SebastianBoblestETAS commented on code in PR #11183:
URL: https://github.com/apache/tvm/pull/11183#discussion_r867767234
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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"
+
+ # Extract input tensor from saved model
+ input_tensor = input_tensors[0]
+
+ # Extract tensors from input tensors from saved model
+ # Input weights
+ input_input_weights = input_tensors[1]
+ input_forget_weights = input_tensors[2]
+ input_cell_weights = input_tensors[3]
+ input_output_weights = input_tensors[4]
+ # Recurrent weights
+ recurrent_input_weights = input_tensors[5]
+ recurrent_forget_weights = input_tensors[6]
+ recurrent_cell_weights = input_tensors[7]
+ recurrent_output_weights = input_tensors[8]
+ # Bias weights
+ input_gate_bias = input_tensors[12]
+ forget_gate_bias = input_tensors[13]
+ cell_gate_bias = input_tensors[14]
+ output_gate_bias = input_tensors[15]
+ # State input
+ output_state_in = input_tensors[18]
+ cell_state_in = input_tensors[19]
+
+ # Extract output tensor from saved model
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "output tensors length should be 1"
+ X_steps = self.unbind(input_tensor, axis=1)
+ weights_dict = {}
+
+ # hidden_state_weights is equivalent to output_state_in in tflite model
+ out_state_in_shape = tuple(self.get_tensor_shape(output_state_in))
+ out_state_in_dtype =
self.get_tensor_type_str(output_state_in.tensor.Type())
+ out_state_in_expr = _op.zeros(out_state_in_shape,
dtype=out_state_in_dtype)
+ weights_dict["hidden_state"] = _op.split(out_state_in_expr, 1)[0]
Review Comment:
A relay LSTM operator might be valuable. But it should then support all
frontends, right? For the moment we decided to use the existing lstm_cell that
is also used by the onnx frontend.
If we have a native LSTM operator in relay later, we can adapt the parsers
to use that instead.
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