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[Relax][PyTorch] Add mul_.Tensor, max.default, min.default and pow.Scalar Op Support into Exported Program Frontend #17843

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Merged
Hzfengsy merged 2 commits into from
Apr 18, 2025
+43 −0
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[Relax][PyTorch] Add mul_.Tensor, max.default, min.default and pow.Scalar Op Support into Exported Program Frontend #17843

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bdd0f8d
add mapping to the missing binary ops
Apr 16, 2025
376f4db
add test script for the supported ops
Apr 16, 2025
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4 changes: 4 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 @@ -340,11 +340,15 @@ def create_convert_map(
),
"max.other": self._binary_op(relax.op.maximum, max),
"min.other": self._binary_op(relax.op.minimum, min),
"max.default": self._unary_op(relax.op.max),
"min.default": self._unary_op(relax.op.min),
"remainder.Tensor": self._binary_op(relax.op.mod, operator.mod),
"remainder.Scalar": self._binary_op(relax.op.mod, operator.mod),
"mul.Tensor": self._binary_op(relax.op.multiply, operator.mul),
"mul_.Tensor": self._binary_op(relax.op.multiply, operator.mul),
"ne.Tensor": self._binary_op(relax.op.not_equal, operator.ne),
"ne.Scalar": self._binary_op(relax.op.not_equal, operator.ne),
"pow.Scalar": self._binary_op(relax.op.power, operator.pow),
"pow.Tensor_Scalar": self._binary_op(relax.op.power, operator.pow),
"pow.Tensor_Tensor": self._binary_op(relax.op.power, operator.pow),
"sub.Tensor": self._binary_op(relax.op.subtract, operator.sub),
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39 changes: 39 additions & 0 deletions tests/python/relax/test_frontend_from_exported_program.py
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Expand Up @@ -474,6 +474,44 @@ def main(

verify_model(Reciprocal(), example_args, {}, expected_reciprocal)

# Returns the maximum value of all elements in the input tensor.
class MaxModel(Module):
def forward(self, input):
return torch.max(input)

@tvm.script.ir_module
class expected_max:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((), dtype="float32")):
with R.dataflow():
lv: R.Tensor((), dtype="float32") = R.max(input, axis=None, keepdims=False)
gv: R.Tuple(R.Tensor((), dtype="float32")) = (lv,)
R.output(gv)
return gv

verify_model(MaxModel(), example_args, {}, expected_max)

# Returns the minimum value of all elements in the input tensor.
class MinModel(Module):
def forward(self, input):
return torch.min(input)

@tvm.script.ir_module
class expected_min:
@R.function
def main(
input: R.Tensor((1, 3, 10, 10), dtype="float32")
) -> R.Tuple(R.Tensor((), dtype="float32")):
with R.dataflow():
lv: R.Tensor((), dtype="float32") = R.min(input, axis=None, keepdims=False)
gv: R.Tuple(R.Tensor((), dtype="float32")) = (lv,)
R.output(gv)
return gv

verify_model(MinModel(), example_args, {}, expected_min)


def test_hardtanh():
class Hardtanh(torch.nn.Module):
Expand Down Expand Up @@ -742,6 +780,7 @@ def main(
(torch.ops.aten.add_, R.add),
(operator.sub, R.subtract),
(operator.mul, R.multiply),
(torch.ops.aten.mul_, R.multiply),
(operator.truediv, R.divide),
(operator.floordiv, R.floor_divide),
(operator.pow, R.power),
Expand Down