Evolution-x/hardware_interfaces
1
0
Fork 0
mirror of https://github.com/Evolution-X/hardware_interfaces synced 2026-02-01 05:49:27 +00:00
Code Issues Packages Projects Releases Wiki Activity

Fix LSTM documentation

Browse Source 
The documentation said that cell-to-input weights are required to be
present when input-to-input weights, recurrent-to-input weights and
input gate bias are present. This was incorrect since this weights can
be omitted if peephole connections are not used even if all the other
tensors are present.

Another bug that is fixed in this change is that for output #0 the docs
said "of shape [batch_size, num_units * 4] with CIFG, or [batch_size,
num_units * 3] without CIFG" when in fact it is the opposite, i.e.  "of
shape [batch_size, num_units * 3] with CIFG, or [batch_size, num_units *
4] without CIFG."

Existing CTS/VTS tests expect behaviour described in the fixed documentation.
Existing CPU implementation is also compliant with the fixed documentation.

Fix: 111842951
Test: mma
Change-Id: Id011783e33672ae65dc6fe3784cb26feb832acf9
This commit is contained in:
Lev Proleev
2019-05-08 16:57:56 +01:00
parent 767a57fb19
commit e0537f09fb
3 changed files with 26 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
current.txt
View File

@@ -401,7 +401,7 @@ f7d7cb747dc01a9fdb2d39a80003b4d8df9be733d65f5842198802eb6209db69 android.hardwar
65a021fa89085b62fc96b2b6d3bef2f9103cf4d63379c68bc154fd9eef672852 android.hardware.health@1.0::types
b7ecf29927055ec422ec44bf776223f07d79ad9f92ccf9becf167e62c2607e7a android.hardware.keymaster@4.0::IKeymasterDevice
574e8f1499436fb4075894dcae0b36682427956ecb114f17f1fe22d116a83c6b android.hardware.neuralnetworks@1.0::IPreparedModel
e75759b40a1c5f97b463b30aab91954012c9ea9e454dde308db853a56796e5a6 android.hardware.neuralnetworks@1.0::types
1e3576c07006d82ba5bc6ddbf87c101414d361c41afe7a82713750844c488725 android.hardware.neuralnetworks@1.0::types
eb754b58c93e5591613208b4c972811288b0fa16a82430d602f107c91a908b22 android.hardware.neuralnetworks@1.1::types
1d4a5776614c08b5d794a5ec5ab04697260cbd4b3441d5935cd53ee71d19da02 android.hardware.radio@1.0::IRadioResponse
ed9da80ec0c96991fd03f0a46107815d0e50f764656e49dba4980fa5c31d5bc3 android.hardware.radio@1.0::types
@@ -515,7 +515,7 @@ b83317b66721241887d2770b5ae95fd5af1e77c5daa7530ecb08fae8892f2b43 android.hardwar
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel
e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
e3b6176e3bf235c4e0e4e451b0166e396c7ee176cfe167c9147c3d46d7b34f0c android.hardware.neuralnetworks@1.2::types
d18bba0b6c8d2d1da3cfb52b14f556d2e04eb91551d97ee60a3524cf993a3e0e 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

25
neuralnetworks/1.0/types.hal
View File

@@ -858,20 +858,21 @@ enum OperationType : int32_t {
* elements of the input matrices.
*
* The operation has the following independently optional inputs:
* * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
* (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
* have values or neither of them have values (i.e., all set to null). If
* they have values, the peephole optimization is used.
* * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
* (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate
* bias (\f$b_i\f$) either all have values, or none of them have values
* (i.e., all set to null). If they have no values, coupling of input and
* forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$)
* is calculated using the following equation instead.
* (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
* or none of them have values. If they have no values, coupling of input
* and forget gates (CIFG) is used, in which case the input gate
* (\f$i_t\f$) is calculated using the following equation instead.
* \f{eqnarray*}{
* i_t = 1 - f_t
* \f}
* * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights
* (\f$W_{co}\f$) either both have values or neither of them have values.
* If they have values, the peephole optimization is used. Additionally,
* if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
* required to have values for peephole optimization.
* In case peephole optimization is used and CIFG is not used
* cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
* cell-to-input weights must have no value.
* * The projection weights (\f$W_{proj}\f$) is required only for the
* recurrent projection layer, and should otherwise have no value.
* * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
@@ -984,8 +985,8 @@ enum OperationType : int32_t {
* Outputs:
* * 0: The scratch buffer.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units * 4] with CIFG, or
* [batch_size, num_units * 3] without CIFG.
* [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].

21
neuralnetworks/1.2/types.hal
View File

@@ -1177,20 +1177,21 @@ enum OperationType : int32_t {
* https://arxiv.org/pdf/1607.06450.pdf
*
* The operation has the following independently optional inputs:
* * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
* (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
* have values or neither of them have values (i.e., all set to null). If
* they have values, the peephole optimization is used.
* * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
* (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate
* bias (\f$b_i\f$) either all have values, or none of them have values
* (i.e., all set to null). If they have no values, coupling of input and
* forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$)
* is calculated using the following equation instead.
* (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
* or none of them have values. If they have no values, coupling of input
* and forget gates (CIFG) is used, in which case the input gate
* (\f$i_t\f$) is calculated using the following equation instead.
* \f{eqnarray*}{
* i_t = 1 - f_t
* \f}
* * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights
* (\f$W_{co}\f$) either both have values or neither of them have values.
* If they have values, the peephole optimization is used. Additionally,
* if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
* required to have values for peephole optimization.
* In case peephole optimization is used and CIFG is not used
* cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
* cell-to-input weights must have no value.
* * The projection weights (\f$W_{proj}\f$) is required only for the
* recurrent projection layer, and should otherwise have no value.
* * The projection bias (\f$b_{proj}\f$) may (but not required to) have a