KJlaccHoeUM9l commented on code in PR #13865:
URL: https://github.com/apache/tvm/pull/13865#discussion_r1090424116
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python/tvm/relay/frontend/onnx.py:
##########
@@ -6148,13 +6148,35 @@ def _impl_v11(cls, inputs, attr, params):
return _expr.Tuple(inputs)
-class SequenceLength(OnnxOpConverter):
- """Operator converter for sequence length op."""
+class SequenceErase(OnnxOpConverter):
+ """Operator converter for sequence erase op."""
@classmethod
def _impl_v11(cls, inputs, attr, params):
- # Get length of input sequence
- return _expr.const(len(inputs[0]), dtype="int64")
+ # Erase tensor from sequence on specified position
+ input_sequence = inputs[0]
+
+ if len(inputs) == 2:
+ position = inputs[1]
+ # Non constant position is not supported.
+ if isinstance(position, _expr.Constant):
+ position = position.data.numpy()
+ elif position.name_hint in params:
+ position = params[position.name_hint].numpy()
+ else:
+ raise NotImplementedError("Position must be a constant.")
+ else:
+ position = -1
+
+ if position < 0:
+ position = len(input_sequence) + position + 1
+ # Convert sequence to a list, insert tensors before erased, and
repackage as Tuple.
+ tensor_list = [input_sequence[i] for i in range(position)]
Review Comment:
Looks like it could be simplified like this:
```Python
tensor_list = [input_sequence[i] for i in range(len(input_sequence)) if i !=
position]
```
This `if` will not affect performance in any way, because everything will be
done at compile time. However, this code looks more readable.
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