + annot_data(matrix_enc, std::move(matrix_enc_data)); + annot_domain(matrix_enc, loco::Domain::Matrix); +} + +} // namespace locomotiv |