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Relaxing type requirements for broadcast_like #17977

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Relaxing type requirements for broadcast_like #17977

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Relaxing type requirements for broadcast_like
tobecontinued Apr 5, 2020
feef46c
enhance unit test
tobecontinued Apr 6, 2020
505ce2f
Merge branch 'master' of https://github.com/apache/incubator-mxnet in…
tobecontinued Apr 8, 2020
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11 changes: 10 additions & 1 deletion src/operator/tensor/broadcast_reduce_op_value.cc
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Expand Up @@ -138,7 +138,16 @@ NNVM_REGISTER_OP(broadcast_like)
[](const NodeAttrs& attrs) {
return std::vector<std::string>{"lhs", "rhs"};
})
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<2, 1>)
.set_attr<nnvm::FInferType>("FInferType", [](const nnvm::NodeAttrs& attrs,
std::vector<int> *in_attrs,
std::vector<int> *out_attrs) {
CHECK_EQ(in_attrs->size(), 2) << " in operator " << attrs.name;
std::vector<int> checked_in_attrs = { (*in_attrs)[0] };
bool ret = !type_is_none((*in_attrs)[1]) &&
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Thanks for your contribution!

Is it necessary for the condition !type_is_none((*in_attrs)[1]) ?

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@tobecontinued tobecontinued Apr 6, 2020 •
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Well, I actully just found the issue, the code is copied from merged PR #14097, which had a similar issue. I think it is better to keep the same assumpation for all *_like op need to relax type requirment.

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Yes, it is necessary - what FInferType returns is whether it succeeded in inferring all the types (so that if all operators return true we know that all types are inferred). That is why it is important to not lie and return true only if all types are really inferred (even if we do not actually do anything with the other type).

ElemwiseType<1, 1>(attrs, &checked_in_attrs, out_attrs);
(*in_attrs)[0] = checked_in_attrs[0];
return ret;
})
.set_attr<nnvm::FGradient>("FGradient",
[](const nnvm::ObjectPtr& n,
const std::vector<nnvm::NodeEntry>& ograds) {
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10 changes: 10 additions & 0 deletions tests/python/unittest/test_operator.py
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Expand Up @@ -3119,6 +3119,16 @@ def test_reshape_like_different_types():
z = mx.nd.reshape_like(x, y)
assert_allclose(z.asnumpy(), [[0,0],[0,0],[0,0]])

@with_seed()
def test_broadcast_like_different_types():
x = mx.nd.zeros((2, 1))
y = mx.nd.ones((2, 2))

y = mx.nd.array(y).astype('int32')
z = mx.nd.broadcast_like(x, y)
assert_allclose(z.asnumpy(), [[0,0],[0,0]])
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assert x.dtype == z.dtype

@with_seed()
def test_flip():
for ndim in range(1, 6):
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