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[Relax][PyTorch] Enable decomposition for unary ops and refactor tests #18401

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mshr-h merged 4 commits into from
Oct 28, 2025
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[Relax][PyTorch] Enable decomposition for unary ops and refactor tests #18401

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157 changes: 149 additions & 8 deletions tests/python/relax/test_frontend_from_exported_program.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,18 +61,13 @@ def verify_model(
(torch.log, R.log),
(torch.neg, R.negative),
(torch.relu, R.nn.relu),
(torch.relu_, R.nn.relu),
(torch.round, R.round),
(torch.rsqrt, R.rsqrt),
(torch.selu, R.nn.selu),
(torch.sigmoid, R.sigmoid),
(torch.ops.aten.silu, R.nn.silu),
(torch.ops.aten.silu_, R.nn.silu),
(torch.sin, R.sin),
(torch.sinh, R.sinh),
(torch.sign, R.sign),
(torch.sqrt, R.sqrt),
(torch.square, R.square),
(torch.tan, R.tan),
(torch.tanh, R.tanh),
(torch.trunc, R.trunc),
Expand All @@ -99,11 +94,10 @@ def main(
R.output(gv)
return gv

verify_model(UnaryOp(), example_args, {}, expected)
verify_model(UnaryOp(), example_args, {}, expected, run_ep_decomposition=True)


operator_bool_unary = [
(torch.isfinite, R.isfinite),
(torch.isinf, R.isinf),
(torch.isnan, R.isnan),
]
Expand All @@ -129,7 +123,7 @@ def main(
R.output(gv)
return gv

verify_model(UnaryOp(), example_args, {}, expected)
verify_model(UnaryOp(), example_args, {}, expected, run_ep_decomposition=True)


def test_extended_unary_ops():
Expand Down Expand Up @@ -467,6 +461,30 @@ def main(
Hardswish3(), example_args, {}, expected_hardswish_for_3, run_ep_decomposition=True
)

# isfinite
class IsFinite(Module):
def forward(self, input):
return torch.isfinite(input)

@tvm.script.ir_module
class expected_isfinite:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="bool")):
with R.dataflow():
lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.abs(input)
lv1: R.Tensor((1, 3, 10, 10), dtype="bool") = R.not_equal(
lv, R.const(float("inf"), "float32")
)
lv2: R.Tensor((1, 3, 10, 10), dtype="bool") = R.equal(input, input)
lv3: R.Tensor((1, 3, 10, 10), dtype="bool") = R.multiply(lv2, lv1)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="bool")) = (lv3,)
R.output(gv)
return gv

verify_model(IsFinite(), example_args, {}, expected_isfinite, run_ep_decomposition=True)

# log2
class Log2(Module):
def forward(self, x):
Expand Down Expand Up @@ -657,6 +675,129 @@ def main(
verify_model(ReLU6_2(), example_args, {}, expected_relu6_2, run_ep_decomposition=True)
verify_model(ReLU6_3(), example_args, {}, expected_relu6_3, run_ep_decomposition=True)

# selu
class SELU(Module):
def forward(self, input):
return torch.nn.functional.selu(input)

@tvm.script.ir_module
class expected_selu:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")):
with R.dataflow():
lv: R.Tensor((1, 3, 10, 10), dtype="bool") = R.greater(
input, R.const(0.0, "float32")
)
lv1: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
input, R.const(1.0507010221481323, "float32")
)
lv2: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
input, R.const(1.0, "float32")
)
lv3: R.Tensor((1, 3, 10, 10), dtype="float32") = R.exp(lv2)
lv4: R.Tensor((1, 3, 10, 10), dtype="float32") = R.subtract(
lv3, R.const(1.0, "float32")
)
lv5: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
lv4, R.const(1.7580993175506592, "float32")
)
lv6: R.Tensor((1, 3, 10, 10), dtype="float32") = R.where(lv, lv1, lv5)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv6,)
R.output(gv)
return gv

verify_model(SELU(), example_args, {}, expected_selu, run_ep_decomposition=True)

# silu
class SiLU(Module):
def forward(self, input):
return torch.nn.functional.silu(input)

@tvm.script.ir_module
class expected_silu:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")):
with R.dataflow():
lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.sigmoid(input)
lv1: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(input, lv)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv1,)
R.output(gv)
return gv

verify_model(SiLU(), example_args, {}, expected_silu, run_ep_decomposition=True)

# silu_
class SiLU_(Module):
def forward(self, input):
return torch.ops.aten.silu_(input)

@tvm.script.ir_module
class expected_silu_:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(
R.Tensor((1, 3, 10, 10), dtype="float32"), R.Tensor((1, 3, 10, 10), dtype="float32")
):
with R.dataflow():
lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.sigmoid(input)
lv1: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(input, lv)
gv: R.Tuple(
R.Tensor((1, 3, 10, 10), dtype="float32"),
R.Tensor((1, 3, 10, 10), dtype="float32"),
) = (
lv1,
lv1,
)
R.output(gv)
return gv

verify_model(SiLU_(), example_args, {}, expected_silu_, run_ep_decomposition=True)

# square
class Square(Module):
def forward(self, input):
return torch.square(input)

@tvm.script.ir_module
class expected_square:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")):
with R.dataflow():
lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.power(
input, R.const(2.0, "float32")
)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
R.output(gv)
return gv

verify_model(Square(), example_args, {}, expected_square, run_ep_decomposition=True)

# relu_
class ReLU_(Module):
def forward(self, input):
return torch.relu_(input.clone())

@tvm.script.ir_module
class expected_relu_:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")):
with R.dataflow():
lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.nn.relu(input)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
R.output(gv)
return gv

verify_model(ReLU_(), example_args, {}, expected_relu_, run_ep_decomposition=True)


def test_hardtanh():
class Hardtanh(torch.nn.Module):
Expand Down
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