AndrewZhaoLuo commented on a change in pull request #9911:
URL: https://github.com/apache/tvm/pull/9911#discussion_r785194825
##########
File path: python/tvm/relay/frontend/onnx.py
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@@ -268,6 +256,40 @@ def flatten_to_nd(x, x_shape, nd=3):
],
0,
)
+
+ b_type = infer_type(inputs[1])
+ # Convert to dense if the second matrix is 2d and non-dynamic
+ if b_rank == 2 and not _ty.is_dynamic(b_type.checked_type):
+ a = flatten_to_nd(inputs[0], a_shape, 2)
+ b = _op.transpose(inputs[1])
+ output = _op.nn.dense(a, b, out_dtype=out_dtype)
+ else:
+ # broadcast a and b
+ a_broadcasted_shape = _op.concatenate(
+ [
+ out_batch,
+ _op.strided_slice(
+ a_shape, [infer_shape(a_shape)[0] - 2],
[infer_shape(a_shape)[0]]
+ ),
+ ],
+ 0,
+ )
+ b_broadcasted_shape = _op.concatenate(
+ [
+ out_batch,
+ _op.strided_slice(
+ b_shape, [infer_shape(b_shape)[0] - 2],
[infer_shape(b_shape)[0]]
Review comment:
same here, use b_rank
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