159 lines
8.0 KiB
C
159 lines
8.0 KiB
C
/*
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* Copyright (C) 2021 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/**
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* @addtogroup NeuralNetworks
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* @{
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*/
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/**
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* @file NeuralNetworksExperimentalFeatures.h
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*/
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#ifndef ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_NEURAL_NETWORKS_EXPERIMENTAL_FEATURES_H
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#define ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_NEURAL_NETWORKS_EXPERIMENTAL_FEATURES_H
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/******************************************************************
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*
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* IMPORTANT NOTICE:
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*
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* This file is part of Android's set of stable system headers
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* exposed by the Android NDK (Native Development Kit).
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*
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* Third-party source AND binary code relies on the definitions
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* here to be FROZEN ON ALL UPCOMING PLATFORM RELEASES.
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*
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* - DO NOT MODIFY ENUMS (EXCEPT IF YOU ADD NEW 32-BIT VALUES)
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* - DO NOT MODIFY CONSTANTS OR FUNCTIONAL MACROS
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* - DO NOT CHANGE THE SIGNATURE OF FUNCTIONS IN ANY WAY
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* - DO NOT CHANGE THE LAYOUT OR SIZE OF STRUCTURES
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*/
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#include <stdbool.h>
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#include <stddef.h>
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#include <stdint.h>
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#include <sys/cdefs.h>
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__BEGIN_DECLS
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/**
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* The Android NNAPI experimental feature level.
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*/
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typedef enum {
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ANEURALNETWORKS_FEATURE_LEVEL_EXPERIMENTAL = 0x7FFFFFFF,
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} ANeuralNetworksExperimentalFeatureLevelCode;
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/**
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* Operation types for experimental features.
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*
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* The type of an operation in a model.
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*/
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typedef enum {
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/**
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* Expands a representation of a sparse tensor to a dense tensor.
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*
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* To encode a conceptual n-dimensional dense tensor with dims [D0, ..., Dn-1], potentially with
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* a k-dimensional block (0 <= k <= n) with dims [Dn, ..., Dn+k-1], the format specifies:
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* * 1: In what order to traverse these dimensions. For example, to store a 2-D matrix in row
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* major order, the traversal order would be [D0, D1], whereas to store it in column major
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* order, the traversal order would be [D1, D0]. If the 2-D matrix has a 2-D inner block,
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* the traversal order could be [D0, D1, D2, D3].
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* * 2: How each block dimension in [Dn, ..., Dn+k-1] maps to the original tensor dimension in
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* [D0, ..., Dn-1].
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* * 3: In the traversal order defined above, the format (dense vs. sparse) and index metadata
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* for each dimension. For a dense dimension, this is just the size of that dimension. For
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* a sparse dimension, it's the same as the compressed index defined in the Compressed
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* Sparse Row (CSR) format.
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* (http://scipy-lectures.org/advanced/scipy_sparse/csr_matrix.html)
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*
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* The number of inputs to this operation is determined by the number of dimensions (including
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* the block dimensions) of the sparsity parameters. Currently, the only formats supported are
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* DENSE and SPARSE_CSR, but additional sparsity formats may be added in later versions of this
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* operation.
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*
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* Supported tensor {@link OperandCode}:
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* * {@link ANEURALNETWORKS_TENSOR_FLOAT16}
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* * {@link ANEURALNETWORKS_TENSOR_FLOAT32}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT8_SYMM}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED}
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* * {@link ANEURALNETWORKS_TENSOR_BOOL8}
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* * {@link ANEURALNETWORKS_TENSOR_INT32}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT16_SYMM}
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* * {@link ANEURALNETWORKS_TENSOR_QUANT16_ASYMM}
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*
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*
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* Reference:
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* * This implementation is a modification of the TACO format.
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* http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
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*
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* Inputs:
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* * 0: A 1-D tensor representing the compressed sparse tensor data of a conceptual
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* n-dimensional tensor.
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* * 1: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor defining the traversal order for
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* reading the non-zero blocks. For an n-dimensional tensor with dimensions [D0, D1, …,
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* Dn-1]: if block sparse with a k-dimensional block (0 < k <= n), the traversal order has
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* n+k elements. The first n elements are still a permutation of [D0, …, Dn-1]. The last k
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* elements are a permutation of [Dn, …, Dn+k-1], defining how to traverse a block
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* internally. If not block sparse, the traversal order is just a permutation of [D0, …,
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* Dn-1].
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* * 2: An optional 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor defining the block map. For
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* a block sparse n-dimensional tensor with a k-dimensional block (0 < k <= n), it stores
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* how a block dimension [Dn, …, Dn+k-1] maps to the original tensor dimension in [D0, …,
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* Dn-1]. For i, j where 0 <= i < j < k, blockMap[i] < blockMap[j]. If not block sparse,
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* this is null.
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* * 3: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with n+k elements defining the format
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* of each dimension in the traversal order (listed above). The format is either DENSE
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* (where DENSE = 0) or SPARSE_CSR (where SPARSE_CSR = 1). DENSE means that each coordinate
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* in this dimension is stored implicitly. SPARSE_CSR means only the coordinates with
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* non-zero elements are stored.
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* * 4: A 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensor with n+k elements defining the size of
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* each dimension or block. The product of all these sizes totals the number of elements in
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* the dense tensor. First n elements represent the sparse tensor’s shape, and the last k
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* elements represent the block’s shape.
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* * 5 ~ (5 + 2 * (n+k)): An optional pair of {@link ANEURALNETWORKS_TENSOR_INT32} tensors which
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* together specify the sparse indices along that dimension. The first pair of arguments
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* corresponds to D0, the second to D1, and so on until Dn+k-1. If the dimension is DENSE,
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* both arguments in the pair are null and the dimension is implicitly specified by the
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* corresponding element in Input 4. If the dimension is SPARSE_CSR, then we use the pair
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* of array segments and array indices to encode that dimension:
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* * * +0: An optional list of n+k input 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensors,
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* defining the array segments. The array segments represent how to segment the indices
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* array, each segment corresponds to one element in the previous dimension. Array
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* segments are interspersed with array indices (listed below), so this input could be
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* input (5, 5 + 2, …, 5 + 2*(n+k-1)). For i, j where 0 =< i < j, arraySegments[i] <=
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* arraySegments[j]. Used if the dimension is SPARSE_CSR, omitted if the dimension is
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* DENSE.
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* * * +1: An optional list of n+k input 1-D {@link ANEURALNETWORKS_TENSOR_INT32} tensors,
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* defining the array indices. The array indices represent the index of the non-zero
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* elements within this dimension (as those in the CSR matrix format, where the first
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* array is row pointers and the second array is column indices). Array indices are
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* interspersed with array segments (listed above), so this input could be input (6, 6 +
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* 2, …, 6 + 2*(n+k-1)). Used if the dimension is SPARSE_CSR, omitted if the dimension
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* is DENSE.
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*
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* Outputs:
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* * 0: An n-D dense tensor. The output tensor has the same {@link OperandCode} as input 0.
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*/
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ANEURALNETWORKS_DENSIFY = 20000,
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} ANeuralNetworksExperimentalOperationCode;
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__END_DECLS
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#endif // ANDROID_PACKAGES_MODULES_NEURALNETWORKS_RUNTIME_NEURAL_NETWORKS_EXPERIMENTAL_FEATURES_H
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/** @} */
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