AndrewZhaoLuo commented on a change in pull request #9329:
URL: https://github.com/apache/tvm/pull/9329#discussion_r734250090
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File path: python/tvm/relay/frontend/onnx.py
##########
@@ -4226,6 +4226,35 @@ def _impl_v1(cls, inputs, attr, params):
return _expr.TupleWrapper(_expr.Tuple(result), len(result))
+class Trilu(OnnxOpConverter):
+ """Operator converter for Trilu"""
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
Review comment:
this should be opset14 so _impl_v14
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -4226,6 +4226,35 @@ def _impl_v1(cls, inputs, attr, params):
return _expr.TupleWrapper(_expr.Tuple(result), len(result))
+class Trilu(OnnxOpConverter):
+ """Operator converter for Trilu"""
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ upper = attr.get("upper")
Review comment:
suggest attr.get("upper", 1) since this appears to be the default
##########
File path: include/tvm/topi/transform.h
##########
@@ -1776,12 +1775,10 @@ inline Tensor matrix_set_diag(const Tensor& input,
const Tensor& diagonal, int k
for (size_t i = 0; i < ndim - 1; i++) {
diagonal_indices.push_back(iter_vars[i]);
}
- if (only_one_diagonal) {
- k = k1;
- } else {
+ auto multi_diagonals = [&]() {
// Determining which diagonal/sub-diagonal/super-diagonal it is
k = iter_vars[ndim] - iter_vars[ndim - 1];
- diagonal_indices.push_back(k2 - k);
+ diagonal_indices.push_back(k2(0) - k);
Review comment:
I believe (though please check) that you can just do k2() for 0-D tensor
access.
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