Merge "BidirectionalSequenceLSTM op: Adds layer norm support." (b6ab7481) · Commits · e / os / android_hardware_interfaces

current.txt

+1 −1

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		@@ -510,7 +510,7 @@ b9422a9aca84df1ff9623dc12c0562abce97716e28d63a965f2bfb88f9ad9607 android.hardwar
		92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
		83885d366f22ada42c00d8854f0b7e7ba4cf73ddf80bb0d8e168ce132cec57ea android.hardware.neuralnetworks@1.2::IPreparedModel
		e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
		730c74ee5a3dd61a73f150cf07653e4b928e413b0f228eb004541bfcc22ed245 android.hardware.neuralnetworks@1.2::types
		2ef1bab554ea484523b396e48033117dbbefc2f90269f9e7e3eb5a58ba50bfb9 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

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		@@ -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.
		*