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[Relax][PyTorch] Add 3D interpolate support using resize3d #18937

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Merged
tlopex merged 1 commit into from
Mar 26, 2026
Merged

[Relax][PyTorch] Add 3D interpolate support using resize3d #18937

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40 changes: 29 additions & 11 deletions python/tvm/relax/frontend/torch/fx_translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -527,7 +527,7 @@ def _interpolate(self, node: fx.Node) -> relax.Var:
# Determine spatial dimension indices based on layout
# NCHW: spatial dims are [2, 3, ...] (skip batch and channel)
# NHWC: spatial dims are [1, 2, ...] (skip batch, before channel)
if self.default_image_layout == "NHWC":
if self.default_image_layout in ("NHWC", "NDHWC"):
spatial_start = 1
spatial_end = len(shape) - 1
else: # NCHW or other layouts
Expand All @@ -547,25 +547,43 @@ def _interpolate(self, node: fx.Node) -> relax.Var:

if method.startswith("nearest"):
method = "nearest_neighbor"
elif method[0:2] == "bi":
elif method.startswith("bi"):
method = method[2:]
elif method.startswith("tri"):
method = method[3:]

if method == "nearest_neighbor":
coord_trans = "asymmetric"
elif align_corners:
elif align_corners is True:
coord_trans = "align_corners"
else:
coord_trans = "half_pixel"

return self.block_builder.emit(
relax.op.image.resize2d(
data,
size,
layout=self.default_image_layout,
method=method,
coordinate_transformation_mode=coord_trans,
if data.struct_info.ndim == 5:
if self.default_image_layout == "NDHWC":
layout_3d = "NDHWC"
else:
layout_3d = "NCDHW"

return self.block_builder.emit(
relax.op.image.resize3d(
data,
size,
layout=layout_3d,
method=method,
coordinate_transformation_mode=coord_trans,
)
)
else:
return self.block_builder.emit(
relax.op.image.resize2d(
data,
size,
layout=self.default_image_layout,
method=method,
coordinate_transformation_mode=coord_trans,
)
)
)

def _linear_module(self, node: fx.Node) -> relax.Var:
x = self.env[node.args[0]]
Expand Down
222 changes: 222 additions & 0 deletions tests/python/relax/test_frontend_from_fx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3671,6 +3671,149 @@ def main(input_1: R.Tensor((1, 3, 10, 10), dtype="float32")) -> R.Tensor(

verify_model(Interpolate4(), input_info, {}, expected4)

input_info_5d = [([1, 3, 4, 10, 10], "float32")]
class Interpolate5(Module):
def forward(self, input):
return torch.nn.functional.interpolate(
input,
size=None,
scale_factor=(2.0, 2.0, 2.0),
mode="trilinear",
align_corners=False,
)
@tvm.script.ir_module
class expected5:
@R.function
def main(input_5: R.Tensor((1, 3, 4, 10, 10), dtype="float32")) -> R.Tensor(
(1, 3, 8, 20, 20), dtype="float32"
):

with R.dataflow():
lv: R.Tensor((1, 3, 8, 20, 20), dtype="float32") = R.image.resize3d(
input_5,
(8, 20, 20),
roi=[0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
layout="NCDHW",
method="linear",
coordinate_transformation_mode="half_pixel",
rounding_method="",
cubic_alpha=-0.75,
cubic_exclude=0,
extrapolation_value=0,
out_dtype="",
)
gv: R.Tensor((1, 3, 8, 20, 20), dtype="float32") = lv
R.output(gv)
return gv

verify_model(Interpolate5(), input_info_5d, {}, expected5)

class Interpolate6(Module):
def forward(self, input):
return torch.nn.functional.interpolate(
input,
size=None,
scale_factor=(2.0,4.0,4.0),
mode="trilinear",
align_corners=False,
)
@tvm.script.ir_module
class expected6:
@R.function
def main(input_5: R.Tensor((1, 3, 4, 10, 10), dtype="float32")) -> R.Tensor(
(1, 3, 8, 40, 40), dtype="float32"
):

with R.dataflow():
lv: R.Tensor((1, 3, 8, 40, 40), dtype="float32") = R.image.resize3d(
input_5,
(8, 40, 40),
roi=[0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
layout="NCDHW",
method="linear",
coordinate_transformation_mode="half_pixel",
rounding_method="",
cubic_alpha=-0.75,
cubic_exclude=0,
extrapolation_value=0,
out_dtype="",
)
gv: R.Tensor((1, 3, 8, 40, 40), dtype="float32") = lv
R.output(gv)
return gv

verify_model(Interpolate6(), input_info_5d, {}, expected6)

class Interpolate7(Module):
def forward(self, input):
return torch.nn.functional.interpolate(
input,
size=(8,40,40),
mode="trilinear",
align_corners=False,
)
@tvm.script.ir_module
class expected7:
@R.function
def main(input_5: R.Tensor((1, 3, 4, 10, 10), dtype="float32")) -> R.Tensor(
(1, 3, 8, 40, 40), dtype="float32"
):

with R.dataflow():
lv: R.Tensor((1, 3, 8, 40, 40), dtype="float32") = R.image.resize3d(
input_5,
(8, 40, 40),
roi=[0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
layout="NCDHW",
method="linear",
coordinate_transformation_mode="half_pixel",
rounding_method="",
cubic_alpha=-0.75,
cubic_exclude=0,
extrapolation_value=0,
out_dtype="",
)
gv: R.Tensor((1, 3, 8, 40, 40), dtype="float32") = lv
R.output(gv)
return gv

verify_model(Interpolate7(), input_info_5d, {}, expected7)

class Interpolate8(Module):
def forward(self, input):
return torch.nn.functional.interpolate(
input,
size=(8,40,40),
mode="trilinear",
align_corners=True,
)
@tvm.script.ir_module
class expected8:
@R.function
def main(input_5: R.Tensor((1, 3, 4, 10, 10), dtype="float32")) -> R.Tensor(
(1, 3, 8, 40, 40), dtype="float32"
):

with R.dataflow():
lv: R.Tensor((1, 3, 8, 40, 40), dtype="float32") = R.image.resize3d(
input_5,
(8, 40, 40),
roi=[0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
layout="NCDHW",
method="linear",
coordinate_transformation_mode="align_corners",
rounding_method="",
cubic_alpha=-0.75,
cubic_exclude=0,
extrapolation_value=0,
out_dtype="",
)
gv: R.Tensor((1, 3, 8, 40, 40), dtype="float32") = lv
R.output(gv)
return gv

verify_model(Interpolate8(), input_info_5d, {}, expected8)


def test_interpolate_nhwc_layout():
# First verify backward compatibility - default should still be NCHW
Expand Down Expand Up @@ -3786,6 +3929,85 @@ def main(input_1: R.Tensor((1, 10, 10, 3), dtype="float32")) -> R.Tensor(
mod2 = from_fx(graph_model2, input_info, default_image_layout="NHWC")
tvm.ir.assert_structural_equal(mod2, expected_nhwc2)

input_info_5d = [([1, 4, 10, 10, 3], "float32")]

class InterpolateNHWC3(Module):
def forward(self, input):
return torch.nn.functional.interpolate(
input,
size=None,
scale_factor=(2.0,4.0,4.0),
mode="trilinear",
align_corners=False,
)
@tvm.script.ir_module
class expected_nhwc3:
@R.function
def main(input_5: R.Tensor((1, 4, 10, 10, 3), dtype="float32")) -> R.Tensor(
(1, 8, 40, 40, 3), dtype="float32"
):

with R.dataflow():
lv: R.Tensor((1, 8, 40, 40, 3), dtype="float32") = R.image.resize3d(
input_5,
(8, 40, 40),
roi=[0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
layout="NDHWC",
method="linear",
coordinate_transformation_mode="half_pixel",
rounding_method="",
cubic_alpha=-0.75,
cubic_exclude=0,
extrapolation_value=0,
out_dtype="",
)
gv: R.Tensor((1, 8, 40, 40, 3), dtype="float32") = lv
R.output(gv)
return gv

graph_model3 = fx.symbolic_trace(InterpolateNHWC3())
with torch.no_grad():
mod3 = from_fx(graph_model3, input_info_5d, default_image_layout="NDHWC")
tvm.ir.assert_structural_equal(mod3, expected_nhwc3)

class InterpolateNHWC4(Module):
def forward(self, input):
return torch.nn.functional.interpolate(
input,
size=None,
scale_factor=(2.0,4.0,4.0),
mode="trilinear",
align_corners=True,
)
@tvm.script.ir_module
class expected_nhwc4:
@R.function
def main(input_5: R.Tensor((1, 4, 10, 10, 3), dtype="float32")) -> R.Tensor(
(1, 8, 40, 40, 3), dtype="float32"
):

with R.dataflow():
lv: R.Tensor((1, 8, 40, 40, 3), dtype="float32") = R.image.resize3d(
input_5,
(8, 40, 40),
roi=[0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000],
layout="NDHWC",
method="linear",
coordinate_transformation_mode="align_corners",
rounding_method="",
cubic_alpha=-0.75,
cubic_exclude=0,
extrapolation_value=0,
out_dtype="",
)
gv: R.Tensor((1, 8, 40, 40, 3), dtype="float32") = lv
R.output(gv)
return gv

graph_model4 = fx.symbolic_trace(InterpolateNHWC4())
with torch.no_grad():
mod4 = from_fx(graph_model4, input_info_5d, default_image_layout="NDHWC")
tvm.ir.assert_structural_equal(mod4, expected_nhwc4)

def test_addmm():
input_info = [
Expand Down