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[Relax] Add layout inference support for repeat operator #18579

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Merged
tlopex merged 1 commit into from
Dec 18, 2025
Merged

[Relax] Add layout inference support for repeat operator #18579

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60 changes: 59 additions & 1 deletion src/relax/op/tensor/manipulate.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1805,12 +1805,70 @@ StructInfo InferStructInfoRepeat(const Call& call, const BlockBuilder& ctx) {
return TensorStructInfo(ShapeExpr(shape_array), data_sinfo->dtype, data_sinfo->vdevice);
}

// TODO(relax-team): implement FRelaxInferLayout for repeat
InferLayoutOutput InferLayoutRepeat(
const Call& call, const ffi::Map<ffi::String, ffi::Array<ffi::String>>& desired_layouts,
const VarLayoutMap& var_layout_map) {
ICHECK(NoDesiredLayout(call, desired_layouts));

const auto* attrs = call->attrs.as<RepeatAttrs>();
ICHECK(attrs != nullptr) << "Invalid Call";
const auto* tensor_sinfo = GetStructInfoAs<TensorStructInfoNode>(call->args[0]);
ICHECK(tensor_sinfo != nullptr) << "Invalid Call";
ICHECK(!tensor_sinfo->IsUnknownNdim()) << "Only support static ndim for now";

LayoutDecision existing_layout = GetLayoutDecision(var_layout_map, call->args[0]);
int ndim = tensor_sinfo->ndim;

// Can't handle sub indexed layouts.
if (existing_layout->layout.ndim() != existing_layout->layout.ndim_primal()) {
existing_layout = LayoutDecision(InitialLayout(ndim));
}

// When axis is not specified, the output is 1D (flattened)
if (!attrs->axis.has_value()) {
return InferLayoutOutput({existing_layout}, {InitialLayoutDecision(1)}, Attrs(call->attrs));
}

// Transform the axis based on the layout
int axis = attrs->axis.value();
if (axis < 0) {
axis += ndim;
}

// Create a mapping from original layout to existing layout
std::string axis_str(ndim, '0');
axis_str[axis] = '1';
for (int i = 0, j = 0; i < ndim; ++i) {
if (axis_str[i] != '1') {
axis_str[i] = 'A' + j++;
}
}

ffi::String new_axis_str =
TransposeStrLike(axis_str, InitialLayout(ndim), existing_layout->layout);

int64_t new_axis = -1;
for (size_t i = 0; i < new_axis_str.size(); ++i) {
if (new_axis_str.at(i) == '1') {
new_axis = i;
break;
}
}
ICHECK_GE(new_axis, 0) << "Failed to find transformed axis";
Comment on lines +1850 to +1857
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@gemini-code-assist gemini-code-assist bot Dec 11, 2025

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medium

This loop to find the index of '1' can be simplified by using ffi::String::find, which is more idiomatic and concise.

  size_t pos = new_axis_str.find('1');
  ICHECK(pos != std::string::npos) << "Failed to find transformed axis";
  int64_t new_axis = static_cast<int64_t>(pos);


ObjectPtr<RepeatAttrs> new_attrs = ffi::make_object<RepeatAttrs>(*attrs);
new_attrs->axis = new_axis;

// When axis is specified, the layout is preserved
return InferLayoutOutput({existing_layout}, {existing_layout}, Attrs(new_attrs));
}

TVM_REGISTER_OP("relax.repeat")
.set_attrs_type<RepeatAttrs>()
.set_num_inputs(1)
.add_argument("data", "Tensor", "The input tensor.")
.set_attr<FInferStructInfo>("FInferStructInfo", InferStructInfoRepeat)
.set_attr<FRelaxInferLayout>("FRelaxInferLayout", InferLayoutRepeat)
.set_attr<Bool>("FPurity", Bool(true));

/* relax.tile */
Expand Down
85 changes: 85 additions & 0 deletions tests/python/relax/test_transform_convert_layout.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4992,5 +4992,90 @@ def main(
verify(Input, Expected_NCHW4c, {"relax.nn.conv2d": ["NCHW4c", "OIHW4o"]})


def test_conv2d_repeat():
@I.ir_module
class Input:
@R.function
def main(
x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32")
) -> R.Tensor(None, "float32", ndim=4):
with R.dataflow():
gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
gv2: R.Tensor((2, 8, 26, 26), "float32") = R.repeat(gv, repeats=2, axis=1)
R.output(gv2)
return gv2

@I.ir_module
class Expected:
@R.function
def main(
x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32")
) -> R.Tensor(None, dtype="float32", ndim=4):
with R.dataflow():
lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1])
lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1])
gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d(
lv,
lv1,
strides=[1, 1],
padding=[0, 0, 0, 0],
dilation=[1, 1],
groups=1,
data_layout="NHWC",
kernel_layout="OHWI",
out_layout="NHWC",
out_dtype="float32",
)
lv2: R.Tensor((2, 26, 26, 8), dtype="float32") = R.repeat(gv, repeats=2, axis=3)
gv2: R.Tensor((2, 8, 26, 26), dtype="float32") = R.permute_dims(
lv2, axes=[0, 3, 1, 2]
)
R.output(gv2)
return gv2

verify(Input, Expected)


def test_conv2d_repeat_flatten():
@I.ir_module
class Input:
@R.function
def main(
x: R.Tensor((2, 3, 28, 28), "float32"), w: R.Tensor((4, 3, 3, 3), "float32")
) -> R.Tensor((5408,), "float32"):
with R.dataflow():
gv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
gv2: R.Tensor((5408,), "float32") = R.repeat(gv, repeats=1)
R.output(gv2)
return gv2

@I.ir_module
class Expected:
@R.function
def main(
x: R.Tensor((2, 3, 28, 28), dtype="float32"), w: R.Tensor((4, 3, 3, 3), dtype="float32")
) -> R.Tensor((5408,), dtype="float32"):
with R.dataflow():
lv: R.Tensor((2, 28, 28, 3), dtype="float32") = R.permute_dims(x, axes=[0, 2, 3, 1])
lv1: R.Tensor((4, 3, 3, 3), dtype="float32") = R.permute_dims(w, axes=[0, 2, 3, 1])
gv: R.Tensor((2, 26, 26, 4), dtype="float32") = R.nn.conv2d(
lv,
lv1,
strides=[1, 1],
padding=[0, 0, 0, 0],
dilation=[1, 1],
groups=1,
data_layout="NHWC",
kernel_layout="OHWI",
out_layout="NHWC",
out_dtype="float32",
)
gv2: R.Tensor((5408,), dtype="float32") = R.repeat(gv, repeats=1)
R.output(gv2)
return gv2

verify(Input, Expected)


if __name__ == "__main__":
tvm.testing.main()
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