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Commit ed5c0049 authored by Viet Dang's avatar Viet Dang Committed by Xusong Wang
Browse files

BidirectionalSequenceLSTM op: Adds layer norm support. 

Also updates documentation for this op and UnidirectionalSequenceLSTM
op.

Bug: 123644584

Test: in ag/6758764

Change-Id: I72d029fef6d890eb1771c21814b028b09af280c7
Merged-In: I72d029fef6d890eb1771c21814b028b09af280c7
(cherry picked from commit f404a1e8)
parent 2d47c80c

current.txt

+1 −1
Original line number Diff line number Diff line
@@ -450,7 +450,7 @@ dbe96a8ecf3c1f645585c27568464bc4db3c4b2d9a9624d88da606c59959afbe android.hardwar 
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback

83885d366f22ada42c00d8854f0b7e7ba4cf73ddf80bb0d8e168ce132cec57ea android.hardware.neuralnetworks@1.2::IPreparedModel

e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback

ba7e93fb136cabfde41ac1b035abd87a51f2c260cea89163984e4e9c69b55a5f android.hardware.neuralnetworks@1.2::types

3316184c595df550eb57837a6eed041d4682314b17b826969da3588ab12f19b6 android.hardware.neuralnetworks@1.2::types

cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc

abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types

4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats

neuralnetworks/1.2/types.hal

+95 −65
Original line number Diff line number Diff line
@@ -2270,113 +2270,113 @@ enum OperationType : int32_t { 
     * Inputs:

     * * 0: The input.

     *      A 3-D tensor of shape:

     *        If time-major: [max_time, batch_size, output_size]

     *        If batch-major: [batch_size, max_time, output_size]

     *        If time-major: [max_time, batch_size, input_size]

     *        If batch-major: [batch_size, max_time, input_size]

     *      where "max_time" is the number of timesteps (sequence length),

     *      "batch_size" corresponds to the batching dimension, and

     *      "input_size" is the size of the input.

     * * 1: The forward input-to-input weights. Optional.

     *      A 2-D tensor of shape [num_units, input_size], where “num_units”

     *      corresponds to the number of cell units.

     *      A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units”

     *      corresponds to the number of forward cell units.

     * * 2: The forward input-to-forget weights.

     *      A 2-D tensor of shape [num_units, input_size].

     *      A 2-D tensor of shape [fw_num_units, input_size].

     * * 3: The forward input-to-cell weights.

     *      A 2-D tensor of shape [num_units, input_size].

     *      A 2-D tensor of shape [fw_num_units, input_size].

     * * 4: The forward input-to-output weights.

     *      A 2-D tensor of shape [num_units, input_size].

     *      A 2-D tensor of shape [fw_num_units, input_size].

     * * 5: The forward recurrent-to-input weights. Optional.

     *      A 2-D tensor of shape [num_units, output_size], where “output_size”

     *      corresponds to either the number of cell units (i.e., “num_units”),

     *      or the second dimension of the “projection_weights”, if defined.

     *      A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size”

     *      corresponds to either the number of cell units (i.e., fw_num_units),

     *      or the second dimension of the “fw_projection_weights”, if defined.

     * * 6: The forward recurrent-to-forget weights.

     *      A 2-D tensor of shape [num_units, output_size].

     *      A 2-D tensor of shape [fw_num_units, fw_output_size].

     * * 7: The forward recurrent-to-cell weights.

     *      A 2-D tensor of shape [num_units, output_size].

     *      A 2-D tensor of shape [fw_num_units, fw_output_size].

     * * 8: The forward recurrent-to-output weights.

     *      A 2-D tensor of shape [num_units, output_size].

     *      A 2-D tensor of shape [fw_num_units, fw_output_size].

     * * 9: The forward cell-to-input weights. Optional.

     *      A 1-D tensor of shape [num_units].

     *      A 1-D tensor of shape [fw_num_units].

     * * 10: The forward cell-to-forget weights. Optional.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [fw_num_units].

     * * 11: The forward cell-to-output weights. Optional.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [fw_num_units].

     * * 12: The forward input gate bias. Optional.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [fw_num_units].

     * * 13: The forward forget gate bias.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [fw_num_units].

     * * 14: The forward cell gate bias.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [fw_num_units].

     * * 15: The forward output gate bias.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [fw_num_units].

     * * 16: The forward projection weights. Optional.

     *       A 2-D tensor of shape [output_size, num_units].

     *       A 2-D tensor of shape [fw_output_size, fw_num_units].

     * * 17: The forward projection bias. Optional.

     *       A 1-D tensor of shape [output_size].

     *       A 1-D tensor of shape [fw_output_size].

     * * 18: The backward input-to-input weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size], where “num_units”

     *       corresponds to the number of cell units.

     *       A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units”

     *       corresponds to the number of backward cell units.

     * * 19: The backward input-to-forget weights.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [bw_num_units, input_size].

     * * 20: The backward input-to-cell weights.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [bw_num_units, input_size].

     * * 21: The backward input-to-output weights.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [bw_num_units, input_size].

     * * 22: The backward recurrent-to-input weights. Optional.

     *       A 2-D tensor of shape [num_units, output_size], where “output_size”

     *       corresponds to either the number of cell units (i.e., “num_units”),

     *       or the second dimension of the “projection_weights”, if defined.

     *       A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size”

     *       corresponds to either the number of cell units (i.e., “bw_num_units”),

     *       or the second dimension of the “bw_projection_weights”, if defined.

     * * 23: The backward recurrent-to-forget weights.

     *       A 2-D tensor of shape [num_units, output_size].

     *       A 2-D tensor of shape [bw_num_units, bw_output_size].

     * * 24: The backward recurrent-to-cell weights.

     *       A 2-D tensor of shape [num_units, output_size].

     *       A 2-D tensor of shape [bw_num_units, bw_output_size].

     * * 25: The backward recurrent-to-output weights.

     *       A 2-D tensor of shape [num_units, output_size].

     *       A 2-D tensor of shape [bw_num_units, bw_output_size].

     * * 26: The backward cell-to-input weights. Optional.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [bw_num_units].

     * * 27: The backward cell-to-forget weights. Optional.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [bw_num_units].

     * * 28: The backward cell-to-output weights. Optional.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [bw_num_units].

     * * 29: The backward input gate bias. Optional.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [bw_num_units].

     * * 30: The backward forget gate bias.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [bw_num_units].

     * * 31: The backward cell gate bias.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [bw_num_units].

     * * 32: The backward output gate bias.

     *       A 1-D tensor of shape [num_units].

     *       A 1-D tensor of shape [bw_num_units].

     * * 33: The backward projection weights. Optional.

     *       A 2-D tensor of shape [output_size, num_units].

     *       A 2-D tensor of shape [bw_output_size, bw_num_units].

     * * 34: The backward projection bias. Optional.

     *       A 1-D tensor of shape [output_size].

     *       A 1-D tensor of shape [bw_output_size].

     * * 35: The forward input activation state.

     *       A 2-D tensor of shape [batch_size, output_size].

     *       A 2-D tensor of shape [batch_size, bw_output_size].

     * * 36: The forward input cell state.

     *       A 2-D tensor of shape [batch_size, num_units].

     *       A 2-D tensor of shape [batch_size, bw_num_units].

     * * 37: The backward input activation state.

     *       A 2-D tensor of shape [batch_size, output_size].

     *       A 2-D tensor of shape [batch_size, bw_output_size].

     * * 38: The backward input cell state.

     *       A 2-D tensor of shape [batch_size, num_units].

     *       A 2-D tensor of shape [batch_size, bw_num_units].

     * * 39: The auxiliary input. Optional.

     *       A 3-D tensor of shape [max_time, batch_size, input_size], where “batch_size”

     *       corresponds to the batching dimension, and “input_size” is the size

     *       of the input.

     * * 40: The forward auxiliary input-to-input weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [fw_num_units, input_size].

     * * 41: The forward auxiliary input-to-forget weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [fw_num_units, input_size].

     * * 42: The forward auxiliary input-to-cell weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [fw_num_units, input_size].

     * * 43: The forward auxiliary input-to-output weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [fw_num_units, input_size].

     * * 44: The backward auxiliary input-to-input weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [bw_num_units, input_size].

     * * 45: The backward auxiliary input-to-forget weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [bw_num_units, input_size].

     * * 46: The backward auxiliary input-to-cell weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [bw_num_units, input_size].

     * * 47: The backward auxiliary input-to-output weights. Optional.

     *       A 2-D tensor of shape [num_units, input_size].

     *       A 2-D tensor of shape [bw_num_units, input_size].

     * * 48: The activation function.

     *       A value indicating the activation function:

     *       <ul>
@@ -2408,16 +2408,46 @@ enum OperationType : int32_t { 
     * * 52: time_major

     *       An {@link OperandType::BOOL} scalar specifying the shape format

     *       of input and output tensors.

     * * 53: The forward input layer normalization weights. Optional.

     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs

     *       to activation at input gate.

     * * 54: The forward forget layer normalization weights. Optional.

     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs

     *       to activation at forget gate.

     * * 55: The forward cell layer normalization weights. Optional.

     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs

     *       to activation at cell gate.

     * * 56: The forward output layer normalization weights. Optional.

     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs

     *       to activation at output gate.

     * * 57: The backward input layer normalization weights. Optional.

     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs

     *       to activation at input gate.

     * * 58: The backward forget layer normalization weights. Optional.

     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs

     *       to activation at forget gate.

     * * 59: The backward cell layer normalization weights. Optional.

     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs

     *       to activation at cell gate.

     * * 60: The backward output layer normalization weights. Optional.

     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs

     *       to activation at output gate.

     *

     * Outputs:

     * * 0: The forward output.

     *      A 3-D tensor of shape:

     *        If time-major: [max_time, batch_size, output_size]

     *        If batch-major: [batch_size, max_time, output_size]

     *        If time-major and not merge_outputs:

     *          [max_time, batch_size, fw_output_size]

     *        If time-major and merge_outputs:

     *          [max_time, batch_size, fw_output_size + bw_output_size]

     *        If batch-major and not merge_outputs:

     *          [batch_size, max_time, fw_output_size]

     *        If batch-major and merge_outputs:

     *          [batch_size, max_time, fw_output_size + bw_output_size]

     * * 1: The backward output.  Unused if merge_outputs is true.

     *      A 3-D tensor of shape:

     *        If time-major: [max_time, batch_size, output_size]

     *        If batch-major: [batch_size, max_time, output_size]

     *        If time-major: [max_time, batch_size, bw_output_size]

     *        If batch-major: [batch_size, max_time, bw_output_size]

     *

     * Available since API level 29.

     */
@@ -4357,9 +4387,9 @@ enum OperationType : int32_t { 
     * Inputs:

     * * 0: The input (\f$x_t\f$).

     *      A 3-D tensor of shape:

     *        If time-major: [max_time, batch_size, output_size]

     *        If batch-major: [batch_size, max_time, output_size]

     *      where “max_size” is the number of timesteps (sequence length),

     *        If time-major: [max_time, batch_size, input_size]

     *        If batch-major: [batch_size, max_time, input_size]

     *      where “max_time” is the number of timesteps (sequence length),

     *      “batch_size” corresponds to the batching dimension, and

     *      “input_size” is the size of the input.

     * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
@@ -4419,16 +4449,16 @@ enum OperationType : int32_t { 
     *      projection layer, such that values are bound within

     *      [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.

     * * 23:Time-major if true, batch-major if false.

     * * 24:The input layer normalization weights.

     * * 24:The input layer normalization weights. Optional.

     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs

     *      to activation at input gate.

     * * 25:The forget layer normalization weights.

     * * 25:The forget layer normalization weights. Optional.

     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs

     *      to activation at forget gate.

     * * 26:The cell layer normalization weights.

     * * 26:The cell layer normalization weights. Optional.

     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs

     *      to activation at cell gate.

     * * 27:The output layer normalization weights.

     * * 27:The output layer normalization weights. Optional.

     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs

     *      to activation at output gate.

     *