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Commit 0cedad69 authored by David Gross's avatar David Gross Committed by Ian Hua
Browse files

Add PACK operation to NNAPI feature level 6. 

Cherrypicked from Ic15d047b70c62437b4f0db6f2ca10127591ae07c

Bug: 200281183
Bug: 200280665

Test: m -j NeuralNetworksTest_static
Test: VtsHalNeuralnetworksTargetTest

Change-Id: Ic15d047b70c62437b4f0db6f2ca10127591ae07c
Merged-In: Ic15d047b70c62437b4f0db6f2ca10127591ae07c
parent ef67442f

neuralnetworks/aidl/aidl_api/android.hardware.neuralnetworks/current/android/hardware/neuralnetworks/OperationType.aidl

+1 −0
Original line number Diff line number Diff line
@@ -137,4 +137,5 @@ enum OperationType { 
  FILL = 100,

  RANK = 101,

  BATCH_MATMUL = 102,

  PACK = 103,

}

neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl

+49 −0
Original line number Diff line number Diff line
@@ -5165,4 +5165,53 @@ enum OperationType { 
     *      c_o = r_y if adj_y else c_y

     */

    BATCH_MATMUL = 102,



    /**

     * Packs N input tensors (N >= 1) of rank R into one output tensor of rank R+1.

     * The tensors are packed along a given axis.

     *

     * The input tensors must have identical {@link OperandType} and dimensions.

     *

     * For example, suppose there are N input tensors of shape (A, B, C).

     * If axis is 0, the output tensor will have shape (N, A, B, C).

     * If axis is 1, the output tensor will have shape (A, N, B, C).

     *

     * All dimensions through the axis dimension determine the output tile count;

     * the remaining dimensions determine the tile shape.

     *

     * Return to the example of N input tensors of shape (A, B, C).

     * If axis is 0, there are N tiles in the output, each of shape (A, B, C).

     * If axis is 1, there are A*N tiles in the output, each of shape (B, C).

     *

     * The coordinates of a tile within the output tensor are (t[0],...,t[axis]).

     * The coordinates of a tile within an input tensor are (t[0],...,t[axis-1]).

     * (If axis is 0, an input tensor consists of a single tile.)

     * If we index input tensors starting with 0 (rather than by operand number),

     * then output_tile[t[0],...,t[axis]] = input_tile[t[axis]][t[0],...,t[axis-1]].

     * That is, all output tile coordinates except for the axis coordinate select

     * the corresponding location within some input tensor; and the axis coordinate

     * selects the input tensor.

     *

     * Supported tensor {@link OperandType}:

     * * {@link OperandType::TENSOR_FLOAT16}

     * * {@link OperandType::TENSOR_FLOAT32}

     * * {@link OperandType::TENSOR_QUANT8_ASYMM}

     * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}

     * * {@link OperandType::TENSOR_INT32}

     *

     * Supported input tensor rank: from 1

     *

     * Inputs:

     * * 0: A scalar of type {@link OperandType::INT32}, specifying

     *      the axis along which to pack.  The valid range is [0, R+1).

     * * 1 ~ N: Input tensors to be packed together.

     *          For {@link OperandType::TENSOR_QUANT8_ASYMM} and

     *          {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensors,

     *          the scales and zeroPoint must be the same for all input tensors,

     *          and will be the same for the output tensor.

     *

     * Outputs:

     * * 0: The packed tensor.

     */

    PACK = 103,

}