vdkhoi commented on code in PR #11183:
URL: https://github.com/apache/tvm/pull/11183#discussion_r867490976
##########
python/tvm/relay/frontend/tflite.py:
##########
@@ -2710,6 +2743,145 @@ def convert_unpack(self, op):
return squeezed
+ def convert_unidirectional_sequence_lstm(self, op):
+ """Long Short Term Memory for TFLite implementation."""
+ if self.is_quantized(op):
+ raise tvm.error.OpNotImplemented(
+ "TFlite quantized UNIDIRECTIONALSEQUENCELSTM operator is not
supported yet."
+ )
+
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) >= 20, "input tensors length should be >= 20"
Review Comment:
As I mentioned above, from our understanding about tflite format, the number
of vectors which can be stored in flatbuffer designed by tflite is 20 tensors.
If you generate a tflite model without explicitly specifying other weights
except input tensor, the ignored weights will be assigned by default values (0)
and store in model file as inputs. It is obvious that we just support some
recent tflite versions. If you can find some models which support different
numbers of inputs and the references from tflite documentation to instruct how
to process it, we deeply thank you and extend out support for that kind of
model.
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