Fix operations docs
* Add missing float16-related docs
* Fix weights' shapes in QUANTIZED_16BIT_LSTM
Bug: 126872052
Bug: 126870779
Test: mma
Change-Id: Iba0b208b101db676b03b04e03e43c7378877843c
diff --git a/current.txt b/current.txt
index 3c147cb..937e7b1 100644
--- a/current.txt
+++ b/current.txt
@@ -449,7 +449,7 @@
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
83885d366f22ada42c00d8854f0b7e7ba4cf73ddf80bb0d8e168ce132cec57ea android.hardware.neuralnetworks@1.2::IPreparedModel
e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
-769f8650631eef7a3ceedc8cf130f4b99eb52fe698a11609d55de32985a3dddf android.hardware.neuralnetworks@1.2::types
+c752cff336d86762c26dc82e7e037f4962b815b1a068d2319d40a3d068e26f68 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
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index ab17598..4a8664f 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -218,6 +218,7 @@
* ) / sum(1)
*
* Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -333,7 +334,7 @@
* ) + bias[channel]
*
* Supported tensor {@link OperandType} configurations:
- * * 32 bit Floating point :
+ * * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
@@ -342,15 +343,15 @@
* * * input.scale * filter.scale).
*
* Available since API level 29:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
* * Quantized with symmetric per channel quantization for the filter:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
* * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
* * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
*
- * * 16 bit Floating point:
- * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
- *
* Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
* With the default data layout NHWC, the data is stored in the order of:
* [batch, height, width, channels]. Alternatively, the data layout could
@@ -482,7 +483,7 @@
* ) + bias[k * channel_multiplier + q]
*
* Supported tensor {@link OperandType} configurations:
- * * 32 bit Floating point :
+ * * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
@@ -491,6 +492,9 @@
* * * input.scale * filter.scale).
*
* Available since API level 29:
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
* * Quantized with symmetric per channel quantization for the filter:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
@@ -1010,6 +1014,7 @@
* output = 1 / (1 + exp(-input))
*
* Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1315,6 +1320,7 @@
* )
*
* Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1623,6 +1629,7 @@
* independently on each 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
+ * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
@@ -1631,8 +1638,12 @@
*
* Inputs:
* * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
- * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
- * scaling factor for the exponent, beta.
+ * * 1: A scalar, specifying the positive scaling factor for the exponent,
+ * beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
+ * {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
+ * {@link OperandType::FLOAT32}. If input0 is of {@link
+ * OperandType::TENSOR_FLOAT16}, then the scalar must be of {@link
+ * OperandType::FLOAT16}.
* * 2: An optional {@link OperandType::INT32} scalar, default to -1,
* specifying the dimension the activation would be performed on.
* Negative index is used to specify axis from the end (e.g. -1 for
@@ -2706,11 +2717,17 @@
* * 10: An {@link OperandType::INT32} scalar, only used when input7 is
* set to true, specifying the maximum number of detections when
* applying NMS algorithm for each single class.
- * * 11: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
- * with scores lower than the threshold are filtered before sending
- * to the NMS algorithm.
- * * 12: An {@link OperandType::FLOAT32} scalar, specifying the IoU
- * threshold for hard NMS.
+ * * 11: A scalar, score_threshold. Boxes with scores lower than the
+ * threshold are filtered before sending to the NMS algorithm. The
+ * scalar must be of {@link OperandType::FLOAT16} if input0 is of
+ * {@link OperandType::TENSOR_FLOAT16} and of {@link
+ * OperandType::FLOAT32} if input0 is of {@link
+ * OperandType::TENSOR_FLOAT32}.
+ * * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
+ * must be of {@link OperandType::FLOAT16} if input0 is of {@link
+ * OperandType::TENSOR_FLOAT16} and of {@link
+ * OperandType::FLOAT32} if input0 is of {@link
+ * OperandType::TENSOR_FLOAT32}.
* * 13: An {@link OperandType::BOOL} scalar, set to true to include
* background class in the list of label map for the output, set
* to false to not include the background. When the background
@@ -3007,11 +3024,11 @@
* where channel_multiplier = depth_out / num_groups
*
* Supported tensor {@link OperandType} configurations:
- * * 32 bit Floating point :
- * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
*
- * * 16 bit Floating point:
- * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ * * 32 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
@@ -3188,12 +3205,21 @@
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be normalized.
- * * 1: An {@link OperandType::FLOAT32} scalar, specifying gamma, the
- * scale applied to the normalized tensor.
- * * 2: An {@link OperandType::FLOAT32} scalar, specifying beta, the
- * offset applied to the normalized tensor.
- * * 3: An {@link OperandType::FLOAT32} scalar, specifying epsilon, the
- * small value added to variance to avoid dividing by zero.
+ * * 1: A scalar, specifying gamma, the scale applied to the normalized
+ * tensor. The scalar must be of {@link OperandType::FLOAT16} if
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
+ * OperandType::FLOAT32} if input0 is of {@link
+ * OperandType::TENSOR_FLOAT32}.
+ * * 2: A scalar, specifying beta, the offset applied to the normalized
+ * tensor. The scalar must be of {@link OperandType::FLOAT16} if
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
+ * OperandType::FLOAT32} if input0 is of {@link
+ * OperandType::TENSOR_FLOAT32}.
+ * * 3: A scalar, specifying epsilon, the small value added to variance to
+ * avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if
+ * input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
+ * OperandType::FLOAT32} if input0 is of {@link
+ * OperandType::TENSOR_FLOAT32}.
* * 4: An {@link OperandType::BOOL} scalar, set to true to specify
* NCHW data layout for input0 and output0. Set to false for NHWC.
*
@@ -3475,10 +3501,12 @@
* padding[i, 1] specifies the number of elements to be padded after
* the end of dimension i.
* * 2: An scalar specifying the value to use for padding input0.
+ * For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+ * pad value must be of {@link OperandType::FLOAT16}.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
- * pad value should be of {@link OperandType::FLOAT32}.
+ * pad value must be of {@link OperandType::FLOAT32}.
* For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
- * the pad value should be of {@link OperandType::INT32}. The
+ * the pad value must be of {@link OperandType::INT32}. The
* scale and zeroPoint are assumed to be the same as in input0.
*
* Outputs:
@@ -3627,25 +3655,25 @@
* weights.
* * 5: The recurrent-to-input weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
- * and shape [outputSize, inputSize] specifying recurrent-to-input part
+ * and shape [outputSize, outputSize] specifying recurrent-to-input part
* of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 6: The recurrent-to-forget weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
- * and shape [outputSize, inputSize] specifying recurrent-to-forget
+ * and shape [outputSize, outputSize] specifying recurrent-to-forget
* part of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 7: The recurrent-to-cell weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
- * and shape [outputSize, inputSize] specifying recurrent-to-cell part
+ * and shape [outputSize, outputSize] specifying recurrent-to-cell part
* of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
* * 8: The recurrent-to-output weights.
* A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
- * and shape [outputSize, inputSize] specifying recurrent-to-output
+ * and shape [outputSize, outputSize] specifying recurrent-to-output
* part of weights for fully-connected layer inside the LSTM cell.
* Quantization zero point and scale must be the same across all the
* weights.
@@ -4205,7 +4233,10 @@
* padding.
*
* Supported tensor {@link OperandCode} configurations:
- * * 32 bit Floating point :
+ * * 16 bit floating point:
+ * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+ *
+ * * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* * Quantized:
@@ -4213,7 +4244,6 @@
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
- * Available since API level 29:
* * Quantized with symmetric per channel quantization for the filter:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
* * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.