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Commit 077c0339 authored by Miao Wang's avatar Miao Wang
Browse files

Update the specification for the following operations 

 - RANDOM_MULTINOMIAL
 - L2_NORMALIZATION

Bug: 136279892
Bug: 140177375
Test: mm
Change-Id: Iab38ef29ebf6d1f5c0a408436b3d564e45e537a0
parent 3b491b48

current.txt

+3 −3
Original line number Original line Diff line number Diff line
@@ -594,11 +594,11 @@ bceee81ec1b59324abd05932b5620fda5a6589597c9cb3953ba7f3ea02cccd3e android.hardwar 
2ce820dc4f3c6d85721b65150ed2157c6e2e2055f866fb6c6ba4790f14408d66 android.hardware.camera.provider@2.4::ICameraProviderCallback

2ce820dc4f3c6d85721b65150ed2157c6e2e2055f866fb6c6ba4790f14408d66 android.hardware.camera.provider@2.4::ICameraProviderCallback

b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice

b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice

eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel

eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel

8eac60e1f724d141c71c69f06d4544acb720a55dfbbcd97fa01bb3d25ee4e2f5 android.hardware.neuralnetworks@1.0::types

92e101b30e47bdf526a01c52cecfbe730def5997b8260ab497eb949eb2a6dcdf android.hardware.neuralnetworks@1.0::types

5f6d3097ba84cb63c430787123f4de1b31c11f90b531b98eae9a8623a5ae962a android.hardware.neuralnetworks@1.1::types

5f6d3097ba84cb63c430787123f4de1b31c11f90b531b98eae9a8623a5ae962a android.hardware.neuralnetworks@1.1::types

fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice

fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice

40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel

40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel

00649d29680f2c47edf60000c3ae7ae906ba638f0616947147e3676a83cf36fa android.hardware.neuralnetworks@1.2::types

ee1a0dee5be00a6fe2d4d3270068c78016dcb194d768fe07ed894ea20904037f android.hardware.neuralnetworks@1.2::types

a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types

a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types

1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback

1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback

fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface

fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
@@ -676,7 +676,7 @@ a3eddd9bbdc87e8c22764070037dd1154f1cf006e6fba93364c4f85d4c134a19 android.hardwar 
6e904be0ddca5ae1de8eba020e6c38ed935ea7d80cd08f47787f137a0ca58555 android.hardware.neuralnetworks@1.3::IFencedExecutionCallback

6e904be0ddca5ae1de8eba020e6c38ed935ea7d80cd08f47787f137a0ca58555 android.hardware.neuralnetworks@1.3::IFencedExecutionCallback

2b0b10d2ea7a18a4048cd0eb83d35c19a817aeee95f65807fc31f4ef21381397 android.hardware.neuralnetworks@1.3::IPreparedModel

2b0b10d2ea7a18a4048cd0eb83d35c19a817aeee95f65807fc31f4ef21381397 android.hardware.neuralnetworks@1.3::IPreparedModel

eee3430cc86c97c7b407495863d8fb61da6f1a64b7721e77b9b4909b11b174e9 android.hardware.neuralnetworks@1.3::IPreparedModelCallback

eee3430cc86c97c7b407495863d8fb61da6f1a64b7721e77b9b4909b11b174e9 android.hardware.neuralnetworks@1.3::IPreparedModelCallback

e442ab1b440327fe4e8a3b0b8ac6874e9bc6342e91fe976eb9fea77c63961ec8 android.hardware.neuralnetworks@1.3::types

acf84925f8ee0a651f2ec547ac334034de266479b93af5434f6c1f25e66aba96 android.hardware.neuralnetworks@1.3::types

b454df853441c12f6e425e8a60dd29fda20f5e6e39b93d1103e4b37495db38aa android.hardware.radio@1.5::IRadio

b454df853441c12f6e425e8a60dd29fda20f5e6e39b93d1103e4b37495db38aa android.hardware.radio@1.5::IRadio

fcbb0742a88215ee7a6d7ce0825d253eb2b50391fc6c8c48667f9fd7f6d4549e android.hardware.radio@1.5::IRadioIndication

fcbb0742a88215ee7a6d7ce0825d253eb2b50391fc6c8c48667f9fd7f6d4549e android.hardware.radio@1.5::IRadioIndication

b809193970a91ca637a4b0184767315601d32e3ef3d5992ffbc7a8d14a14f015 android.hardware.radio@1.5::IRadioResponse

b809193970a91ca637a4b0184767315601d32e3ef3d5992ffbc7a8d14a14f015 android.hardware.radio@1.5::IRadioResponse

neuralnetworks/1.0/types.hal

+5 −5
Original line number Original line Diff line number Diff line
@@ -628,7 +628,7 @@ enum OperationType : int32_t { 
    HASHTABLE_LOOKUP = 10,

    HASHTABLE_LOOKUP = 10,





    /**

    /**

     * Applies L2 normalization along the depth dimension.

     * Applies L2 normalization along the axis dimension.

     *

     *

     * The values in the output tensor are computed as:

     * The values in the output tensor are computed as:

     *

     *

neuralnetworks/1.2/types.hal

+4 −4
Original line number Original line Diff line number Diff line
@@ -846,7 +846,7 @@ enum OperationType : int32_t { 
    HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,

    HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,





    /**

    /**

     * Applies L2 normalization along the depth dimension.

     * Applies L2 normalization along the axis dimension.

     *

     *

     * The values in the output tensor are computed as:

     * The values in the output tensor are computed as:

     *

     *
@@ -854,8 +854,7 @@ enum OperationType : int32_t { 
     *         input[batch, row, col, channel] /

     *         input[batch, row, col, channel] /

     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))

     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))

     *

     *

     * For input tensor with rank less than 4, independently normalizes each

     * By default the axis dimension is the last dimension of the input tensor.

     * 1-D slice along dimension dim.

     *

     *

     * Supported tensor {@link OperandType}:

     * Supported tensor {@link OperandType}:

     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)

     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
@@ -3843,7 +3842,8 @@ enum OperationType : int32_t { 
     * * 1: A scalar {@link OperandType::INT32}, specifying the number of

     * * 1: A scalar {@link OperandType::INT32}, specifying the number of

     *      independent samples to draw for each row slice.

     *      independent samples to draw for each row slice.

     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],

     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],

     *      specifying seeds used to initialize the random distribution.

     *      specifying seeds used to initialize the random distribution. If both

     *      provided seeds are 0, both will be randomly generated.

     * Outputs:

     * Outputs:

     * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape

     * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape

     *      [batches, samples], containing the drawn samples.

     *      [batches, samples], containing the drawn samples.

neuralnetworks/1.3/types.hal

+8 −4
Original line number Original line Diff line number Diff line
@@ -833,7 +833,7 @@ enum OperationType : int32_t { 
    HASHTABLE_LOOKUP = @1.2::OperationType:HASHTABLE_LOOKUP,

    HASHTABLE_LOOKUP = @1.2::OperationType:HASHTABLE_LOOKUP,





    /**

    /**

     * Applies L2 normalization along the depth dimension.

     * Applies L2 normalization along the axis dimension.

     *

     *

     * The values in the output tensor are computed as:

     * The values in the output tensor are computed as:

     *

     *
@@ -841,8 +841,7 @@ enum OperationType : int32_t { 
     *         input[batch, row, col, channel] /

     *         input[batch, row, col, channel] /

     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))

     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))

     *

     *

     * For input tensor with rank less than 4, independently normalizes each

     * By default the axis dimension is the last dimension of the input tensor.

     * 1-D slice along dimension dim.

     *

     *

     * Supported tensor {@link OperandType}:

     * Supported tensor {@link OperandType}:

     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)

     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
@@ -867,6 +866,10 @@ enum OperationType : int32_t { 
     *      the scale must be 1.f / 128 and the zeroPoint must be 128.

     *      the scale must be 1.f / 128 and the zeroPoint must be 128.

     *      For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},

     *      For {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED},

     *      the scale must be 1.f / 128 and the zeroPoint must be 0.

     *      the scale must be 1.f / 128 and the zeroPoint must be 0.

     *

     *      NOTE: Before HAL version 1.3, if the elements along an axis are all zeros,

     *      the result is undefined. Since HAL version 1.3, if the elements along an axis

     *      are all zeros, the result is logical zero.

     */

     */

    L2_NORMALIZATION = @1.2::OperationType:L2_NORMALIZATION,

    L2_NORMALIZATION = @1.2::OperationType:L2_NORMALIZATION,
@@ -4063,7 +4066,8 @@ enum OperationType : int32_t { 
     * * 1: A scalar {@link OperandType::INT32}, specifying the number of

     * * 1: A scalar {@link OperandType::INT32}, specifying the number of

     *      independent samples to draw for each row slice.

     *      independent samples to draw for each row slice.

     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],

     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],

     *      specifying seeds used to initialize the random distribution.

     *      specifying seeds used to initialize the random distribution. If both

     *      provided seeds are 0, both will be randomly generated.

     * Outputs:

     * Outputs:

     * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape

     * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape

     *      [batches, samples], containing the drawn samples.

     *      [batches, samples], containing the drawn samples.