| %% template file for generating OperationTypes.h. |
| %% see README.md. |
| /* |
| * Copyright (C) 2020 The Android Open Source Project |
| * |
| * 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. |
| */ |
| |
| #ifndef ANDROID_PACKAGES_MODULES_NEURALNETWORKS_COMMON_TYPES_NNAPI_OPERATION_TYPES_H |
| #define ANDROID_PACKAGES_MODULES_NEURALNETWORKS_COMMON_TYPES_NNAPI_OPERATION_TYPES_H |
| |
| namespace android::nn { |
| |
| %insert Operation_1.0_Comment |
| enum class OperationType { |
| %insert Operation_1.0 |
| |
| %insert Operation_1.1 |
| |
| %insert Operation_1.2 |
| |
| %insert Operation_1.3 |
| |
| %insert Operation_fl6 |
| |
| %insert Operation_fl7 |
| |
| /** |
| * DEPRECATED. Since HAL version 1.2, extensions are the preferred |
| * alternative to OEM operation and data types. |
| * |
| * This operation is OEM specific. It should only be used for OEM |
| * applications. |
| */ |
| OEM_OPERATION = 10000, |
| |
| #ifdef NN_EXPERIMENTAL_FEATURE |
| /** |
| * Expands a representation of a sparse tensor to a dense tensor. |
| * |
| * To encode a conceptual n-dimensional dense tensor with dims [D0, ..., Dn-1], potentially with |
| * a k-dimensional block (0 <= k <= n) with dims [Dn, ..., Dn+k-1], the format specifies: |
| * * 1: In what order to traverse these dimensions. For example, to store a 2-D matrix in row |
| * major order, the traversal order would be [D0, D1], whereas to store it in column major |
| * order, the traversal order would be [D1, D0]. If the 2-D matrix has a 2-D inner block, |
| * the traversal order could be [D0, D1, D2, D3]. |
| * * 2: How each block dimension in [Dn, ..., Dn+k-1] maps to the original tensor dimension in |
| * [D0, ..., Dn-1]. |
| * * 3: In the traversal order defined above, the format (dense vs. sparse) and index metadata |
| * for each dimension. For a dense dimension, this is just the size of that dimension. For |
| * a sparse dimension, it's the same as the compressed index defined in the Compressed |
| * Sparse Row (CSR) format. |
| * (http://scipy-lectures.org/advanced/scipy_sparse/csr_matrix.html) |
| * |
| * The number of inputs to this operation is determined by the number of dimensions (including |
| * the block dimensions) of the sparsity parameters. Currently, the only formats supported are |
| * DENSE and SPARSE_CSR, but additional sparsity formats may be added in later versions of this |
| * operation. |
| * |
| * Supported tensor {@link OperandType}: |
| * * {@link OperandType::TENSOR_FLOAT16} |
| * * {@link OperandType::TENSOR_FLOAT32} |
| * * {@link OperandType::TENSOR_QUANT8_SYMM} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM} |
| * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} |
| * * {@link OperandType::TENSOR_BOOL8} |
| * * {@link OperandType::TENSOR_INT32} |
| * * {@link OperandType::TENSOR_QUANT16_SYMM} |
| * * {@link OperandType::TENSOR_QUANT16_ASYMM} |
| * |
| * |
| * Reference: |
| * * This implementation is a modification of the TACO format. |
| * http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf |
| * |
| * Inputs: |
| * * 0: A 1-D tensor representing the compressed sparse tensor data of a conceptual |
| * n-dimensional tensor. |
| * * 1: A 1-D {@link OperandType::TENSOR_INT32} tensor defining the traversal order for reading |
| * the non-zero blocks. For an n-dimensional tensor with dimensions [D0, D1, …, Dn-1]: if |
| * block sparse with a k-dimensional block (0 < k <= n), the traversal order has n+k |
| * elements. The first n elements are still a permutation of [D0, …, Dn-1]. The last k |
| * elements are a permutation of [Dn, …, Dn+k-1], defining how to traverse a block |
| * internally. If not block sparse, the traversal order is just a permutation of [D0, …, |
| * Dn-1]. |
| * * 2: An optional 1-D {@link OperandType::TENSOR_INT32} tensor defining the block map. For a |
| * block sparse n-dimensional tensor with a k-dimensional block (0 < k <= n), it stores how |
| * a block dimension [Dn, …, Dn+k-1] maps to the original tensor dimension in [D0, …, |
| * Dn-1]. For i, j where 0 <= i < j < k, blockMap[i] < blockMap[j]. If not block sparse, |
| * this is null. |
| * * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor with n+k elements defining the format of |
| * each dimension in the traversal order (listed above). The format is either DENSE (where |
| * DENSE = 0) or SPARSE_CSR (where SPARSE_CSR = 1). DENSE means that each coordinate in |
| * this dimension is stored implicitly. SPARSE_CSR means only the coordinates with non-zero |
| * elements are stored. |
| * * 4: A 1-D {@link OperandType::TENSOR_INT32} tensor with n+k elements defining the size of |
| * each dimension or block. The product of all these sizes totals the number of elements in |
| * the dense tensor. First n elements represent the sparse tensor’s shape, and the last k |
| * elements represent the block’s shape. |
| * * 5 ~ (5 + 2 * (n+k)): An optional pair of {@link OperandType::TENSOR_INT32} tensors which |
| * together specify the sparse indices along that dimension. The first pair of arguments |
| * corresponds to D0, the second to D1, and so on until Dn+k-1. If the dimension is DENSE, |
| * both arguments in the pair are null and the dimension is implicitly specified by the |
| * corresponding element in Input 4. If the dimension is SPARSE_CSR, then we use the pair |
| * of array segments and array indices to encode that dimension: |
| * * * +0: An optional list of n+k input 1-D {@link OperandType::TENSOR_INT32} tensors, defining |
| * the array segments. The array segments represent how to segment the indices array, |
| * each segment corresponds to one element in the previous dimension. Array segments are |
| * interspersed with array indices (listed below), so this input could be input (5, 5 + |
| * 2, …, 5 + 2*(n+k-1)). For i, j where 0 =< i < j, arraySegments[i] <= |
| * arraySegments[j]. Used if the dimension is SPARSE_CSR, omitted if the dimension is |
| * DENSE. |
| * * * +1: An optional list of n+k input 1-D {@link OperandType::TENSOR_INT32} tensors, defining |
| * the array indices. The array indices represent the index of the non-zero elements |
| * within this dimension (as those in the CSR matrix format, where the first array is |
| * row pointers and the second array is column indices). Array indices are interspersed |
| * with array segments (listed above), so this input could be input (6, 6 + 2, …, 6 + |
| * 2*(n+k-1)). Used if the dimension is SPARSE_CSR, omitted if the dimension is DENSE. |
| * |
| * Outputs: |
| * * 0: An n-D dense tensor. The output tensor has the same {@link OperandType} as input 0. |
| */ |
| DENSIFY = 20000, |
| #endif // NN_EXPERIMENTAL_FEATURE |
| }; |
| |
| } // namespace android::nn |
| |
| #endif // ANDROID_PACKAGES_MODULES_NEURALNETWORKS_COMMON_TYPES_NNAPI_OPERATION_TYPES_H |
