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[Relax][PyTorch] Support elu, celu, selu ops for ExportedProgram importer #17738

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Merged
Hzfengsy merged 6 commits into from
Mar 12, 2025
Merged

[Relax][PyTorch] Support elu, celu, selu ops for ExportedProgram importer #17738

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2d34aa7
Update exported_program_translator.py
tlopex Mar 11, 2025
8487e96
Update test_frontend_from_exported_program.py
tlopex Mar 11, 2025
6148c0f
Merge branch 'apache:main' into tlopex-torch_exportn1
tlopex Mar 11, 2025
6d876a6
Update test_frontend_from_exported_program.py
tlopex Mar 11, 2025
659aed5
Update test_frontend_from_exported_program.py
tlopex Mar 11, 2025
8278604
Update test_frontend_from_exported_program.py
tlopex Mar 11, 2025
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3 changes: 3 additions & 0 deletions python/tvm/relax/frontend/torch/exported_program_translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,10 +192,12 @@ def create_convert_map(
"atanh.default": self._unary_op(relax.op.atanh),
"bitwise_not.default": self._unary_op(relax.op.bitwise_not),
"ceil.default": self._unary_op(relax.op.ceil),
"celu.default": self._celu,
"clamp.default": self._clamp,
"cos.default": self._unary_op(relax.op.cos),
"cosh.default": self._unary_op(relax.op.cosh),
"dropout.default": lambda node: self.env[node.args[0]],
"elu.default": self._elu,
"erf.default": self._unary_op(relax.op.erf),
"exp.default": self._unary_op(relax.op.exp),
"floor.default": self._unary_op(relax.op.floor),
Expand All @@ -213,6 +215,7 @@ def create_convert_map(
"relu.default": self._unary_op(relax.op.nn.relu),
"round.default": self._round,
"rsqrt.default": self._unary_op(relax.op.rsqrt),
"selu.default": self._selu,
"sigmoid.default": self._unary_op(relax.op.sigmoid),
"sign.default": self._unary_op(relax.op.sign),
"silu.default": self._unary_op(relax.op.nn.silu),
Expand Down
123 changes: 123 additions & 0 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 @@ -126,6 +126,49 @@ def main(
def test_extended_unary_ops():
example_args = (torch.randn(1, 3, 10, 10, dtype=torch.float32),)

# celu
class Celu1(Module):
def __init__(self):
super().__init__()
self.celu = torch.nn.CELU()

def forward(self, input):
return self.celu(input)

class Celu2(Module):
def forward(self, input):
return torch.nn.functional.celu(input)

# alpha * min(0, exp(x / alpha) - 1) + max(0, x)
@tvm.script.ir_module
class expected_celu:
@R.function
def main(
input_1: 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.exp(input_1)
lv_div: R.Tensor((1, 3, 10, 10), dtype="float32") = R.divide(
lv, R.const(1.0, "float32")
)
lv_sub: R.Tensor((1, 3, 10, 10), dtype="float32") = R.subtract(
lv_div, R.const(1.0, "float32")
)
lv_min: R.Tensor((1, 3, 10, 10), dtype="float32") = R.minimum(
R.const(0.0, "float32"), lv_sub
)
lv_scaled: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
R.const(1.0, "float32"), lv_min
)
lv_relu_x: R.Tensor((1, 3, 10, 10), dtype="float32") = R.nn.relu(input_1)
lv_celu: R.Tensor((1, 3, 10, 10), dtype="float32") = R.add(lv_scaled, lv_relu_x)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv_celu,)
R.output(gv)
return gv

verify_model(Celu1(), example_args, {}, expected_celu)
verify_model(Celu2(), example_args, {}, expected_celu)

# clamp
class Clamp(Module):
def forward(self, input):
Expand Down Expand Up @@ -174,6 +217,46 @@ def main(
verify_model(Dropout1(), example_args, {}, expected_dropout)
verify_model(Dropout2(), example_args, {}, expected_dropout)

# elu
class Elu(Module):
def __init__(self):
super().__init__()
self.elu = torch.nn.ELU()

def forward(self, input):
return self.elu(input)

class Elu2(Module):
def forward(self, input):
return torch.nn.functional.elu(input)

@tvm.script.ir_module
class expected_elu:
@R.function
def main(
input_1: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")):
# block 0
with R.dataflow():
lv_exp: R.Tensor((1, 3, 10, 10), dtype="float32") = R.exp(input_1)
lv_one_minus_exp: R.Tensor((1, 3, 10, 10), dtype="float32") = R.subtract(
R.const(1.0, dtype="float32"), lv_exp
)
lv_relu_one_minus_exp: R.Tensor((1, 3, 10, 10), dtype="float32") = R.nn.relu(
lv_one_minus_exp
)
lv_scaled: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
R.const(-1.0, dtype="float32"), lv_relu_one_minus_exp
)
lv_relu_x: R.Tensor((1, 3, 10, 10), dtype="float32") = R.nn.relu(input_1)
lv_elu: R.Tensor((1, 3, 10, 10), dtype="float32") = R.add(lv_scaled, lv_relu_x)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv_elu,)
R.output(gv)
return gv

verify_model(Elu(), example_args, {}, expected_elu)
verify_model(Elu2(), example_args, {}, expected_elu)

# gelu
class Gelu(Module):
def __init__(self):
Expand Down Expand Up @@ -306,6 +389,46 @@ def main(
verify_model(ReLU0(), example_args, {}, expected_relu)
verify_model(ReLU1(), example_args, {}, expected_relu)

# selu
class Selu1(Module):
def __init__(self):
super().__init__()
self.selu = torch.nn.SELU()

def forward(self, input):
return self.selu(input)

class Selu2(Module):
def forward(self, input):
return torch.nn.functional.selu(input)

@tvm.script.ir_module
class expected_selu:
@R.function
def main(
input_1: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")):
with R.dataflow():
lv_relu: R.Tensor((1, 3, 10, 10), dtype="float32") = R.nn.relu(input_1)
lv_exp: R.Tensor((1, 3, 10, 10), dtype="float32") = R.exp(input_1)
lv_sub: R.Tensor((1, 3, 10, 10), dtype="float32") = R.subtract(
lv_exp, R.const(1.0, "float32")
)
lv_scaled: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
R.const(1.6732631921768188, "float32"), lv_sub
)
lv_add: R.Tensor((1, 3, 10, 10), dtype="float32") = R.add(lv_relu, lv_scaled)
lv_selu: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
R.const(1.0507010221481323, "float32"), lv_add
)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv_selu,)
R.output(gv)

return gv

verify_model(Selu1(), example_args, {}, expected_selu)
verify_model(Selu2(), example_args, {}, expected_selu)

# sigmoid
class Sigmoid(Module):
def __init__(self):
Expand Down