MLBEDSW-7165: Update to TensorFlow 2.12

- Updated FlatBuffers files using TensorFlow 2.12.0 schema

- Added restriction for UnidirectionalSequenceLSTM to have 2D recurrent
  weights to handle that diagonal_recurrent_tensors attr is not
  currently supported.

Change-Id: I104fd1f52485b9b83d644772dbcdeea2d17585f0
Signed-off-by: William Isaksson <william.isaksson@arm.com>

4 files changed, 28 insertions, 3 deletions

diff --git a/README.md b/README.md
index 5e3c419..8b9f35e 100644
--- a/README.md
+++ b/README.md
@@ -45,7 +45,8 @@ The tool has limited functionality for compiling a
 (EXPERIMENTAL).
 
 ## TensorFlow Support
-* Vela 3.8.0 to current supports TensorFlow 2.11
+* Vela 3.9.0 to current supports TensorFlow 2.12
+* Vela 3.8.0 supports TensorFlow 2.11
 * Vela 3.6.0 to 3.7.0 supports TensorFlow 2.10
 * Vela 3.5.0 supports TensorFlow 2.9
 * Vela 3.4.0 supports TensorFlow 2.8
diff --git a/ethosu/vela/tflite/UnidirectionalSequenceLSTMOptions.py b/ethosu/vela/tflite/UnidirectionalSequenceLSTMOptions.py
index 16641a7..73d298d 100644
--- a/ethosu/vela/tflite/UnidirectionalSequenceLSTMOptions.py
+++ b/ethosu/vela/tflite/UnidirectionalSequenceLSTMOptions.py
@@ -63,7 +63,14 @@ class UnidirectionalSequenceLSTMOptions(object):
             return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
         return False
 
-def UnidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(5)
+    # UnidirectionalSequenceLSTMOptions
+    def DiagonalRecurrentTensors(self):
+        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
+        if o != 0:
+            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
+        return False
+
+def UnidirectionalSequenceLSTMOptionsStart(builder): builder.StartObject(6)
 def Start(builder):
     return UnidirectionalSequenceLSTMOptionsStart(builder)
 def UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
@@ -81,6 +88,9 @@ def AddTimeMajor(builder, timeMajor):
 def UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(4, asymmetricQuantizeInputs, 0)
 def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
     return UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)
+def UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors): builder.PrependBoolSlot(5, diagonalRecurrentTensors, 0)
+def AddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors):
+    return UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors)
 def UnidirectionalSequenceLSTMOptionsEnd(builder): return builder.EndObject()
 def End(builder):
     return UnidirectionalSequenceLSTMOptionsEnd(builder)
\ No newline at end of file
diff --git a/ethosu/vela/tflite_mapping.py b/ethosu/vela/tflite_mapping.py
index 83c55bb..dba30c4 100644
--- a/ethosu/vela/tflite_mapping.py
+++ b/ethosu/vela/tflite_mapping.py
@@ -716,7 +716,14 @@ builtin_operator_map = {
         Op.UnidirectionalSequenceLstm,
         OptionsSerializer(
             "UnidirectionalSequenceLSTMOptions",
-            ("asymmetric_quantize_inputs", "cell_clip", fused_act, "proj_clip", "time_major"),
+            (
+                "asymmetric_quantize_inputs",
+                "cell_clip",
+                "diagonal_recurrent_tensors",
+                fused_act,
+                "proj_clip",
+                "time_major",
+            ),
         ),
         TFLITE_IFM_WEIGHTS_INDICES,
     ),
diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py
index 7d54400..723c5f2 100644
--- a/ethosu/vela/tflite_supported_operators.py
+++ b/ethosu/vela/tflite_supported_operators.py
@@ -331,6 +331,7 @@ class TFLiteSupportedOperators:
         self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_lstm_no_projection)
         self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_lstm_no_normalisation)
         self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_lstm_weights)
+        self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_lstm_weight_dimensions)
 
         # Rsqrt specific checks
         self.specific_constraints[Op.Rsqrt].append(TFLiteSupportedOperators.constraint_rsqrt_input_int8)
@@ -967,6 +968,12 @@ class TFLiteSupportedOperators:
         return valid, "Op has missing weights"
 
     @staticmethod
+    def constraint_lstm_weight_dimensions(op):
+        "All recurrent weights must be 2D"
+        valid = all([len(input.shape) == 2 for input in op.inputs[5:9]])
+        return valid, "Op recurrent weights are not 2D"
+
+    @staticmethod
     def constraint_rsqrt_input_int8(op):
         "IFM must be int8"
         ifm_dtype = op.ifm.dtype