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new 03ba03eb21 [Relax][PyTorch] Cleanup tests for ExportedProgram frontend
(#17822)
03ba03eb21 is described below
commit 03ba03eb21c03e18a9bd56cd1878b8e6c682c661
Author: Masahiro Hiramori <[email protected]>
AuthorDate: Thu Apr 10 21:58:24 2025 +0900
[Relax][PyTorch] Cleanup tests for ExportedProgram frontend (#17822)
* move gelu, relu, selu, sigmoid, silu tests to test_basic_unary_ops
* remove unused torchversion
* we don't need to manually call `test_*` functions
* remove unused variable
---
.../relax/test_frontend_from_exported_program.py | 186 +--------------------
1 file changed, 7 insertions(+), 179 deletions(-)
diff --git a/tests/python/relax/test_frontend_from_exported_program.py
b/tests/python/relax/test_frontend_from_exported_program.py
index 58f28fb1b3..42288cf562 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -27,9 +27,6 @@ from tvm.script import ir as I
from tvm.script import relax as R
from tvm.script import tir as T
from tvm.relax.frontend.torch import from_exported_program
-from packaging import version
-
-torch_version = torch.__version__
def verify_model(torch_model, example_args, binding, expected,
dynamic_shapes=None):
@@ -56,10 +53,17 @@ operator_basic_unary = [
(torch.erf, R.erf),
(torch.exp, R.exp),
(torch.floor, R.floor),
+ (torch.ops.aten.gelu, R.nn.gelu),
(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),
@@ -314,35 +318,6 @@ def test_extended_unary_ops():
verify_model(Elu(), example_args, {}, expected_elu)
verify_model(Elu2(), example_args, {}, expected_elu)
- # gelu
- class Gelu(Module):
- def __init__(self):
- super().__init__()
- self.gelu = torch.nn.GELU()
-
- def forward(self, input):
- return self.gelu(input)
-
- class Gelu2(Module):
- def forward(self, input):
- return torch.nn.functional.gelu(input)
-
- @tvm.script.ir_module
- class expected_gelu:
- @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: R.Tensor((1, 3, 10, 10), dtype="float32") =
R.nn.gelu(input_1)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
- R.output(gv)
- return gv
-
- verify_model(Gelu(), example_args, {}, expected_gelu)
- verify_model(Gelu2(), example_args, {}, expected_gelu)
-
# hardsigmoid
class Hardsigmoid(torch.nn.Module):
def __init__(self):
@@ -413,15 +388,6 @@ def test_extended_unary_ops():
verify_model(Hardswish2(), example_args, {}, expected1)
verify_model(Hardswish3(), example_args, {}, expected1)
- # hardtanh
- test_hardtanh()
-
- # leakyrelu
- test_leakyrelu()
-
- # softplus
- test_softplus()
-
# log2
class Log2(Module):
def forward(self, x):
@@ -487,9 +453,6 @@ def test_extended_unary_ops():
verify_model(Log1p(), example_args, {}, Expected_log1p)
- # log_softmax
- test_logsoftmax()
-
# reciprocal
class Reciprocal(Module):
def forward(self, input):
@@ -511,140 +474,6 @@ def test_extended_unary_ops():
verify_model(Reciprocal(), example_args, {}, expected_reciprocal)
- # relu
- class ReLU0(Module):
- def __init__(self):
- super().__init__()
- self.relu = torch.nn.ReLU()
-
- def forward(self, input):
- return self.relu(input)
-
- class ReLU1(Module):
- def forward(self, input):
- return torch.nn.functional.relu(input)
-
- class ReLU2(Module):
- def forward(self, input):
- return torch.ops.aten.relu_(input)
-
- @tvm.script.ir_module
- class expected_relu:
- @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: R.Tensor((1, 3, 10, 10), dtype="float32") =
R.nn.relu(input_1)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
- R.output(gv)
- return gv
-
- verify_model(ReLU0(), example_args, {}, expected_relu)
- verify_model(ReLU1(), example_args, {}, expected_relu)
- verify_model(ReLU2(), 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: 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.selu(input)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
- 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):
- super().__init__()
- self.sigmoid = torch.nn.Sigmoid()
-
- def forward(self, input):
- return self.sigmoid(input)
-
- class Sigmoid2(Module):
- def forward(self, input):
- return torch.sigmoid(input)
-
- @tvm.script.ir_module
- class expected_sigmoid:
- @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: R.Tensor((1, 3, 10, 10), dtype="float32") =
R.sigmoid(input_1)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
- R.output(gv)
- return gv
-
- verify_model(Sigmoid(), example_args, {}, expected_sigmoid)
- verify_model(Sigmoid2(), example_args, {}, expected_sigmoid)
-
- # silu
- class SiLU(Module):
- def __init__(self):
- super().__init__()
- self.silu = torch.nn.SiLU()
-
- def forward(self, input):
- return self.silu(input)
-
- class SiLU2(Module):
- def forward(self, input):
- return torch.nn.functional.silu(input)
-
- class SiLU3(Module):
- def forward(self, input):
- return torch.ops.aten.silu_(input)
-
- @tvm.script.ir_module
- class expected_silu:
- @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: R.Tensor((1, 3, 10, 10), dtype="float32") =
R.nn.silu(input_1)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
- R.output(gv)
- return gv
-
- verify_model(SiLU(), example_args, {}, expected_silu)
- verify_model(SiLU2(), example_args, {}, expected_silu)
- verify_model(SiLU3(), example_args, {}, expected_silu)
-
- # softmax
- test_softmax()
-
- # softshrink
- test_softshrink()
-
- # tril, triu
- test_tril_triu()
-
def test_hardtanh():
class Hardtanh(torch.nn.Module):
@@ -1044,7 +873,6 @@ def test_binary3():
torch.randn(10, 10, dtype=torch.float32),
torch.randn(10, 10, dtype=torch.float32),
)
- example_args2 = (torch.randn(10, 10, dtype=torch.float32),)
# Max
class Max1(Module):
