Add quantized LSTM op. am: 232cfebd5b am: f3c5bf1cc8 (db335ad7) · Commits · e / os / android_hardware_interfaces

current.txt

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		@@ -627,7 +627,7 @@ adb0efdf1462e9b2e742c0dcadd598666aac551f178be06e755bfcdf5797abd0 android.hardwar
		9e59fffceed0dd72a9799e04505db5f777bbbea1af0695ba4107ef6d967c6fda android.hardware.neuralnetworks@1.3::IDevice
		258825966435b3ed08832055bb736d81516013e405f161d9ccde9a90cfcdde83 android.hardware.neuralnetworks@1.3::IPreparedModel
		94e803236398bed1febb11cc21051bc42ec003700139b099d6c479e02a7ca3c3 android.hardware.neuralnetworks@1.3::IPreparedModelCallback
		35668befe89fc7f84d58fc1dab7dd3e4d6067c7eeccbae154fe36cd964dfaef7 android.hardware.neuralnetworks@1.3::types
		618a628f8c94d6f6e4cb401b69fa50ccb8b82191ea434e3a071252289b4f312c android.hardware.neuralnetworks@1.3::types
		3e01d4446cd69fd1c48f8572efd97487bc179564b32bd795800b97bbe10be37b android.hardware.wifi@1.4::IWifi
		a64467bae843569f0d465c5be7f0c7a5b987985b55a3ef4794dd5afc68538650 android.hardware.wifi.supplicant@1.3::ISupplicant
		44445b8a03d7b9e68b2fbd954672c18a8fce9e32851b0692f4f4ab3407f86ecb android.hardware.wifi.supplicant@1.3::ISupplicantStaIface

neuralnetworks/1.3/types.hal

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		@@ -4746,6 +4746,135 @@ enum OperationType : int32_t {
		*/
		RESIZE_NEAREST_NEIGHBOR = @1.2::OperationType:RESIZE_NEAREST_NEIGHBOR,

		/**
		* Quantized version of {@link OperationType:LSTM}.
		*
		* The input and the output use asymmetric quantized types, while the rest
		* use symmetric ones.
		*
		* Inputs:
		* * 0: The input to the LSTM cell.
		* Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
		* Shape: [batchSize, inputSize]
		* * 1: The input-to-input weights. Optional.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, inputSize]
		* * 2: The input-to-forget weights.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, inputSize]
		* * 3: The input-to-cell weights.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, inputSize]
		* * 4: The input-to-output weights.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, inputSize]
		* * 5: The recurrent-to-input weights. Optional.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, outputSize]
		* * 6: The recurrent-to-forget weights.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, outputSize]
		* * 7: The recurrent-to-cell weights.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, outputSize]
		* * 8: The recurrent-to-output weights.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [numUnits, outputSize]
		* * 9: The cell-to-input weights (for peephole). Optional.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [numUnits]
		* * 10: The cell-to-forget weights (for peephole). Optional.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [numUnits]
		* * 11: The cell-to-output weights (for peephole). Optional.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [numUnits]
		* * 12: The input gate bias. Quantized with scale being the
		* product of input and weights scales and zeroPoint equal to 0.
		* Optional.
		* Type: {@link OperandType::TENSOR_INT32}
		* Shape: [numUnits]
		* * 13: The forget gate bias. Quantized with scale being the
		* product of input and weights scales and zeroPoint equal to 0.
		* Type: {@link OperandType::TENSOR_INT32}
		* Shape: [numUnits]
		* * 14: The cell bias. Quantized with scale being the
		* product of input and weights scales and zeroPoint equal to 0.
		* Type: {@link OperandType::TENSOR_INT32}
		* Shape: [numUnits]
		* * 15: The output gate bias. Quantized with scale being the
		* product of input and weights scales and zeroPoint equal to 0.
		* Type: {@link OperandType::TENSOR_INT32}
		* Shape: [numUnits]
		* * 16: The projection weights. Optional.
		* Type: {@link OperandType::TENSOR_QUANT8_SYMM}
		* Shape: [outputSize, numUnits]
		* * 17: The projection bias. Quantized with scale being the
		* product of input and weights scales and zeroPoint equal to 0.
		* Optional.
		* Type: {@link OperandType::TENSOR_INT32}
		* Shape: [outputSize]
		* * 18: The output from the previous time step.
		* Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
		* Shape: [batchSize, outputSize]
		* * 19: The cell state from the previous time step.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [batchSize, numUnits]
		* * 20: The input layer normalization weights. Used to rescale
		* normalized inputs to activation at input gate. Optional.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [numUnits]
		* * 21: The forget layer normalization weights. Used to
		* rescale normalized inputs to activation at forget gate. Optional.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [numUnits]
		* * 22: The cell layer normalization weights. Used to rescale
		* normalized inputs to activation at cell gate. Optional.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [numUnits]
		* * 23: The output layer normalization weights. Used to
		* rescale normalized inputs to activation at output gate. Optional.
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [numUnits]
		* * 24: The cell clip. If provided the cell state is clipped
		* by this value prior to the cell output activation. Optional.
		* Type: {@link OperandType::FLOAT32}.
		* * 25: The projection clip. If provided and projection is enabled,
		* this is used for clipping the projected values. Optional.
		* Type: {@link OperandType::FLOAT32}.
		* * 26: The scale of the intermediate result of matmul,
		* i.e. input to layer normalization, at input gate.
		* Type: {@link OperandType::FLOAT32}.
		* * 27: The scale of the intermediate result of matmul,
		* i.e. input to layer normalization, at forget gate.
		* Type: {@link OperandType::FLOAT32}.
		* * 28: The scale of the intermediate result of matmul,
		* i.e. input to layer normalization, at cell gate.
		* Type: {@link OperandType::FLOAT32}.
		* * 29: The scale of the intermediate result of matmul,
		* i.e. input to layer normalization, at output gate.
		* Type: {@link OperandType::FLOAT32}.
		* * 30: The zero point of the hidden state, i.e. input to
		* projection.
		* Type: {@link OperandType::INT32}.
		* * 31: The scale of the hidden state, i.e. input to
		* projection.
		* Type: {@link OperandType::FLOAT32}.
		*
		* Outputs:
		* * 0: The output state (out).
		* Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
		* Shape: [batchSize, outputSize]
		* * 1: The cell state (out).
		* Type: {@link OperandType::TENSOR_QUANT16_SYMM}
		* Shape: [batchSize, numUnits]
		* * 2: The output. This is effectively the same as the current
		* "output state (out)" value.
		* Type: {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}
		* Shape: [batchSize, outputSize]
		*/
		QUANTIZED_LSTM = 95,

		/**
		* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
		* OEM operation and data types.
		@@ -4768,7 +4897,7 @@ enum OperationType : int32_t {
		enum OperationTypeRange : uint32_t {
		BASE_MIN = 0,
		FUNDAMENTAL_MIN = 0,
		FUNDAMENTAL_MAX = 94,
		FUNDAMENTAL_MAX = 95,
		OEM_MIN = 10000,
		OEM_MAX = 10000,
		BASE_MAX = 0xFFFF,

neuralnetworks/1.3/vts/functional/ValidateModel.cpp

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Original line number	Diff line number	Diff line
		@@ -27,7 +27,6 @@ using implementation::PreparedModelCallback;
		using V1_0::ErrorStatus;
		using V1_0::OperandLifeTime;
		using V1_1::ExecutionPreference;
		using V1_2::OperationTypeRange;
		using V1_2::SymmPerChannelQuantParams;
		using HidlToken =
		hidl_array<uint8_t, static_cast<uint32_t>(V1_2::Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;