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Fix onnx round import with float64 inputs. #11685

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Merged
AndrewZhaoLuo merged 2 commits into from
Jun 13, 2022
Merged

Fix onnx round import with float64 inputs. #11685

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2ba27f9
Fix onnx round import with float64 inputs.
lazycal Jun 12, 2022
5d3ecef
Fix lint and optimize dtype mapping.
lazycal Jun 12, 2022
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7 changes: 4 additions & 3 deletions python/tvm/relay/frontend/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5095,9 +5095,10 @@ def _impl_v11(cls, inputs, attr, params):
# Onnx round uses Banker's rounding which rounds .5 to the nearest even integer

x = inputs[0]
half = _expr.const(0.5, dtype="float32")
one = _expr.const(1, dtype="float32")
two = _expr.const(2, dtype="float32")
dtype = infer_type(x).checked_type.dtype
half = _expr.const(0.5, dtype=dtype)
one = _expr.const(1, dtype=dtype)
two = _expr.const(2, dtype=dtype)

rounded = _op.ceil(x - half)
bankers_mask = one - (_op.ceil(x + half) - _op.floor(x + half))
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29 changes: 23 additions & 6 deletions tests/python/frontend/onnx/test_forward.py
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Expand Up @@ -966,24 +966,38 @@ def add_noop_to_input_attr(attr_name, attr):


def _test_onnx_op_elementwise(
target, dev, inshape, outfunc, npargs, dtype, opname, kwargs, opset=None
target, dev, inshape, outfunc, npargs, dtype, opname, kwargs, opset=None, verify=True
):
indata = np.random.uniform(-1, 1, size=inshape).astype(dtype)
outdata = outfunc(indata, **npargs)

y = helper.make_node(opname, ["in"], ["out"], **kwargs)

ONNX_DTYPE = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]

graph = helper.make_graph(
[y],
opname + "_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(indata.shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(outdata.shape))],
inputs=[helper.make_tensor_value_info("in", ONNX_DTYPE, list(indata.shape))],
outputs=[helper.make_tensor_value_info("out", ONNX_DTYPE, list(outdata.shape))],
)

model = helper.make_model(graph, producer_name=opname + "_test")
verify_with_ort_with_inputs(
model, [indata], [outdata.shape], opset=opset, dtype=dtype, target=target, dev=dev
)
if verify:
verify_with_ort_with_inputs(
model, [indata], [outdata.shape], opset=opset, dtype=dtype, target=target, dev=dev
)
else:
get_tvm_output(
model,
[indata],
target,
dev,
[outdata.shape],
dtype,
opset=opset,
opt_level=3,
)


@tvm.testing.parametrize_targets
Expand Down Expand Up @@ -1058,6 +1072,9 @@ def test_clip_min_max_as_inputs(target, dev):
@tvm.testing.parametrize_targets
def test_round(target, dev):
_test_onnx_op_elementwise(target, dev, (2, 4, 5, 6), np.round, {}, "float32", "Round", {})
_test_onnx_op_elementwise(
target, dev, (2, 4, 5, 6), np.round, {}, "float64", "Round", {}, verify=False
) # TODO: enable verification once ORT supports float64


def _test_finite_ops(target, dev, inshape, outfunc, npargs, dtype, opname, kwargs):
Expand Down