srinidhigoud commented on a change in pull request #7901:
URL: https://github.com/apache/tvm/pull/7901#discussion_r617979127
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File path: python/tvm/relay/frontend/tensorflow.py
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@@ -1765,6 +1765,44 @@ def _impl(inputs, attr, params, mod):
return _impl
+def _broadcast_args():
+ def _impl(inputs, attr, params, mod):
+ if isinstance(inputs[0], _expr.Var):
+ s0 = params[inputs[0].name_hint]
+ else:
+ s0 = _infer_value(inputs[0], params, mod)
+ if isinstance(inputs[1], _expr.Var):
+ s1 = params[inputs[1].name_hint]
+ else:
+ s1 = _infer_value(inputs[1], params, mod)
+ s0 = list(s0.asnumpy().reshape([-1]))
+ s1 = list(s1.asnumpy().reshape([-1]))
+ s0_size, s1_size = len(s0), len(s1)
+ from collections import deque
+
+ out = deque([])
+ for i in range(1, min(s0_size, s1_size) + 1):
+ if s0[s0_size - i] == s1[s1_size - i]:
+ out.appendleft(s0[s0_size - i])
+ elif s0[s0_size - i] == 1:
+ out.appendleft(s1[s1_size - i])
+ else:
+ assert s1[s1_size - i] == 1, "Incompatible broadcast type %s
and %s" % (
+ s0[s0_size - i],
+ s1[s1_size - i],
+ )
+ out.appendleft(s0[s0_size - i])
+ if s0_size < s1_size:
+ for i in range(s0_size + 1, s1_size + 1):
+ out.appendleft(s1[s1_size - i])
+ if s1_size < s0_size:
+ for i in range(s1_size + 1, s0_size + 1):
+ out.appendleft(s0[s0_size - i])
Review comment:
This will require atleast two list inversions (both the inputs as we
have to iterate in reverse to verify broadcasting rules). Fill values will only
make sense if I use a zip. It is only two loops (one of last two will fail) and
merging them to one will give marginal optimization.
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