masahi commented on a change in pull request #7562:
URL: https://github.com/apache/tvm/pull/7562#discussion_r586915168
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File path: python/tvm/relay/frontend/tensorflow.py
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@@ -1166,6 +1166,125 @@ def _impl(inputs, attr, params, mod):
return _impl
+def _math_segment_sum():
+ def _impl(inputs, attr, params, mod):
+ assert len(inputs) == 2, "There should be 2 input tensors"
+ return get_relay_op("segment_sum")(inputs[0], inputs[1])
+
+ return _impl
+
+
+def _sparse_segment_sum():
+ def _impl(inputs, attr, params, mod):
+ assert len(inputs) == 3, "There should be 3 input tensors"
+ data = _op.take(inputs[0], inputs[1], axis=0)
+ return _op.segment_sum(data, inputs[2])
+
Review comment:
By "fused" I meant we shouldn't materialize the result of `take`, which
can be huge. In a fused implementation, we need to look up indices and
accumulate the sum on the fly. This is why PT has `EmbeddingBag` op, see their
doc https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html.
Yes, a complicated op like this will not likely be feasible if we rely only
on Relay-level op fusion. We need a dedicated `sparse_segment_sum` TOPI/Relay
op.
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