ehsanmok commented on code in PR #13074:
URL: https://github.com/apache/tvm/pull/13074#discussion_r996186639
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python/tvm/relay/frontend/onnx.py:
##########
@@ -944,6 +946,36 @@ def _impl_v1(cls, inputs, attr, params):
return Gelu._impl_v1([inp], attr, params)
+class LayerNormalization(OnnxOpConverter):
+ """Operator converter for LayerNormalization from Microsoft onnxruntime
contrib opset."""
+
+ @classmethod
+ def _impl_v17(cls, inputs, attr, params):
+ x = inputs[0]
+ gamma = inputs[1]
+ beta = inputs[2]
+ axis = attr.get("axis", -1)
+ eps = attr.get("epsilon", 1e-5)
+ # according to the onnx doc, given the int axis (default -1)
+ # to compute the mean and inv_stdev which are of dim [d[0], ...,
d[axis-1], 1, ..., 1]
+ # the actual computation is over (axis, ..., rank(x) - 1) axes
+ # see
https://github.com/onnx/onnx/blob/main/docs/Changelog.md#layernormalization-17
+ rank = len(infer_shape(x))
+ axis = tuple(range(axis, rank)) if axis >= 0 else tuple(range(rank +
axis, rank))
+ dtype = infer_type(x).checked_type.dtype
+ mean = _op.mean(x, axis, keepdims=True)
Review Comment:
Isn't that (the point of rely) to optimize using simplification passes that
rely does so we don't need to worry too much about it?
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