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BidirectionalSequenceLSTM op: Adds layer norm support.

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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 f404a1e894)
This commit is contained in:
Viet Dang 2019-03-20 10:35:21 +00:00 committed by Xusong Wang
parent 2d47c80c8e
commit ed5c004915
2 changed files with 96 additions and 66 deletions
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current.txt
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@ -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

160
neuralnetworks/1.2/types.hal
View file

@ -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.
*