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Add quantized LSTM op.

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Bug: 144841609

Test: NeuralNetworksTest_static

Change-Id: I8aab51695382211e003d8fc442d33bae3006dc13
This commit is contained in:
Viet Dang
2019-11-26 12:23:32 +00:00
parent e019586ca4
commit c8b0cd33b9
3 changed files with 131 additions and 3 deletions
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2
current.txt
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@@ -648,7 +648,7 @@ a3eddd9bbdc87e8c22764070037dd1154f1cf006e6fba93364c4f85d4c134a19 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
7d136c169b62abdee0bb6abafb97638acd792ce2102dfccddaa5df98d4bd3df9 android.hardware.wifi.hostapd@1.2::IHostapd
11f6448d15336361180391c8ebcdfd2d7cf77b3782d577e594d583aadc9c2877 android.hardware.wifi.hostapd@1.2::types

131
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,

1
neuralnetworks/1.3/vts/functional/ValidateModel.cpp
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@@ -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)>;