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[Relax] Add FRelaxInferLayout and TMixedPrecisionPolicy for dynamic_strided_slice #18633

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Merged
tlopex merged 1 commit into from
Jan 5, 2026
Merged

[Relax] Add FRelaxInferLayout and TMixedPrecisionPolicy for dynamic_strided_slice #18633

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20 changes: 19 additions & 1 deletion src/relax/op/tensor/index.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -549,14 +549,32 @@ StructInfo InferStructInfoDynStridedSlice(const Call& call, const BlockBuilder&
return TensorStructInfo(data_sinfo->dtype, n_axis, data_sinfo->vdevice);
}

// TODO(tvm-team): Register FRelaxInferLayout, TMixedPrecisionPolicy
InferLayoutOutput InferLayoutDynStridedSlice(
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* tensor_sinfo = GetStructInfoAs<TensorStructInfoNode>(call->args[0]);
CHECK(tensor_sinfo) << "Invalid Call";
CHECK(!tensor_sinfo->IsUnknownNdim()) << "Layout inference only supports known dimensionality, "
<< "but expression " << call << " has argument "
<< call->args[0] << " of unknown dimensionality.";
int ndim = tensor_sinfo->ndim;
// Since begin/end/strides are dynamic tensors, we cannot transform
// them at compile time. Fall back to the initial layout.
LayoutDecision initial = LayoutDecision(InitialLayout(ndim));
return InferLayoutOutput({initial}, {initial}, Attrs());
}

TVM_REGISTER_OP("relax.dynamic_strided_slice")
.set_num_inputs(4)
.add_argument("x", "Tensor", "The source tensor to be sliced.")
.add_argument("begin", "Tensor", "The indices to begin with in the slicing.")
.add_argument("end", "Tensor", "Indices indicating end of the slice.")
.add_argument("strides", "Tensor", "The stride values.")
.set_attr<FInferStructInfo>("FInferStructInfo", InferStructInfoDynStridedSlice)
.set_attr<FRelaxInferLayout>("FRelaxInferLayout", InferLayoutDynStridedSlice)
.set_attr<TMixedPrecisionPolicy>("TMixedPrecisionPolicy", MixedPrecisionPolicyKind::kFollow)
.set_attr<Bool>("FPurity", Bool(true));

} // namespace relax
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52 changes: 52 additions & 0 deletions tests/python/relax/test_transform_convert_layout.py
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Expand Up @@ -5231,5 +5231,57 @@ def main(
verify(Input, Expected)


def test_conv2d_dynamic_strided_slice():
@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"),
begin: R.Tensor((4,), "int64"),
end: R.Tensor((4,), "int64"),
strides: R.Tensor((4,), "int64"),
) -> 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.dynamic_strided_slice(gv, begin, end, strides)
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"),
begin: R.Tensor((4,), dtype="int64"),
end: R.Tensor((4,), dtype="int64"),
strides: R.Tensor((4,), dtype="int64"),
) -> 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, 4, 26, 26), dtype="float32") = R.permute_dims(
gv, axes=[0, 3, 1, 2]
)
gv2 = R.dynamic_strided_slice(lv2, begin, end, strides)
R.output(gv2)
return gv2

verify(Input, Expected)


if __name__ == "__main__":
tvm.testing.main()
53 changes: 53 additions & 0 deletions tests/python/relax/test_transform_to_mixed_precision.py
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Expand Up @@ -1064,5 +1064,58 @@ def tir_identity(
tvm.ir.assert_structural_equal(Expected, After)


def test_dynamic_strided_slice():
@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"),
begin: R.Tensor((4,), "int64"),
end: R.Tensor((4,), "int64"),
strides: R.Tensor((4,), "int64"),
) -> R.Tensor(None, "float32", ndim=4):
with R.dataflow():
lv: R.Tensor((2, 4, 26, 26), "float32") = R.nn.conv2d(x, w, out_dtype="float32")
gv = R.dynamic_strided_slice(lv, begin, end, strides)
R.output(gv)
return gv

@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"),
begin: R.Tensor((4,), dtype="int64"),
end: R.Tensor((4,), dtype="int64"),
strides: R.Tensor((4,), dtype="int64"),
) -> R.Tensor(None, dtype="float32", ndim=4):
with R.dataflow():
lv: R.Tensor((2, 3, 28, 28), dtype="float16") = R.astype(x, dtype="float16")
lv1: R.Tensor((4, 3, 3, 3), dtype="float16") = R.astype(w, dtype="float16")
lv2: R.Tensor((2, 4, 26, 26), dtype="float32") = R.nn.conv2d(
lv,
lv1,
strides=[1, 1],
padding=[0, 0, 0, 0],
dilation=[1, 1],
groups=1,
data_layout="NCHW",
kernel_layout="OIHW",
out_layout="NCHW",
out_dtype="float32",
)
lv3: R.Tensor((2, 4, 26, 26), dtype="float16") = R.astype(lv2, dtype="float16")
lv4: R.Tensor((2, 4, 26, 26), dtype="float32") = R.astype(lv3, dtype="float32")
gv: R.Tensor(None, dtype="float32", ndim=4) = R.dynamic_strided_slice(
lv4, begin, end, strides
)
R.output(gv)
return gv

_assert_test(Input, Expected)
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@tlopex tlopex Jan 5, 2026

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Why we don't use tvm.ir.assert_structural_equal here?

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@guan404ming guan404ming Jan 5, 2026

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_assert_test is shared helper func in this file. I use it for consistency here. Shall we update to use tvm.ir.assert_structural_equal?

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@tlopex tlopex Jan 5, 2026

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No worries, I had a look and it's nearly the same



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