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[Relax][Frontend][ONNX] Add support for Pad mode="wrap" for opset 19 #19827

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[Relax][Frontend][ONNX] Add support for Pad mode="wrap" for opset 19 #19827

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57 changes: 57 additions & 0 deletions python/tvm/relax/frontend/onnx/onnx_frontend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2753,6 +2753,63 @@ def _impl_v11(cls, bb, inputs, attr, params):
# edge mode - replicate border values
return bb.emit_te(topi.nn.replicate_pad, inputs[0], pad_before, pad_after)

@classmethod
def _impl_v19(cls, bb, inputs, attr, params):
pads = get_constant(inputs[1], params)
constant_value = get_constant(inputs[2], params)
if constant_value is not None:
constant_value = constant_value.data.numpy().item()
else:
constant_value = 0.0

if isinstance(pads, relax.Constant):
pad_before, pad_after = _np.split(pads.data.numpy(), 2)
pad_before = _np.ndarray.tolist(pad_before)
pad_after = _np.ndarray.tolist(pad_after)
else:
raise ValueError("Dynamic pads are not supported yet.")

axes_input = inputs[3] if len(inputs) > 3 else None
if axes_input is not None:
axes_const = get_constant(axes_input, params)
if not isinstance(axes_const, relax.Constant):
raise ValueError("Dynamic axes are not supported for Pad yet.")

axes = axes_const.data.numpy().tolist()
if len(pad_before) != len(axes):
raise ValueError(
f"Pad expects pads length 2 * len(axes), got "
f"{len(pad_before) + len(pad_after)} pads and {len(axes)} axes."
)

rank = _get_known_tensor_rank(inputs[0])
if rank is None:
raise ValueError("Pad with axes requires a statically known input rank.")

axes = _normalize_constant_axes([int(a) for a in axes], rank, "Pad")
full_before = [0] * rank
full_after = [0] * rank
for i, ax in enumerate(axes):
full_before[ax] = pad_before[i]
full_after[ax] = pad_after[i]
pad_before, pad_after = full_before, full_after

pad_mode = attr.get("mode", b"constant").decode("utf-8")
if pad_mode not in ["constant", "edge", "reflect", "wrap"]:
raise tvm.error.OpAttributeInvalid(
"Value " + pad_mode + ' in attribute "mode" is invalid for operator Pad.'
)

if pad_mode == "constant":
return bb.emit_te(topi.nn.pad, inputs[0], pad_before, pad_after, constant_value)
elif pad_mode == "reflect":
return bb.emit_te(topi.nn.mirror_pad, inputs[0], pad_before, pad_after, "REFLECT")
elif pad_mode == "wrap":
return bb.emit_te(topi.nn.circular_pad, inputs[0], pad_before, pad_after)
else:
# edge mode - replicate border values
return bb.emit_te(topi.nn.replicate_pad, inputs[0], pad_before, pad_after)

Comment on lines +2756 to +2812

@gemini-code-assist gemini-code-assist Bot Jun 18, 2026

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medium

The implementation of _impl_v19 is almost identical to _impl_v11, introducing significant code duplication. Since the only difference is the support for mode="wrap", we can simplify _impl_v19 by handling the "wrap" mode directly and delegating all other modes to _impl_v11. This reduces duplication and improves maintainability.

    @classmethod
    def _impl_v19(cls, bb, inputs, attr, params):
        pad_mode = attr.get("mode", b"constant").decode("utf-8")
        if pad_mode == "wrap":
            pads = get_constant(inputs[1], params)
            if isinstance(pads, relax.Constant):
                pad_before, pad_after = _np.split(pads.data.numpy(), 2)
                pad_before = _np.ndarray.tolist(pad_before)
                pad_after = _np.ndarray.tolist(pad_after)
            else:
                raise ValueError("Dynamic pads are not supported yet.")
            return bb.emit_te(topi.nn.circular_pad, inputs[0], pad_before, pad_after)

        return cls._impl_v11(bb, inputs, attr, params)


class Tile(OnnxOpConverter):
"""Converts an onnx Tile node into an equivalent Relax expression."""
Expand Down
119 changes: 103 additions & 16 deletions tests/python/relax/test_frontend_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6277,9 +6277,18 @@ def main(
)


def _make_pad_expected_ir(input_shape, pads, mode="constant", value=0.0, opset=14):
def _make_pad_expected_ir(input_shape, pads, mode="constant", value=0.0, opset=14, axes=None):
len_dim = len(pads) // 2
np_pads = [(pads[i], pads[i + len_dim]) for i in range(len_dim)]

if axes is not None:
rank = len(input_shape)
full_pads = [(0, 0)] * rank
for i, axis in enumerate(axes):
axis = axis if axis >= 0 else axis + rank
full_pads[axis] = np_pads[i]
np_pads = full_pads

if mode == "constant":
out_shape = np.pad(
np.empty(input_shape, dtype=np.float32),
Expand All @@ -6294,6 +6303,7 @@ def _make_pad_expected_ir(input_shape, pads, mode="constant", value=0.0, opset=1
input_shape = tuple(input_shape)
out_shape = tuple(out_shape)
pads_shape = (len(pads),)
axes_shape = None if axes is None else (len(axes),)

if mode == "constant" and opset >= 11:

Expand Down Expand Up @@ -6455,6 +6465,60 @@ def main(input: R.Tensor(input_shape, dtype="float32")) -> R.Tensor(

return ExpectedPadEdgeAttrs

if mode == "wrap" and opset >= 19:
if axes is None:

@I.ir_module
class ExpectedPadWrapWithInputs:
@T.prim_func(private=True, s_tir=True)
def circular_pad(input: T.handle, CircularPadInput: T.handle):
T.evaluate(0)

@R.function
def main(
input: R.Tensor(input_shape, dtype="float32"),
pads: R.Tensor(pads_shape, dtype="int64"),
) -> R.Tensor(out_shape, dtype="float32"):
R.func_attr({"num_input": 1})
cls = ExpectedPadWrapWithInputs
with R.dataflow():
lv = R.call_tir(
cls.circular_pad,
(input,),
out_ty=R.Tensor(out_shape, dtype="float32"),
)
gv: R.Tensor(out_shape, dtype="float32") = lv
R.output(gv)
return gv

return ExpectedPadWrapWithInputs

@I.ir_module
class ExpectedPadWrapWithAxes:
@T.prim_func(private=True, s_tir=True)
def circular_pad(input: T.handle, CircularPadInput: T.handle):
T.evaluate(0)

@R.function
def main(
input: R.Tensor(input_shape, dtype="float32"),
pads: R.Tensor(pads_shape, dtype="int64"),
axes: R.Tensor(axes_shape, dtype="int64"),
) -> R.Tensor(out_shape, dtype="float32"):
R.func_attr({"num_input": 1})
cls = ExpectedPadWrapWithAxes
with R.dataflow():
lv = R.call_tir(
cls.circular_pad,
(input,),
out_ty=R.Tensor(out_shape, dtype="float32"),
)
gv: R.Tensor(out_shape, dtype="float32") = lv
R.output(gv)
return gv

return ExpectedPadWrapWithAxes

raise AssertionError(f"No Pad expected IR for mode={mode}, opset={opset}")


Expand All @@ -6463,21 +6527,39 @@ def test_pad(dynamic):
if dynamic:
pytest.skip("Dynamic pad not supported")

def verify_pad(input_shape, pads, expected, mode="constant", value=0.0):
def verify_pad(input_shape, pads, expected, mode="constant", value=0.0, opset=14, axes=None):
len_dim = len(pads) // 2
np_pads = [(pads[i], pads[i + len_dim]) for i in range(len_dim)]
pads = np.array(pads)

if axes is not None:
rank = len(input_shape)
full_pads = [(0, 0)] * rank
for i, axis in enumerate(axes):
axis = axis if axis >= 0 else axis + rank
full_pads[axis] = np_pads[i]
np_pads = full_pads

pads = np.array(pads, dtype=np.int64)
# onnx graph
if mode in ["edge", "reflect"]:
if mode in ["edge", "reflect", "wrap"]:
outdata = np.pad(np.empty(input_shape, dtype=np.float32), pad_width=np_pads, mode=mode)
node = helper.make_node("Pad", inputs=["input", "pads"], outputs=["output"], mode=mode)

node_inputs = ["input", "pads"]
initializer = [helper.make_tensor("pads", TensorProto.INT64, (len(pads),), pads)]

if axes is not None:
axes = np.array(axes, dtype=np.int64)
node_inputs = ["input", "pads", "", "axes"]
initializer.append(helper.make_tensor("axes", TensorProto.INT64, (len(axes),), axes))

node = helper.make_node("Pad", inputs=node_inputs, outputs=["output"], mode=mode)
graph = helper.make_graph(
[node],
"pad_test",
inputs=[
helper.make_tensor_value_info("input", TensorProto.FLOAT, list(input_shape))
],
initializer=[helper.make_tensor("pads", TensorProto.INT64, (len(pads),), pads)],
initializer=initializer,
outputs=[
helper.make_tensor_value_info("output", TensorProto.FLOAT, list(outdata.shape))
],
Expand Down Expand Up @@ -6510,29 +6592,34 @@ def verify_pad(input_shape, pads, expected, mode="constant", value=0.0):
],
)
model = helper.make_model(graph, producer_name="pad_test")
model.opset_import[0].version = 14
tvm_model = from_onnx(model, opset=14, keep_params_in_input=True)
model.opset_import[0].version = opset
tvm_model = from_onnx(model, opset=opset, keep_params_in_input=True)
tvm_model["main"] = tvm_model["main"].without_attr("params")
expected = tvm.IRModule(expected.functions)
for gv in expected.get_global_vars():
if gv.name_hint != "main":
expected.update_func(gv, tvm_model[gv.name_hint])
tvm.ir.assert_structural_equal(tvm_model, expected)

for input_shape, pads, mode, value in [
((2, 2), [0, 1, 0, 0], "constant", 0.0),
((2, 3), [1, 0, 0, 1], "constant", 0.0),
((3, 2), [0, 0, 1, 0], "constant", 5.0),
((1, 3, 4, 5), [0, 1, 1, 1, 0, 0, 1, 1], "reflect", 0.0),
((2, 3), [1, 1, 1, 1], "edge", 0.0),
((1, 3, 4, 5), [0, 1, 1, 1, 0, 0, 1, 1], "edge", 0.0),
for input_shape, pads, mode, value, opset, axes in [
((2, 2), [0, 1, 0, 0], "constant", 0.0, 14, None),
((2, 3), [1, 0, 0, 1], "constant", 0.0, 14, None),
((3, 2), [0, 0, 1, 0], "constant", 5.0, 14, None),
((1, 3, 4, 5), [0, 1, 1, 1, 0, 0, 1, 1], "reflect", 0.0, 14, None),
((2, 3), [1, 1, 1, 1], "edge", 0.0, 14, None),
((1, 3, 4, 5), [0, 1, 1, 1, 0, 0, 1, 1], "edge", 0.0, 14, None),
((1, 3, 4), [0, 0, 2, 0, 0, 2], "wrap", 0.0, 19, None),
((1, 3, 4), [2, 2], "wrap", 0.0, 19, [2]),
((1, 3, 4), [1, 2, 1, 2], "wrap", 0.0, 19, [1, 2]),
]:
verify_pad(
input_shape,
pads,
_make_pad_expected_ir(input_shape, pads, mode=mode, value=value, opset=14),
_make_pad_expected_ir(input_shape, pads, mode=mode, value=value, opset=opset, axes=axes),
mode,
value,
opset,
axes,
)


Expand Down