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Replace sync_enums_to_hal.py with generate_api.{py,sh} and regenerate */types.hal am: 8c0a48bceb

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am: b14faf16d3

Change-Id: I1f22cc68be18e646a35928cb068d05d60d70f541
This commit is contained in:
David Gross
2019-10-09 12:20:33 -07:00
committed by android-build-merger
parent 3057612183 b14faf16d3
commit 14556dfcc6
7 changed files with 1922 additions and 690 deletions
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3
current.txt
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@@ -574,8 +574,11 @@ cfa81f229b69f9011c58f48264fcb552447430fe68610eac514e811e65bc306a android.hardwar
# ABI preserving changes to HALs during Android R
b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice
eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel
f1109cbb10297b7429a11fab42afa912710b303c9bf20bd5cdb8bd57b9c84186 android.hardware.neuralnetworks@1.0::types
9d8ee57c490ffeaa28f702eaea8d198cb510e4bbfb99e6cb5f63e73341057c7c android.hardware.neuralnetworks@1.1::types
fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice
40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel
71c0f7127335e5b74d1615d5e7f129831b43ffbae5318ad0924d7d8d8910a859 android.hardware.neuralnetworks@1.2::types
a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types
1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback
fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface

467
neuralnetworks/1.0/types.hal
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@@ -25,25 +25,24 @@ package android.hardware.neuralnetworks@1.0;
* with at least one dimension). Types not prefaced by TENSOR_* represent
* scalar values and must have no dimensions.
*
* Although many types are defined, most operators accept just a few
* Although we define many types, most operators accept just a few
* types. Most used are {@link OperandType::TENSOR_FLOAT32},
* {@link OperandType::TENSOR_QUANT8_ASYMM},
* and {@link OperandType::INT32}.
*/
enum OperandType : int32_t {
/** A 32 bit floating point scalar value. */
FLOAT32 = 0,
FLOAT32 = 0,
/** A signed 32 bit integer scalar value. */
INT32 = 1,
INT32 = 1,
/** An unsigned 32 bit integer scalar value. */
UINT32 = 2,
UINT32 = 2,
/** A tensor of 32 bit floating point values. */
TENSOR_FLOAT32 = 3,
TENSOR_FLOAT32 = 3,
/** A tensor of 32 bit integer values. */
TENSOR_INT32 = 4,
TENSOR_INT32 = 4,
/**
* A tensor of 8 bit integers that represent real numbers.
* A tensor of 8 bit unsigned integers that represent real numbers.
*
* Attached to this tensor are two numbers that can be used to convert the
* 8 bit integer to the real value and vice versa. These two numbers are:
@@ -51,21 +50,21 @@ enum OperandType : int32_t {
* - zeroPoint: a 32 bit integer, in range [0, 255].
*
* The formula is:
* real_value = (integer_value - zeroPoint) * scale.
* real_value = (integer_value - zeroPoint) * scale.
*/
TENSOR_QUANT8_ASYMM = 5,
/**
* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
* OEM operation and data types.
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* OEM specific scalar value.
*/
OEM = 10000,
/**
* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
* OEM operation and data types.
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
*/
@@ -78,7 +77,6 @@ enum OperandType : int32_t {
* The type of an operation in a model.
*/
enum OperationType : int32_t {
/**
* Adds two tensors, element-wise.
*
@@ -110,14 +108,16 @@ enum OperationType : int32_t {
* * 0: A tensor.
* * 1: A tensor of the same {@link OperandType}, and compatible dimensions
* as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scales and zeroPoint can be different from input0 scale and zeroPoint.
* * 2: An {@link OperandType::INT32} scalar, and has to be one of the
* {@link FusedActivationFunc} values. Specifies the activation to
* invoke on the result.
*
* Outputs:
* * 0: The sum, a tensor of the same {@link OperandType} as input0.
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint can be different from inputs' scale and zeroPoint.
*/
ADD = 0,
@@ -187,8 +187,8 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
AVERAGE_POOL_2D = 1,
@@ -206,22 +206,23 @@ enum OperationType : int32_t {
*
* Inputs:
* * 0 ~ n-1: The list of n input tensors, of shape
* [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
* {@link OperandType::TENSOR_QUANT8_ASYMM}, all input tensors
* must have the same scale and zeroPoint.
* [D0, D1, ..., Daxis(i), ..., Dm].
* All input tensors of
* {@link OperandType::TENSOR_QUANT8_ASYMM}
* must have the same scale and zeroPoint as the output tensor.
* * n: An {@link OperandType::INT32} scalar, specifying the
* concatenation axis.
*
* Outputs:
* * 0: The output, a tensor of the same {@link OperandType} as the input
* tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, the scale and zeroPoint
* values must be the same as the input tensors'.
*/
CONCATENATION = 2,
/**
* Performs an 2-D convolution operation.
* Performs a 2-D convolution operation.
*
* The CONV_2D op sweeps a 2-D filter that can mix channels together over a
* batch of images, applying the filter to each window of each image of the
@@ -238,11 +239,17 @@ enum OperationType : int32_t {
* filter[channel, di, dj, k]
* ) + bias[channel]
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor {@link OperandType} configurations:
* * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* Supported tensor rank: 4, with "NHWC" data layout.
* * Quantized:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -252,12 +259,12 @@ enum OperationType : int32_t {
* * 1: A 4-D tensor, of shape
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
* should also be of {@link OperandType::TENSOR_FLOAT32}. For input
* tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias
* should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
* 0 and bias_scale == input_scale * filter_scale.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of type {@link OperandType::TENSOR_FLOAT32}
* the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -281,11 +288,11 @@ enum OperationType : int32_t {
* [depth_out, filter_height, filter_width, depth_in], specifying the
* filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* tensor of type {@link OperandType::TENSOR_FLOAT32}
* the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -299,11 +306,9 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out]. For output tensor of
* {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale.
*
* Available since API level 27.
* [batches, out_height, out_width, depth_out].
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied: output_scale > input_scale * filter_scale
*/
CONV_2D = 3,
@@ -329,11 +334,17 @@ enum OperationType : int32_t {
* filter[1, di, dj, k * channel_multiplier + q]
* ) + bias[k * channel_multiplier + q]
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* Supported tensor {@link OperandType} configurations:
* * 32 bit floating point:
* * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
*
* Supported tensor rank: 4, with "NHWC" data layout.
* * Quantized:
* * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
* * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
* * * input.scale * filter.scale).
*
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -343,11 +354,11 @@ enum OperationType : int32_t {
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* tensor of type {@link OperandType::TENSOR_FLOAT32}
* the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the padding on
* the left, in the ‘width’ dimension.
* * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -372,11 +383,11 @@ enum OperationType : int32_t {
* * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
* specifying the filter.
* * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
* tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
* also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
* of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
* of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
* bias_scale == input_scale * filter_scale.
* tensor of type {@link OperandType::TENSOR_FLOAT32}
* the bias must be of the same
* type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
* of 0 and bias_scale == input_scale * filter_scale.
* * 3: An {@link OperandType::INT32} scalar, specifying the implicit
* padding scheme, has to be one of the
* following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -392,11 +403,10 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth_out]. For output tensor of
* {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
* must be satisfied: output_scale > input_scale * filter_scale.
*
* Available since API level 27.
* [batches, out_height, out_width, depth_out]. For
* output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input_scale * filter_scale
*/
DEPTHWISE_CONV_2D = 4,
@@ -419,7 +429,8 @@ enum OperationType : int32_t {
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -431,8 +442,8 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The output 4-D tensor, of shape [batch, height*block_size,
* width*block_size, depth/(block_size*block_size)].
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
DEPTH_TO_SPACE = 5,
@@ -443,19 +454,19 @@ enum OperationType : int32_t {
*
* output = (input - zeroPoint) * scale.
*
* Supported tensor {@link OperandType}:
* Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported output tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}.
*
* Supported tensor rank: up to 4
*
* Inputs:
* * 0: A tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}.
* * 0: A tensor.
*
* Outputs:
* * 0: The output tensor of same shape as input0, but with
* {@link OperandType::TENSOR_FLOAT32}.
*
* Available since API level 27.
* * 0: A tensor with the same shape as input0.
*/
DEQUANTIZE = 6,
@@ -479,6 +490,13 @@ enum OperationType : int32_t {
* If a value in Lookups is out of bounds, the operation must fail
* and an error must be reported.
*
* Supported value tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported value tensor rank: from 2
*
* Inputs:
* * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
* The values are indices into the first dimension of Values.
@@ -489,8 +507,8 @@ enum OperationType : int32_t {
* * 0: A n-D tensor with the same rank and shape as the Values
* tensor, except for the first dimension which has the same size
* as Lookups' only dimension.
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input1.
*/
EMBEDDING_LOOKUP = 7,
@@ -508,8 +526,6 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The output tensor, of the same {@link OperandType} and dimensions as
* the input tensor.
*
* Available since API level 27.
*/
FLOOR = 8,
@@ -549,12 +565,9 @@ enum OperationType : int32_t {
* invoke on the result.
*
* Outputs:
* * 0: The output tensor, of shape [batch_size, num_units]. For output
* tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied:
* output_scale > input_scale * filter_scale.
*
* Available since API level 27.
* * 0: The output tensor, of shape [batch_size, num_units]. For
* output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
* condition must be satisfied: output_scale > input_scale * filter_scale.
*/
FULLY_CONNECTED = 9,
@@ -585,6 +598,13 @@ enum OperationType : int32_t {
* must be selected. If no entry in Keys has 123456, a slice of zeroes
* must be concatenated.
*
* Supported value tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported value tensor rank: from 2
*
* Inputs:
* * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
* shape [ k ].
@@ -598,13 +618,13 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: Output. A tensor with shape [ k …].
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input2.
* * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
* hits (True) or not (False).
* Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
* and scale 1.0f.
* A non-zero byte represents True, a hit. A zero indicates otherwise.
*
* Available since API level 27.
*/
HASHTABLE_LOOKUP = 10,
@@ -617,9 +637,6 @@ enum OperationType : int32_t {
* input[batch, row, col, channel] /
* sqrt(sum_{c} pow(input[batch, row, col, c], 2))
*
* For input tensor with more dimensions, independently normalizes each 1-D
* slice along dimension dim.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
@@ -627,13 +644,10 @@ enum OperationType : int32_t {
* Height, Width, and Channels).
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth].
* * 0: A 4-D tensor, specifying the tensor to be normalized.
*
* Outputs:
* * 0: The output 4-D tensor, of the same shape as input
* [batches, height, width, depth].
*
* Available since API level 27.
* * 0: A tensor of the same {@link OperandType} and same shape as input0.
*/
L2_NORMALIZATION = 11,
@@ -652,7 +666,8 @@ enum OperationType : int32_t {
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -700,8 +715,6 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*
* Available since API level 27.
*/
L2_POOL_2D = 12,
@@ -729,17 +742,18 @@ enum OperationType : int32_t {
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the radius of
* the normalization window.
* * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
* not be zero.
* * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
* factor, alpha.
* * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
* beta.
* * 2: A scalar, specifying the bias, must not be zero.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
* value must be of {@link OperandType::FLOAT32}.
* * 3: A scalar, specifying the scale factor, alpha.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
* alpha value must be of {@link OperandType::FLOAT32}.
* * 4: A scalar, specifying the exponent, beta.
* For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
* value must be of {@link OperandType::FLOAT32}.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
*/
LOCAL_RESPONSE_NORMALIZATION = 13,
@@ -763,45 +777,53 @@ enum OperationType : int32_t {
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*
* Available since API level 27.
*/
LOGISTIC = 14,
/**
* Projects an input to a bit vector via locality senstive hashing.
*
* Supported input tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_INT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported input tensor rank: from 1
*
* Inputs:
* * 0: Hash functions. Dim.size == 2, DataType: Float.
* Tensor[0].Dim[0]: Number of hash functions.
* Tensor[0].Dim[1]: Number of seeds per hash functions.
* Tensor[0].Dim[1] <= 32 in sparse case.
* Tensor[0].Dim[0]: Number of hash functions.
* Tensor[0].Dim[1]: Number of projected output bits generated by each
* hash function.
* If the projection type is Sparse:
* Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
*
* * 1: Input. Dim.size >= 1, no restriction on DataType.
* * 2: Weight. Optional. Dim.size == 1, DataType: Float.
* If not set, each input element is considered to have the same weight
* of 1.0.
* Tensor[1].Dim[0] == Tensor[2].Dim[0]
* If not set, each input element is considered to have the same weight
* of 1.0.
* Tensor[1].Dim[0] == Tensor[2].Dim[0]
* * 3: Type:
* Sparse: Value LSHProjectionType_SPARSE(=1).
* Sparse:
* Value LSHProjectionType_SPARSE(=1).
* Computed bit vector is considered to be sparse.
* Each output element is an int32 made up of multiple bits
* computed from hash functions.
*
* Dense: Value LSHProjectionType_DENSE(=2).
* Dense:
* Value LSHProjectionType_DENSE(=2).
* Computed bit vector is considered to be dense. Each output
* element represents a bit and can take the value of either
* 0 or 1.
*
* Outputs:
* * 0: If the projection type is sparse:
* Output.Dim == { Tensor[0].Dim[0] }
* A tensor of int32 that represents hash signatures.
* If the projection type is Dense:
* Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
* A flattened tensor that represents projected bit vectors.
* * 0: If the projection type is Sparse:
* Output.Dim == { Tensor[0].Dim[0] }
* A tensor of int32 that represents hash signatures.
*
* Available since API level 27.
* If the projection type is Dense:
* Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
* A flattened tensor that represents projected bit vectors.
*/
LSH_PROJECTION = 15,
@@ -901,71 +923,54 @@ enum OperationType : int32_t {
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input and output tensors must be of the same type.
*
* Inputs:
* * 0: The input (\f$x_t\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, input_size], where “batch_size” corresponds to the
* batching dimension, and “input_size” is the size of the input.
* A 2-D tensor of shape [batch_size, input_size], where “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.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size], where “num_units” corresponds to the
* number of cell units.
* A 2-D tensor of shape [num_units, input_size], where “num_units”
* corresponds to the number of cell units.
* * 2: The input-to-forget weights (\f$W_{xf}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* A 2-D tensor of shape [num_units, input_size].
* * 3: The input-to-cell weights (\f$W_{xc}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* A 2-D tensor of shape [num_units, input_size].
* * 4: The input-to-output weights (\f$W_{xo}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size].
* A 2-D tensor of shape [num_units, input_size].
* * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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 [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.
* * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* A 2-D tensor of shape [num_units, output_size].
* * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* A 2-D tensor of shape [num_units, output_size].
* * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, output_size].
* A 2-D tensor of shape [num_units, output_size].
* * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 12:The input gate bias (\f$b_i\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 13:The forget gate bias (\f$b_f\f$).
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 14:The cell bias (\f$b_c\f$).
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 15:The output gate bias (\f$b_o\f$).
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 16:The projection weights (\f$W_{proj}\f$). Optional.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [output_size, num_units].
* A 2-D tensor of shape [output_size, num_units].
* * 17:The projection bias (\f$b_{proj}\f$). Optional.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [output_size].
* A 1-D tensor of shape [output_size].
* * 18:The output state (in) (\f$h_{t-1}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, output_size].
* A 2-D tensor of shape [batch_size, output_size].
* * 19:The cell state (in) (\f$C_{t-1}\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
* A 2-D tensor of shape [batch_size, num_units].
* * 20:The activation function (\f$g\f$).
* A value indicating the activation function:
* <ul>
@@ -984,21 +989,15 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: The scratch buffer.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units * 3] with CIFG, or
* A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
* [batch_size, num_units * 4] without CIFG.
* * 1: The output state (out) (\f$h_t\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, output_size].
* A 2-D tensor of shape [batch_size, output_size].
* * 2: The cell state (out) (\f$C_t\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
* A 2-D tensor of shape [batch_size, num_units].
* * 3: The output (\f$o_t\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, output_size]. This is effectively the same as the
* current “output state (out)” value.
*
* Available since API level 27.
* A 2-D tensor of shape [batch_size, output_size]. This is effectively
* the same as the current “output state (out)” value.
*/
LSTM = 16,
@@ -1019,7 +1018,8 @@ enum OperationType : int32_t {
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Both explicit padding and implicit padding are supported.
*
@@ -1067,8 +1067,8 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, out_height, out_width, depth].
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
MAX_POOL_2D = 17,
@@ -1106,8 +1106,6 @@ enum OperationType : int32_t {
* For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
* the following condition must be satisfied:
* output_scale > input1_scale * input2_scale.
*
* Available since API level 27.
*/
MUL = 18,
@@ -1129,8 +1127,8 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RELU = 19,
@@ -1151,9 +1149,9 @@ enum OperationType : int32_t {
* * 0: A tensor, specifying the input.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
* * 0: The output tensor of the same shape as input0.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RELU1 = 20,
@@ -1175,8 +1173,8 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RELU6 = 21,
@@ -1205,8 +1203,8 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: The output tensor, of shape specified by the input shape.
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
RESHAPE = 22,
@@ -1220,9 +1218,10 @@ enum OperationType : int32_t {
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* Supported tensor rank: 4, with "NHWC" data layout.
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Inputs:
* Inputs (resizing by shape):
* * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
* the input.
* * 1: An {@link OperandType::INT32} scalar, specifying the output
@@ -1233,8 +1232,6 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The output 4-D tensor, of shape
* [batches, new_height, new_width, depth].
*
* Available since API level 27.
*/
RESIZE_BILINEAR = 23,
@@ -1257,25 +1254,23 @@ enum OperationType : int32_t {
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* The input tensors must all be the same type.
*
* Inputs:
* * 0: input.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32} of shape
* [batch_size, input_size], where “batch_size” corresponds to the
* batching dimension, and “input_size” is the size of the input.
* A 2-D tensor of shape [batch_size, input_size], where “batch_size”
* corresponds to the batching dimension, and “input_size” is the size
* of the input.
* * 1: weights.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size], where “num_units” corresponds to the
* number of units.
* A 2-D tensor of shape [num_units, input_size], where “num_units”
* corresponds to the number of units.
* * 2: recurrent_weights.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, num_units], with columns corresponding to the weights
* from each unit.
* A 2-D tensor of shape [num_units, num_units], with columns
* corresponding to the weights from each unit.
* * 3: bias.
* A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units].
* A 1-D tensor of shape [num_units].
* * 4: hidden state (in).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
* A 2-D tensor of shape [batch_size, num_units].
* * 5: fused_activation_function.
* An optional {@link FusedActivationFunc} value indicating the
* activation function. If “NONE” is specified then it results in a
@@ -1283,15 +1278,11 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: hidden state (out).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units].
* A 2-D tensor of shape [batch_size, num_units].
*
* * 1: output.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units]. This is effectively the same as the
* current state value.
*
* Available since API level 27.
* A 2-D tensor of shape [batch_size, num_units]. This is effectively
* the same as the current state value.
*/
RNN = 24,
@@ -1306,6 +1297,9 @@ enum OperationType : int32_t {
* exp((input[batch, i] - max(input[batch, :])) * beta) /
* sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
*
* For input tensor with rank other than 2, the activation will be applied
* independently on each 1-D slice along specified dimension.
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
@@ -1314,15 +1308,15 @@ enum OperationType : int32_t {
*
* 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}.
*
* Outputs:
* * 0: The output tensor of same shape as input0.
* For {@link OperandType::TENSOR_QUANT8_ASYMM},
* the scale must be 1.f / 256 and the zeroPoint must be 0.
*
* Available since API level 27.
*/
SOFTMAX = 25,
@@ -1344,7 +1338,8 @@ enum OperationType : int32_t {
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4, with "NHWC" data layout.
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Inputs:
* * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -1356,8 +1351,8 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The output 4-D tensor, of shape [batches, height/block_size,
* width/block_size, depth_in*block_size*block_size].
*
* Available since API level 27.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_DEPTH = 26,
@@ -1403,25 +1398,23 @@ enum OperationType : int32_t {
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
*
* All input tensors must be the same type.
*
* Inputs:
* * 0: input.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, input_size], where “batch_size” corresponds to the
* batching dimension, and “input_size” is the size of the input.
* A 2-D tensor of shape [batch_size, input_size], where “batch_size”
* corresponds to the batching dimension, and “input_size” is the size
* of the input.
* * 1: weights_feature.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, input_size], where “num_units” corresponds to the
* number of units.
* A 2-D tensor of shape [num_units, input_size], where “num_units”
* corresponds to the number of units.
* * 2: weights_time.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [num_units, memory_size], where “memory_size” corresponds to the
* fixed-size of the memory.
* A 2-D tensor of shape [num_units, memory_size], where “memory_size”
* corresponds to the fixed-size of the memory.
* * 3: bias.
* An optional 1-D tensor of {@link OperandType::TENSOR_FLOAT32},
* of shape [num_units].
* An optional 1-D tensor of shape [num_units].
* * 4: state (in).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, (memory_size - 1) * num_units * rank].
* A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
* * 5: rank.
* The rank of the SVD approximation.
* * 6: fused_activation_function.
@@ -1431,13 +1424,11 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: state (out).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, (memory_size - 1) * num_units * rank].
* * 1: output.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* A 2-D tensor of the same {@link OperandType} as the inputs, with shape
* [batch_size, num_units].
*
* Available since API level 27.
*/
SVDF = 27,
@@ -1458,8 +1449,6 @@ enum OperationType : int32_t {
*
* Outputs:
* * 0: The output tensor of same shape as input0.
*
* Available since API level 27.
*/
TANH = 28,

431
neuralnetworks/1.0/types.t Normal file
View File

@@ -0,0 +1,431 @@
%% template file for generating types.hal.
%% see frameworks/ml/nn/tools/api/README.md.
/*
* Copyright (C) 2017 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package android.hardware.neuralnetworks@1.0;
%insert Operand_1.0_Comment
enum OperandType : int32_t {
%insert Operand_1.0
/**
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* OEM specific scalar value.
*/
OEM = 10000,
/**
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
*/
TENSOR_OEM_BYTE = 10001,
};
%insert Operation_1.0_Comment
enum OperationType : int32_t {
%insert Operation_1.0
/**
* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
* OEM operation and data types.
*
* This operation is OEM specific. It should only be used for OEM
* applications.
*/
OEM_OPERATION = 10000,
};
/**
* Fused activation function types.
*/
enum FusedActivationFunc : int32_t {
NONE = 0,
RELU = 1,
RELU1 = 2,
RELU6 = 3,
};
/**
* How an operand is used.
*/
enum OperandLifeTime : int32_t {
/**
* The operand is internal to the model. It's created by an operation and
* consumed by other operations. It must be an output operand of
* exactly one operation.
*/
TEMPORARY_VARIABLE,
/**
* The operand is an input of the model. It must not be an output
* operand of any operation.
*
* An operand can't be both input and output of a model.
*/
MODEL_INPUT,
/**
* The operand is an output of the model. It must be an output
* operand of exactly one operation.
*
* An operand can't be both input and output of a model.
*/
MODEL_OUTPUT,
/**
* The operand is a constant found in Model.operandValues. It must
* not be an output operand of any operation.
*/
CONSTANT_COPY,
/**
* The operand is a constant that was specified via a Memory
* object. It must not be an output operand of any operation.
*/
CONSTANT_REFERENCE,
/**
* The operand does not have a value. This is valid only for optional
* arguments of operations.
*/
NO_VALUE,
};
/**
* Status of a device.
*/
enum DeviceStatus : int32_t {
AVAILABLE,
BUSY,
OFFLINE,
UNKNOWN,
};
/**
* Performance information for the reference workload.
*
* Used by a driver to report its performance characteristics.
*/
struct PerformanceInfo {
/**
* Ratio of the time taken by the driver to execute the
* workload compared to the time the CPU would take for the
* same workload. A lower number is better.
*/
float execTime;
/**
* Ratio of the energy used by the driver compared to what
* the CPU would use for doing the same workload. A lower number
* is better.
*/
float powerUsage;
};
/**
* The capabilities of a driver.
*/
struct Capabilities {
/**
* Driver performance when operating on float32 data.
*/
PerformanceInfo float32Performance;
/**
* Driver performance when operating on asymmetric 8-bit quantized data.
*/
PerformanceInfo quantized8Performance;
};
/**
* Describes the location of a data object.
*/
struct DataLocation {
/**
* The index of the memory pool where this location is found.
*/
uint32_t poolIndex;
/**
* Offset in bytes from the start of the pool.
*/
uint32_t offset;
/**
* The length of the data in bytes.
*/
uint32_t length;
};
/**
* Describes one operand of the model's graph.
*/
struct Operand {
/**
* Data type of the operand.
*/
OperandType type;
/**
* Dimensions of the operand.
*
* For a scalar operand, dimensions.size() must be 0.
*
* For a tensor operand, dimensions.size() must be at least 1;
* however, any of the dimensions may be unspecified.
*
* A tensor operand with all dimensions specified has "fully
* specified" dimensions. Whenever possible (i.e., whenever the
* dimensions are known at model construction time), a tensor
* operand should have (but is not required to have) fully
* specified dimensions, in order to enable the best possible
* performance.
*
* If a tensor operand's dimensions are not fully specified, the
* dimensions of the operand are deduced from the operand
* dimensions and values of the operation for which that operand
* is an output.
*
* In the following situations, a tensor operand's dimensions must
* be fully specified:
*
* . The operand has lifetime CONSTANT_COPY or
* CONSTANT_REFERENCE.
*
* . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully
* specified dimensions must either be present in the
* Operand or they must be provided in the corresponding
* RequestArgument.
* EXCEPTION: If the input or output is optional and omitted
* (by setting the hasNoValue field of the corresponding
* RequestArgument to true) then it need not have fully
* specified dimensions.
*
* A tensor operand with some number of unspecified dimensions is
* represented by setting each unspecified dimension to 0.
*/
vec<uint32_t> dimensions;
/**
* The number of times this operand appears as an operation input.
*
* (For example, if this operand appears once in one operation's
* input list, and three times in another operation's input list,
* then numberOfConsumers = 4.)
*/
uint32_t numberOfConsumers;
/**
* Quantized scale of the operand.
*
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
* TENSOR_INT32.
*/
float scale;
/**
* Quantized zero-point offset of the operand.
*
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
*/
int32_t zeroPoint;
/**
* How the operand is used.
*/
OperandLifeTime lifetime;
/**
* Where to find the data for this operand.
* If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
* NO_VALUE:
* - All the fields must be 0.
* If the lifetime is CONSTANT_COPY:
* - location.poolIndex is 0.
* - location.offset is the offset in bytes into Model.operandValues.
* - location.length is set.
* If the lifetime is CONSTANT_REFERENCE:
* - location.poolIndex is set.
* - location.offset is the offset in bytes into the specified pool.
* - location.length is set.
*/
DataLocation location;
};
/**
* Describes one operation of the model's graph.
*/
struct Operation {
/**
* The operation type.
*/
OperationType type;
/**
* Describes the table that contains the indexes of the inputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> inputs;
/**
* Describes the table that contains the indexes of the outputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> outputs;
};
/**
* A Neural Network Model.
*
* This includes not only the execution graph, but also constant data such as
* weights or scalars added at construction time. The only information that
* might not be known is the shape of the input tensors.
*/
struct Model {
/**
* All operands included in the model.
*/
vec<Operand> operands;
/**
* All operations included in the model.
*
* The operations are sorted into execution order. Every operand
* with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
* written before it is read.
*/
vec<Operation> operations;
/**
* Input indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> inputIndexes;
/**
* Output indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> outputIndexes;
/**
* A byte buffer containing operand data that were copied into the model.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_COPY.
*/
vec<uint8_t> operandValues;
/**
* A collection of shared memory pools containing operand values.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_REFERENCE.
*/
vec<memory> pools;
};
/**
* Metadata information specifying the location of the input or output data and
* any updates to the input or output operand.
*/
struct RequestArgument {
/**
* If true, the argument does not have a value. This can be used for
* operations that take optional arguments. If true, the fields of location
* are set to 0 and the dimensions vector is left empty.
*/
bool hasNoValue;
/**
* The location within one of the memory pools passed in the Request.
*/
DataLocation location;
/**
* Updated dimension information.
*
* If dimensions.size() > 0, dimension information was provided
* along with the argument. This can be the case for models that
* accept inputs of varying size. This can't change the rank, just
* the value of the dimensions that were unspecified in the
* model. If dimensions.size() > 0, then all dimensions must be
* specified here; and any dimension that was specified in the
* model must have the same value here.
*
* If the dimensions in the model are not fully specified, then
* they must be fully specified here, unless hasNoValue is set to
* true. If the dimensions in the model are fully specified, then
* either dimensions.size() may be 0, or the dimensions in the
* model must be identical to the dimensions here.
*/
vec<uint32_t> dimensions;
};
/**
* Inputs to be sent to and outputs to be retrieved from a prepared model.
*
* A Request serves two primary tasks:
* 1) Provides the input and output data to be used when executing the model.
* 2) Specifies any updates to the input operand metadata that were left
* unspecified at model preparation time.
*
* An output must not overlap with any other output, with an input, or
* with an operand of lifetime CONSTANT_REFERENCE.
*/
struct Request {
/**
* Input data and information to be used in the execution of a prepared
* model.
*
* The index of the input corresponds to the index in Model.inputIndexes.
* E.g., input[i] corresponds to Model.inputIndexes[i].
*/
vec<RequestArgument> inputs;
/**
* Output data and information to be used in the execution of a prepared
* model.
*
* The index of the output corresponds to the index in Model.outputIndexes.
* E.g., output[i] corresponds to Model.outputIndexes[i].
*/
vec<RequestArgument> outputs;
/**
* A collection of shared memory pools containing operand data for both the
* inputs and the outputs to a model.
*/
vec<memory> pools;
};
/**
* Return status of a function.
*/
enum ErrorStatus : int32_t {
NONE,
DEVICE_UNAVAILABLE,
GENERAL_FAILURE,
OUTPUT_INSUFFICIENT_SIZE,
INVALID_ARGUMENT,
};

47
neuralnetworks/1.1/types.hal
View File

@@ -26,7 +26,6 @@ import @1.0::PerformanceInfo;
* The type of an operation in a model.
*/
enum OperationType : @1.0::OperationType {
/**
* BatchToSpace for N-dimensional tensors.
*
@@ -41,7 +40,8 @@ enum OperationType : @1.0::OperationType {
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
*
* Supported tensor rank: 4
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Inputs:
* * 0: An n-D tensor, specifying the tensor to be reshaped
@@ -51,8 +51,8 @@ enum OperationType : @1.0::OperationType {
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
*
* Available since API level 28.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
BATCH_TO_SPACE_ND = 29,
@@ -91,8 +91,6 @@ enum OperationType : @1.0::OperationType {
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
*
* Available since API level 28.
*/
DIV = 30,
@@ -126,8 +124,8 @@ enum OperationType : @1.0::OperationType {
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
*
* Available since API level 28.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be same as input0.
*/
MEAN = 31,
@@ -138,7 +136,8 @@ enum OperationType : @1.0::OperationType {
*
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM} (the pad value is undefined)
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* (the pad value is undefined)
*
* Supported tensor rank: up to 4
*
@@ -160,11 +159,8 @@ enum OperationType : @1.0::OperationType {
* of the padding:
* output0.dimension[i] =
* padding[i, 0] + input0.dimension[i] + padding[i, 1]
*
* NOTE: The pad value for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
* is undefined.
*
* Available since API level 28.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
PAD = 32,
@@ -182,8 +178,10 @@ enum OperationType : @1.0::OperationType {
* Supported tensor {@link OperandType}:
* * {@link OperandType::TENSOR_FLOAT32}
* * {@link OperandType::TENSOR_QUANT8_ASYMM}
* (the pad value is undefined)
*
* Supported tensor rank: 4
* Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
* and Channels) data layout.
*
* Inputs:
* * 0: An n-D tensor, specifying the input.
@@ -201,8 +199,8 @@ enum OperationType : @1.0::OperationType {
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
*
* Available since API level 28.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
SPACE_TO_BATCH_ND = 33,
@@ -232,8 +230,8 @@ enum OperationType : @1.0::OperationType {
* * 0: A tensor of the same {@link OperandType} as input0. Contains the
* same data as input, but has one or more dimensions of size 1
* removed.
*
* Available since API level 28.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
SQUEEZE = 34,
@@ -278,8 +276,8 @@ enum OperationType : @1.0::OperationType {
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
* where k is the number of bits set in shrink_axis_mask.
*
* Available since API level 28.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
STRIDED_SLICE = 35,
@@ -318,8 +316,6 @@ enum OperationType : @1.0::OperationType {
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
*
* Available since API level 28.
*/
SUB = 36,
@@ -345,11 +341,10 @@ enum OperationType : @1.0::OperationType {
*
* Outputs:
* * 0: A tensor of the same {@link OperandType} as input0.
*
* Available since API level 28.
* For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
* the scale and zeroPoint must be the same as input0.
*/
TRANSPOSE = 37,
};
/**

158
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@@ -0,0 +1,158 @@
%% template file for generating types.hal.
%% see frameworks/ml/nn/tools/api/README.md.
/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package android.hardware.neuralnetworks@1.1;
import @1.0::Operand;
import @1.0::OperationType;
import @1.0::PerformanceInfo;
/**
* Operation types.
*
* The type of an operation in a model.
*/
enum OperationType : @1.0::OperationType {
%insert Operation_1.1
};
/**
* The capabilities of a driver.
*/
struct Capabilities {
/**
* Driver performance when operating on float32 data.
*/
PerformanceInfo float32Performance;
/**
* Driver performance when operating on asymmetric 8-bit quantized data.
*/
PerformanceInfo quantized8Performance;
/**
* Driver performance when operating on float32 data but performing
* calculations with range and/or precision as low as that of the IEEE
* 754 16-bit floating-point format.
*/
PerformanceInfo relaxedFloat32toFloat16Performance;
};
/**
* Describes one operation of the model's graph.
*/
struct Operation {
/**
* The operation type.
*/
OperationType type;
/**
* Describes the table that contains the indexes of the inputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> inputs;
/**
* Describes the table that contains the indexes of the outputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> outputs;
};
/**
* A Neural Network Model.
*
* This includes not only the execution graph, but also constant data such as
* weights or scalars added at construction time. The only information that
* may not be known is the shape of the input tensors.
*/
struct Model {
/**
* All operands included in the model.
*/
vec<Operand> operands;
/**
* All operations included in the model.
*
* The operations are sorted into execution order. Every operand
* with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
* written before it is read.
*/
vec<Operation> operations;
/**
* Input indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> inputIndexes;
/**
* Output indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> outputIndexes;
/**
* A byte buffer containing operand data that were copied into the model.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_COPY.
*/
vec<uint8_t> operandValues;
/**
* A collection of shared memory pools containing operand values.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_REFERENCE.
*/
vec<memory> pools;
/**
* 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
* precision as low as that of the IEEE 754 16-bit floating-point format.
* 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
* range and precision of the IEEE 754 32-bit floating-point format.
*/
bool relaxComputationFloat32toFloat16;
};
/**
* Execution preferences.
*/
enum ExecutionPreference : int32_t {
/**
* Prefer executing in a way that minimizes battery drain.
* This is desirable for compilations that will be executed often.
*/
LOW_POWER = 0,
/**
* Prefer returning a single answer as fast as possible, even if this causes
* more power consumption.
*/
FAST_SINGLE_ANSWER = 1,
/**
* Prefer maximizing the throughput of successive frames, for example when
* processing successive frames coming from the camera.
*/
SUSTAINED_SPEED = 2,
};

761
neuralnetworks/1.2/types.hal
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745
neuralnetworks/1.2/types.t Normal file
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@@ -0,0 +1,745 @@
%% template file for generating types.hal.
%% see frameworks/ml/nn/tools/api/README.md.
/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package android.hardware.neuralnetworks@1.2;
import @1.0::DataLocation;
import @1.0::ErrorStatus;
import @1.0::OperandLifeTime;
import @1.0::OperandType;
import @1.0::PerformanceInfo;
import @1.1::OperationType;
import android.hidl.safe_union@1.0::Monostate;
enum Constant : uint32_t {
/**
* The byte size of the cache token.
*/
BYTE_SIZE_OF_CACHE_TOKEN = 32,
/**
* The maximum number of files for each type of cache in compilation caching.
*/
MAX_NUMBER_OF_CACHE_FILES = 32,
};
enum OperandType : @1.0::OperandType {
%insert Operand_1.2
/*
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* OEM specific scalar value.
* OEM = 10000,
*/
/*
* DEPRECATED. Since HAL version 1.2, extensions are the preferred
* alternative to OEM operation and data types.
*
* A tensor of OEM specific values.
* TENSOR_OEM_BYTE = 10001,
*/
/* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF
* OperandTypeRange::FUNDAMENTAL_MAX.
*/
/* ADDING A NEW OEM TYPE REQUIRES UPDATING THE VALUE OF
* OperandTypeRange::OEM_MAX.
*/
};
/**
* The range of operand values in the OperandType enum.
*/
enum OperandTypeRange : uint32_t {
BASE_MIN = 0,
FUNDAMENTAL_MIN = 0,
%insert Operand_1.2_MAX
OEM_MIN = 10000,
OEM_MAX = 10001,
BASE_MAX = 0xFFFF,
};
/**
* Operation types.
*
* The type of an operation in a model.
*/
enum OperationType : int32_t {
%insert Operation_1.0
%insert Operation_1.1
%insert Operation_1.2
/**
* DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
* OEM operation and data types.
*
* This operation is OEM specific. It should only be used for OEM
* applications.
*/
OEM_OPERATION = @1.1::OperationType:OEM_OPERATION,
/* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
* OperationTypeRange::FUNDAMENTAL_MAX.
*/
/* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF
* OperationTypeRange::OEM_MAX.
*/
};
/**
* The range of values in the OperationType enum.
*/
enum OperationTypeRange : uint32_t {
BASE_MIN = 0,
FUNDAMENTAL_MIN = 0,
%insert Operation_1.2_MAX
OEM_MIN = 10000,
OEM_MAX = 10000,
BASE_MAX = 0xFFFF,
};
/**
* Device types.
*
* The type of NNAPI device.
*/
enum DeviceType : int32_t {
// Leaving 0 unused as it means unknown type in NDK NNAPI. There is no
// HAL equivalent of unknown type and a 1.2 HAL implementation must belong
// to one of the categories below.
/** The device does not fall into any category below. */
OTHER = 1,
/** The device runs NNAPI models on single or multi-core CPU. */
CPU = 2,
/** The device can run NNAPI models and also accelerate graphics APIs such
* as OpenGL ES and Vulkan. */
GPU = 3,
/** Dedicated accelerator for Machine Learning workloads. */
ACCELERATOR = 4,
};
/**
* The capabilities of a driver.
*
* Performance of an operation comes from the type of its first operand.
* This represents performance for non extension operand types.
*/
struct Capabilities {
/**
* Driver performance when operating on float32 data but performing
* calculations with range and/or precision as low as that of the IEEE
* 754 16-bit floating-point format.
*/
PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
/**
* Driver performance when operating on a particular data type.
* In the case of float32 data, this is used when the calculations
* are not relaxed.
*/
struct OperandPerformance {
OperandType type;
PerformanceInfo info;
};
/**
* Performance by operand type. Must be sorted by OperandType.
* If a particular OperandType is not present in operandPerformance,
* its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
*/
vec<OperandPerformance> operandPerformance;
};
/**
* Describes one operation of the model's graph.
*/
struct Operation {
/**
* The operation type.
*
* Besides the values listed in {@link OperationType}, any value above
* {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted
* as an extension type according to {@link Model::extensionNameToPrefix}.
*/
OperationType type;
/**
* Describes the table that contains the indexes of the inputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> inputs;
/**
* Describes the table that contains the indexes of the outputs of the
* operation. The offset is the index in the operandIndexes table.
*/
vec<uint32_t> outputs;
};
/**
* Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
*/
struct SymmPerChannelQuantParams {
/** Array of scaling values for each channel. Each value must be greater than zero. */
vec<float> scales;
/** Index of the channel dimension */
uint32_t channelDim;
};
/**
* Describes one operand of the model's graph.
*/
struct Operand {
/**
* The data type.
*
* Besides the values listed in {@link OperandType}, any value above
* {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
* as an extension type according to {@link Model::extensionNameToPrefix}.
*/
OperandType type;
/**
* Dimensions of the operand.
*
* For a scalar operand, dimensions.size() must be 0.
*
* A tensor operand with all dimensions specified has "fully
* specified" dimensions. Whenever possible (i.e., whenever the
* dimensions are known at model construction time), a tensor
* operand should have (but is not required to have) fully
* specified dimensions, in order to enable the best possible
* performance.
*
* If a tensor operand's dimensions are not fully specified, the
* dimensions of the operand are deduced from the operand
* dimensions and values of the operation for which that operand
* is an output.
*
* In the following situations, a tensor operand's dimensions must
* be fully specified:
*
* . The operand has lifetime CONSTANT_COPY or
* CONSTANT_REFERENCE.
*
* . The operand has lifetime MODEL_INPUT. Fully
* specified dimensions must either be present in the
* Operand or they must be provided in the corresponding
* RequestArgument.
* EXCEPTION: If the input is optional and omitted
* (by setting the hasNoValue field of the corresponding
* RequestArgument to true) then it need not have fully
* specified dimensions.
*
* A tensor operand with some number of unspecified dimensions is
* represented by setting each unspecified dimension to 0.
*
* A tensor operand with unspecified rank is represented by providing
* an empty dimensions vector.
*/
vec<uint32_t> dimensions;
/**
* The number of times this operand appears as an operation input.
*
* (For example, if this operand appears once in one operation's
* input list, and three times in another operation's input list,
* then numberOfConsumers = 4.)
*/
uint32_t numberOfConsumers;
/**
* Quantized scale of the operand.
*
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
* TENSOR_INT32.
*/
float scale;
/**
* Quantized zero-point offset of the operand.
*
* Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
*/
int32_t zeroPoint;
/**
* How the operand is used.
*/
OperandLifeTime lifetime;
/**
* Where to find the data for this operand.
* If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
* NO_VALUE:
* - All the fields must be 0.
* If the lifetime is CONSTANT_COPY:
* - location.poolIndex is 0.
* - location.offset is the offset in bytes into Model.operandValues.
* - location.length is set.
* If the lifetime is CONSTANT_REFERENCE:
* - location.poolIndex is set.
* - location.offset is the offset in bytes into the specified pool.
* - location.length is set.
*/
DataLocation location;
/**
* Additional parameters specific to a particular operand type.
*/
safe_union ExtraParams {
/**
* No additional parameters.
*/
Monostate none;
/**
* Symmetric per-channel quantization parameters.
*
* Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
*/
SymmPerChannelQuantParams channelQuant;
/**
* Extension operand parameters.
*
* The framework treats this as an opaque data blob.
* The format is up to individual extensions.
*/
vec<uint8_t> extension;
} extraParams;
};
/**
* A Neural Network Model.
*
* This includes not only the execution graph, but also constant data such as
* weights or scalars added at construction time. The only information that
* may not be known is the shape of the input tensors.
*/
struct Model {
/**
* All operands included in the model.
*/
vec<Operand> operands;
/**
* All operations included in the model.
*
* The operations are sorted into execution order. Every operand
* with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
* written before it is read.
*/
vec<Operation> operations;
/**
* Input indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> inputIndexes;
/**
* Output indexes of the model. There must be at least one.
*
* Each value corresponds to the index of the operand in "operands".
*/
vec<uint32_t> outputIndexes;
/**
* A byte buffer containing operand data that were copied into the model.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_COPY.
*/
vec<uint8_t> operandValues;
/**
* A collection of shared memory pools containing operand values.
*
* An operand's value must be located here if and only if Operand::lifetime
* equals OperandLifeTime::CONSTANT_REFERENCE.
*/
vec<memory> pools;
/**
* 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
* precision as low as that of the IEEE 754 16-bit floating-point format.
* 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
* range and precision of the IEEE 754 32-bit floating-point format.
*/
bool relaxComputationFloat32toFloat16;
/**
* The mapping between extension names and prefixes of operand and
* operation type values.
*
* An operand or operation whose numeric type value is above
* {@link OperandTypeRange::BASE_MAX} or
* {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
* as an extension operand. The low
* {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
* correspond to the type ID within the extension and the high
* {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
* the "prefix", which maps uniquely to the extension name.
*
* For example, if a model contains an operation whose value is
* 0xAAAABBBB and extensionNameToPrefix contains an entry with
* prefix=0xAAAA and name="vendor.test.test_extension", then
* the operation should be interpreted as the operation 0xBBBB
* of the extension named vendor.test.test_extension.
*
* This is a one-to-one correspondence. That is, there must be at most one
* prefix corresponding to each extension name and at most one extension
* name corresponding to each prefix.
*/
vec<ExtensionNameAndPrefix> extensionNameToPrefix;
/**
* A correspondence between an extension name and a prefix of operand and
* operation type values.
*/
struct ExtensionNameAndPrefix {
/**
* The extension name.
*
* See {@link Extension::name} for the format specification.
*/
string name;
/**
* The unique extension identifier within the model.
*
* See {@link Model::extensionNameToPrefix}.
*/
uint16_t prefix;
};
/**
* Numeric values of extension operand and operation types have the
* following structure:
* - 16 high bits represent the "prefix", which corresponds uniquely to the
* extension name.
* - 16 low bits represent the type ID within the extension.
*/
enum ExtensionTypeEncoding : uint8_t {
HIGH_BITS_PREFIX = 16,
LOW_BITS_TYPE = 16,
};
};
/**
* Describes the shape information of an output operand after execution.
*/
struct OutputShape {
/**
* Dimensions of the operand.
*/
vec<uint32_t> dimensions;
/**
* Whether the provided buffer size is sufficient for the output.
*/
bool isSufficient;
};
/**
* Specifies whether or not to measure timing information during execution.
*/
enum MeasureTiming : int32_t {
NO = 0,
YES = 1,
};
/**
* Timing information measured during execution. Each time is a duration from
* the beginning of some task to the end of that task, including time when that
* task is not active (for example, preempted by some other task, or
* waiting for some resource to become available).
*
* Times are measured in microseconds.
* When a time is not available, it must be reported as UINT64_MAX.
*/
struct Timing {
/** Execution time on device (not driver, which runs on host processor). */
uint64_t timeOnDevice;
/** Execution time in driver (including time on device). */
uint64_t timeInDriver;
};
/**
* FmqRequestDatum is a single element of a serialized representation of an
* execution request (a {@link @1.0::Request} object and a {@link MeasureTiming}
* value) which is sent across FastMessageQueue.
*
* The serialized representation for a particular execution is referred to later
* in these descriptions as a 'packet'.
*
* FastMessageQueue can only pass HIDL-defined types that do not involve nested
* buffers, handles, or interfaces.
*
* The request is serialized as follows:
* 1) 'packetInformation'
* 2) For each input operand:
* 2.1) 'inputOperandInformation'
* 2.2) For each dimension element of the operand:
* 2.2.1) 'inputOperandDimensionValue'
* 3) For each output operand:
* 3.1) 'outputOperandInformation'
* 3.2) For each dimension element of the operand:
* 3.2.1) 'outputOperandDimensionValue'
* 4) For each pool:
* 4.1) 'poolIdentifier'
* 5) 'measureTiming'
*/
safe_union FmqRequestDatum {
/**
* Type to describe the high-level layout of the packet.
*/
struct PacketInformation {
/**
* How many elements the packet contains, including the
* "packetInformation" datum.
*/
uint32_t packetSize;
/**
* Number of input operands.
*/
uint32_t numberOfInputOperands;
/**
* Number of output operands.
*/
uint32_t numberOfOutputOperands;
/**
* Number of pool identifiers.
*/
uint32_t numberOfPools;
};
/**
* Type representing the information for each operand.
*/
struct OperandInformation {
/**
* If true, the argument does not have a value. This can be used for
* operations that take optional arguments. If true, the fields of
* 'location' are set to 0, 'numberOfDimensions' is set to 0, and the
* dimensions information is omitted from the serialization.
*/
bool hasNoValue;
/**
* The location within one of the memory pools passed in the Request.
*/
DataLocation location;
/**
* Number of subsequent elements that belong to the dimensions vector.
*/
uint32_t numberOfDimensions;
};
/**
* packetInformation is the first element of the packet and describes the
* remainder of the packet.
*/
PacketInformation packetInformation;
/**
* Information for each input operand.
*/
OperandInformation inputOperandInformation;
/**
* Element of the dimensions vector.
*/
uint32_t inputOperandDimensionValue;
/**
* Information for each output operand.
*/
OperandInformation outputOperandInformation;
/**
* Element of the dimensions vector.
*/
uint32_t outputOperandDimensionValue;
/**
* Unique identifier for a pool.
*
* A {@link @1.0::Request} passes across one or more pools of shared memory
* for the inputs and outputs of an execution. However, these memory pools
* are not able to be sent across FastMessageQueue directly. Instead, the
* producing side of the FMQ represents each different pool with a unique
* identifier, and sends this identifier across the FMQ. Whenever the
* consuming side of the FMQ needs the memory corresponding to this unique
* identifier, it can pass the identifier to
* {@link IBurstCallback::getMemories} to retreive the memory. Although this
* HIDL Binder call is expensive compared to communication across FMQ, it is
* only needed in the cases when the consumer does not recognize the unique
* identifier.
*/
int32_t poolIdentifier;
/**
* Specifies whether or not to measure duration of the execution. The
* duration runs from the time the driver dequeues the request from a
* FastMessageQueue to the time the driver enqueues results to a
* FastMessageQueue.
*/
MeasureTiming measureTiming;
};
/**
* FmqResultDatum is a single element of a serialized representation of the
* values returned from an execution ({@link @1.0::ErrorStatus},
* vec<{@link OutputShape}>, and {@link Timing}) which is returned via
* FastMessageQueue.
*
* The serialized representation for a particular execution is referred to later
* in these descriptions as a 'packet'.
*
* FastMessageQueue can only pass HIDL-defined types that do not involve nested
* buffers, handles, or interfaces.
*
* The execution return values ({@link @1.0::ErrorStatus} and
* vec<{@link OutputShape}>) are serialized as follows:
* 1) 'packetInformation'
* 2) For each returned operand:
* 2.1) 'operandInformation'
* 2.2) For each dimension element of the operand:
* 2.2.1) 'operandDimensionValue'
* 3) 'executionTiming'
*/
safe_union FmqResultDatum {
/**
* Type to describe the high-level layout of the packet.
*/
struct PacketInformation {
/**
* How many elements the packet contains, including the
* "packetInformation" datum.
*/
uint32_t packetSize;
/**
* Status of the execution.
*/
ErrorStatus errorStatus;
/**
* Number of returned operands.
*/
uint32_t numberOfOperands;
};
/**
* Type representing the information for each operand.
*/
struct OperandInformation {
/**
* Indicates whether the operand's output buffer is large enough to
* store the operand's result data.
*/
bool isSufficient;
/**
* Number of subsequent elements that belong to the dimensions vector.
*/
uint32_t numberOfDimensions;
};
/**
* packetInformation is the first element of the packet and describes the
* remainder of the packet. It additionally includes the status of the
* execution.
*/
PacketInformation packetInformation;
/**
* Information for each returned operand.
*/
OperandInformation operandInformation;
/**
* Element of the dimensions vector.
*/
uint32_t operandDimensionValue;
/**
* Duration of execution. Unless measurement was requested and execution
* succeeds, all times must be reported as UINT64_MAX. A driver may choose
* to report any time as UINT64_MAX, indicating that measurement is not
* available.
*/
Timing executionTiming;
};
/**
* Information about an extension.
*/
struct Extension {
/**
* The extension name.
*
* The name must consist of lowercase latin letters, numbers, periods, and
* underscore signs. The name must contain at least one period.
*
* The name must start with the reverse domain name of the vendor.
*
* Example: com.google.test_extension
*/
string name;
/**
* Information about an extension operand type.
*/
struct OperandTypeInformation {
/**
* The extension operand type.
*/
uint16_t type;
/**
* Indicates whether the extension operand type represents a tensor or
* a scalar.
*/
bool isTensor;
/**
* The byte size of the operand (if scalar) or of a single element (if
* tensor).
*/
uint32_t byteSize;
};
/**
* Information about operand types defined by the extension.
*/
vec<OperandTypeInformation> operandTypes;
};