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[TF frontend][bugfix]Avoid making a new node when already has span info #7789

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Apr 9, 2021
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[TF frontend][bugfix]Avoid making a new node when already has span info #7789

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Avoid making a new node when already has span info
Apr 2, 2021
97b4eb6
add test
Apr 6, 2021
60562a8
add test
Apr 6, 2021
3b1cd8a
add test
Apr 6, 2021
51f59e7
fix
Apr 6, 2021
0cf3579
fix
Apr 6, 2021
6040348
move test to test_forward.py
Apr 8, 2021
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fix
Apr 8, 2021
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fix
Apr 8, 2021
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4 changes: 2 additions & 2 deletions python/tvm/relay/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3851,11 +3851,11 @@ def _convert_operator(
@staticmethod
def _set_span(sym, node_name):
span = tvm.relay.Span(tvm.relay.SourceName(node_name), 0, 0, 0, 0)
if isinstance(sym, _expr.Call):
if isinstance(sym, _expr.Call) and sym.span is None:
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could you add a test case for these two lines?

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I'd love to, but I don't know how to create unitest for pb parsering flow, any suggestion? thanks

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Does this require pb parsing or directly creating a tf program would be sufficient?

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Does this require pb parsing or directly creating a tf program would be sufficient?

You are right，where can I find an exmple? I know the basic idea is creatig a tf graph, using from_tensorflow to handle it, then check the output graph ir.

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I think we can refer to the existing unit/integration tests for tf frontend. Most of them just create a tf graph.

sym = _expr.Call(sym.op, sym.args, sym.attrs, sym.type_args, span)
elif isinstance(sym, _expr.TupleWrapper):
tuple_value = sym.tuple_value
if isinstance(tuple_value, _expr.Call):
if isinstance(tuple_value, _expr.Call) and tuple_value.span is None:
tuple_value = _expr.Call(
tuple_value.op, tuple_value.args, tuple_value.attrs, tuple_value.type_args, span
)
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39 changes: 39 additions & 0 deletions tests/python/frontend/tensorflow/test_forward.py
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Expand Up @@ -5451,5 +5451,44 @@ def test_forward_unique_with_counts():
_test_unique_with_counts(20, dtype, is_dyn)


#######################################################################
# check graph ir for nn.moments
# ------------

SEMVER = '#[version = "0.0.5"]\n'


def run_from_tensorflow(graph):
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mod, _ = from_tensorflow(graph.as_graph_def(add_shapes=True))
return mod


def test_moments():
g = tf.Graph()
shape = [4, 176, 8, 8]
dtype = "float32"
with g.as_default():
A = tf.placeholder(shape=shape, dtype=dtype, name="A")
B = tf.placeholder(shape=shape, dtype=dtype, name="B")
mean, variance = tf.nn.moments(A, [1], keep_dims=True)
normalised_input = (A - mean) / tf.sqrt(variance + 0.0005)

mod = run_from_tensorflow(g)
program = """
def @main(%A: Tensor[(4, 176, 8, 8), float32]) {
%527 = mean(%A, axis=[1], keepdims=True) /* moments/mean */;
%528 = subtract(%A, %527) /* sub */;
%529 = subtract(%A, %527);
%530 = multiply(%529, %529) /* moments/SquaredDifference */;
%531 = mean(%530, axis=[1], keepdims=True) /* moments/variance */;
%532 = add(%531, 0.0005f) /* add */;
%533 = sqrt(%532) /* Sqrt */;
divide(%528, %533) /* truediv */
}
"""
mod_golden = tvm.parser.parse(SEMVER + program)
tvm.ir.assert_structural_equal(mod["main"].body, mod_golden["main"].body, map_free_vars=True)


if __name__ == "__main__":
pytest.main([__file__])