tequilaOS/platform_hardware_interfaces
2
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

Merge "NNAPI: Add state outputs for {UNI|BI}DIRECTIONAL_SEQUENCE_{RNN|LSTM}"

Browse source
This commit is contained in:
Lev Proleev 2020-02-12 11:12:57 +00:00 committed by Android (Google) Code Review
commit 0b0d69e74f
3 changed files with 59 additions and 4 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
current.txt
View file

@ -675,7 +675,7 @@ a3eddd9bbdc87e8c22764070037dd1154f1cf006e6fba93364c4f85d4c134a19 android.hardwar
2fa3679ad7c94b5e88724adcd560c561041068a4ca565c63830e68101988746a android.hardware.neuralnetworks@1.3::IFencedExecutionCallback
237b23b126a66f3432658020fed78cdd06ba6297459436fe6bae0ba753370833 android.hardware.neuralnetworks@1.3::IPreparedModel
0439a1fbbec7f16e5e4c653d85ac685d51bfafbae15b8f8cca530acdd7d6a8ce android.hardware.neuralnetworks@1.3::IPreparedModelCallback
abbc4e1a969881c9f8ab587add5b5e75b08df834c9c969c013ae38cb4bb16f6a android.hardware.neuralnetworks@1.3::types
2fabd246f985d94a0172dacefb0d6cf19e2aeb2d5f17752653988ef39570a52d android.hardware.neuralnetworks@1.3::types
3e01d4446cd69fd1c48f8572efd97487bc179564b32bd795800b97bbe10be37b android.hardware.wifi@1.4::IWifi
c67aaf26a7a40d14ea61e70e20afacbd0bb906df1704d585ac8599fbb69dd44b android.hardware.wifi.hostapd@1.2::IHostapd
2b5a7ea572b736030c64a3b4043af244425477c4672301780fe15aba5ed393d9 android.hardware.wifi.hostapd@1.2::types

49
neuralnetworks/1.3/types.hal
View file

@ -2556,6 +2556,30 @@ enum OperationType : int32_t {
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, bw_output_size]
* If batch-major: [batch_size, max_time, bw_output_size]
* * 2: The forward activation state output.
* A 2-D tensor of shape [batch_size, fw_output_size] containing an
* activation state from the last time step in the sequence. This
* output is optional and can be omitted. If this output is present
* then outputs 3-5 must be present as well.
* Available since HAL version 1.3.
* * 3: The forward cell state output.
* A tensor of shape [batch_size, fw_cell_size] containing a cell state
* from the last time step in the sequence. This output is optional
* and can be omitted. If this output is present
* then outputs 2, 4, 5 must be present as well.
* Available since HAL version 1.3.
* * 4: The backward activation state output.
* A 2-D tensor of shape [batch_size, bw_output_size] containing an
* activation state from the last time step in the sequence. This
* output is optional and can be omitted. If this output is present
* then outputs 2, 3, 5 must be present as well.
* Available since HAL version 1.3.
* * 5: The backward cell state output.
* A tensor of shape [batch_size, bw_cell_size] containing a cell state
* from the last time step in the sequence. This output is optional
* and can be omitted. If this output is present
* then outputs 2-4 must be present as well.
* Available since HAL version 1.3.
*/
BIDIRECTIONAL_SEQUENCE_LSTM = @1.2::OperationType:BIDIRECTIONAL_SEQUENCE_LSTM,
@ -2673,6 +2697,18 @@ enum OperationType : int32_t {
* (timeMajor). If it is set to true, then the shape is set to
* [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
* [batchSize, maxTime, bwNumUnits].
* * 2: The forward hidden state output.
* A 2-D tensor of shape [batchSize, fwNumUnits] containing a hidden
* state from the last time step in the sequence. This output is
* optional and can be omitted. If this output is present then output
* 3 must be present as well.
* Available since HAL version 1.3.
* * 3: The backward hidden state output.
* A 2-D tensor of shape [batchSize, bwNumUnits] containing a hidden
* state from the last time step in the sequence. This output is
* optional and can be omitted. If this output is present then output
* 2 must be present as well.
* Available since HAL version 1.3.
*/
BIDIRECTIONAL_SEQUENCE_RNN = @1.2::OperationType:BIDIRECTIONAL_SEQUENCE_RNN,
@ -4656,6 +4692,15 @@ enum OperationType : int32_t {
* A 3-D tensor of shape:
* If time-major: [max_time, batch_size, output_size]
* If batch-major: [batch_size, max_time, output_size]
* * 1: A tensor of shape [batch_size, output_size] containing a hidden
* state from the last time step in the sequence. This output is
* optional and can be omitted. If this output is present then
* output #2 must be present as well.
* Available since HAL version 1.3.
* * 2: A tensor of shape [batch_size, cell_size] containing a cell state
* from the last time step in the sequence. This output is optional
* and can be omitted.
* Available since HAL version 1.3.
*/
UNIDIRECTIONAL_SEQUENCE_LSTM = @1.2::OperationType:UNIDIRECTIONAL_SEQUENCE_LSTM,
@ -4711,6 +4756,10 @@ enum OperationType : int32_t {
* it is set to 1, then the output has a shape [maxTime, batchSize,
* numUnits], otherwise the output has a shape [batchSize, maxTime,
* numUnits].
* * 1: A tensor of shape [batchSize, numUnits] containing hidden state
* from the last time step in the sequence. This output is optional
* and can be omitted.
* Available since HAL version 1.3.
*/
UNIDIRECTIONAL_SEQUENCE_RNN = @1.2::OperationType:UNIDIRECTIONAL_SEQUENCE_RNN,

12
neuralnetworks/1.3/vts/functional/ValidateModel.cpp
View file

@ -527,9 +527,15 @@ static bool removeOperandSkip(size_t operand, const Model& model) {
}
}
}
// BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have either one or two
// outputs depending on their mergeOutputs parameter.
if (operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
// BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have
// either one, two, three or four outputs depending on their
// mergeOutputs parameter and if state outputs are provided.
// UNIDIRECTIONAL_SEQUENCE_LSTM and UNIDIRECTIONAL_SEQUENCE_RNN can have
// either one or three outputs depending on whether state outputs are
// provided.
if (operation.type == OperationType::UNIDIRECTIONAL_SEQUENCE_LSTM ||
operation.type == OperationType::UNIDIRECTIONAL_SEQUENCE_RNN ||
operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
for (const size_t outOprand : operation.outputs) {
if (operand == outOprand) {