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Posted to commits@tvm.apache.org by ms...@apache.org on 2025/10/29 01:42:48 UTC
(tvm) branch main updated: [Relax][PyTorch] Add support for decomposed operators and fix IR of ops tests(1) (#18402)
This is an automated email from the ASF dual-hosted git repository.
mshr pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/main by this push:
new f532b89e55 [Relax][PyTorch] Add support for decomposed operators and fix IR of ops tests(1) (#18402)
f532b89e55 is described below
commit f532b89e5558c27cf92c573fbc005c6b3c53b0a8
Author: Shushi Hong <82...@qq.com>
AuthorDate: Tue Oct 28 21:42:41 2025 -0400
[Relax][PyTorch] Add support for decomposed operators and fix IR of ops tests(1) (#18402)
* finish1
* finish2
---
.../frontend/torch/exported_program_translator.py | 2 +
.../relax/test_frontend_from_exported_program.py | 121 ++++++++++++++++-----
2 files changed, 97 insertions(+), 26 deletions(-)
diff --git a/python/tvm/relax/frontend/torch/exported_program_translator.py b/python/tvm/relax/frontend/torch/exported_program_translator.py
index cbf9e33a12..011e23f1df 100644
--- a/python/tvm/relax/frontend/torch/exported_program_translator.py
+++ b/python/tvm/relax/frontend/torch/exported_program_translator.py
@@ -837,7 +837,9 @@ class ExportedProgramImporter(BaseFXGraphImporter):
"log10.default": self._log10,
"log1p.default": self._log1p,
"logical_not.default": self._unary_op(relax.op.logical_not),
+ "logical_and.default": self._binary_op(relax.op.logical_and, operator.and_),
"log_softmax.int": self._log_softmax,
+ "_log_softmax.default": self._log_softmax,
"neg.default": self._unary_op(relax.op.negative),
"pad.default": self._pad,
"pixel_shuffle.default": self._pixel_shuffle,
diff --git a/tests/python/relax/test_frontend_from_exported_program.py b/tests/python/relax/test_frontend_from_exported_program.py
index 9d1ef48712..9851804e2a 100644
--- a/tests/python/relax/test_frontend_from_exported_program.py
+++ b/tests/python/relax/test_frontend_from_exported_program.py
@@ -817,7 +817,7 @@ def test_hardtanh():
return torch.ops.aten.hardtanh_(input)
@tvm.script.ir_module
- class expected1:
+ class expected_for_1_2:
@R.function
def main(
inp_0: R.Tensor((1, 3, 10, 10), dtype="float32")
@@ -830,10 +830,29 @@ def test_hardtanh():
R.output(gv)
return gv
+ @tvm.script.ir_module
+ class expected_hardtanh_for_3:
+ @R.function
+ def main(
+ inp_0: 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.clip(
+ inp_0, R.prim_value(T.float64(-1.0)), R.prim_value(T.float64(1.0))
+ )
+ gv: R.Tuple(
+ R.Tensor((1, 3, 10, 10), dtype="float32"),
+ R.Tensor((1, 3, 10, 10), dtype="float32"),
+ ) = (lv, lv)
+ R.output(gv)
+ return gv
+
example_args = (torch.randn(1, 3, 10, 10, dtype=torch.float32),)
- verify_model(Hardtanh(), example_args, {}, expected1)
- verify_model(Hardtanh2(), example_args, {}, expected1)
- verify_model(Hardtanh3(), example_args, {}, expected1)
+ verify_model(Hardtanh(), example_args, {}, expected_for_1_2, run_ep_decomposition=True)
+ verify_model(Hardtanh2(), example_args, {}, expected_for_1_2, run_ep_decomposition=True)
+ verify_model(Hardtanh3(), example_args, {}, expected_hardtanh_for_3, run_ep_decomposition=True)
def test_softplus():
@@ -861,16 +880,26 @@ def test_softplus():
x: 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.softplus(
- x, beta=1.0, threshold=20.0
+ lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(
+ x, R.const(1.0, "float32")
)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
+ lv1: R.Tensor((1, 3, 10, 10), dtype="float32") = R.exp(lv)
+ lv2: R.Tensor((1, 3, 10, 10), dtype="float32") = R.add(lv1, R.const(1.0, "float32"))
+ lv3: R.Tensor((1, 3, 10, 10), dtype="float32") = R.log(lv2)
+ lv4: R.Tensor((1, 3, 10, 10), dtype="float32") = R.divide(
+ lv3, R.const(1.0, "float32")
+ )
+ lv5: R.Tensor((1, 3, 10, 10), dtype="bool") = R.greater(
+ lv, R.const(20.0, "float32")
+ )
+ lv6: R.Tensor((1, 3, 10, 10), dtype="float32") = R.where(lv5, x, lv4)
+ gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv6,)
R.output(gv)
return gv
example_args = (torch.randn(1, 3, 10, 10, dtype=torch.float32),)
- verify_model(Softplus0(), example_args, {}, expected)
- verify_model(Softplus1(), example_args, {}, expected)
+ verify_model(Softplus0(), example_args, {}, expected, run_ep_decomposition=True)
+ verify_model(Softplus1(), example_args, {}, expected, run_ep_decomposition=True)
def test_leakyrelu():
@@ -896,22 +925,40 @@ def test_leakyrelu():
return torch.ops.aten.leaky_relu_(input, 0.02)
@tvm.script.ir_module
- class expected:
+ class expected_for_1_2:
@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.leakyrelu(input_1, 0.02)
+ lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.nn.leakyrelu(input_1, alpha=0.02)
gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
R.output(gv)
return gv
+ @tvm.script.ir_module
+ class expected_for_3:
+ @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")
+ ):
+ # block 0
+ with R.dataflow():
+ lv: R.Tensor((1, 3, 10, 10), dtype="float32") = R.nn.leakyrelu(input, alpha=0.02)
+ gv: R.Tuple(
+ R.Tensor((1, 3, 10, 10), dtype="float32"),
+ R.Tensor((1, 3, 10, 10), dtype="float32"),
+ ) = (lv, lv)
+ R.output(gv)
+ return gv
+
example_args = (torch.randn(1, 3, 10, 10, dtype=torch.float32),)
- verify_model(LeakyReLU0(), example_args, {}, expected)
- verify_model(LeakyReLU1(), example_args, {}, expected)
- verify_model(LeakyReLU2(), example_args, {}, expected)
+ verify_model(LeakyReLU0(), example_args, {}, expected_for_1_2, run_ep_decomposition=True)
+ verify_model(LeakyReLU1(), example_args, {}, expected_for_1_2, run_ep_decomposition=True)
+ verify_model(LeakyReLU2(), example_args, {}, expected_for_3, run_ep_decomposition=True)
def test_logaddexp():
@@ -923,13 +970,32 @@ def test_logaddexp():
class expected:
@R.function
def main(
- input_1: R.Tensor((1, 3, 10, 10), dtype="float32"),
- input_2: R.Tensor((1, 3, 10, 10), dtype="float32"),
+ input1: R.Tensor((1, 3, 10, 10), dtype="float32"),
+ input2: 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.log_add_exp(input_1, input_2)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
+ lv: R.Tensor((1, 3, 10, 10), dtype="bool") = R.greater_equal(input1, input2)
+ lv1: R.Tensor((1, 3, 10, 10), dtype="float32") = R.where(lv, input1, input2)
+ lv2: R.Tensor((1, 3, 10, 10), dtype="float32") = R.where(lv, input2, input1)
+ lv3: R.Tensor((1, 3, 10, 10), dtype="float32") = R.abs(input1)
+ lv4: R.Tensor((1, 3, 10, 10), dtype="bool") = R.not_equal(
+ lv3, R.const(float("inf"), "float32")
+ )
+ lv5: R.Tensor((1, 3, 10, 10), dtype="bool") = R.equal(input1, input1)
+ lv6: R.Tensor((1, 3, 10, 10), dtype="bool") = R.multiply(lv5, lv4)
+ lv7: R.Tensor((1, 3, 10, 10), dtype="bool") = R.logical_not(lv6)
+ lv8: R.Tensor((1, 3, 10, 10), dtype="bool") = R.equal(input1, input2)
+ lv9: R.Tensor((1, 3, 10, 10), dtype="bool") = R.logical_and(lv7, lv8)
+ lv10: R.Tensor((1, 3, 10, 10), dtype="float32") = R.subtract(lv2, lv1)
+ lv11: R.Tensor((1, 3, 10, 10), dtype="float32") = R.exp(lv10)
+ lv12: R.Tensor((1, 3, 10, 10), dtype="float32") = R.add(
+ lv11, R.const(1.0, "float32")
+ )
+ lv13: R.Tensor((1, 3, 10, 10), dtype="float32") = R.log(lv12)
+ lv14: R.Tensor((1, 3, 10, 10), dtype="float32") = R.add(lv1, lv13)
+ lv15: R.Tensor((1, 3, 10, 10), dtype="float32") = R.where(lv9, input1, lv14)
+ gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv15,)
R.output(gv)
return gv
@@ -937,7 +1003,7 @@ def test_logaddexp():
torch.randn(1, 3, 10, 10, dtype=torch.float32),
torch.randn(1, 3, 10, 10, dtype=torch.float32),
)
- verify_model(LogAddExp(), example_args, {}, expected)
+ verify_model(LogAddExp(), example_args, {}, expected, run_ep_decomposition=True)
def test_logsoftmax():
@@ -967,8 +1033,8 @@ def test_logsoftmax():
return gv
example_args = (torch.randn(1, 3, 10, 10, dtype=torch.float32),)
- verify_model(LogSoftmax(), example_args, {}, expected1)
- verify_model(LogSoftmax2(), example_args, {}, expected1)
+ verify_model(LogSoftmax(), example_args, {}, expected1, run_ep_decomposition=True)
+ verify_model(LogSoftmax2(), example_args, {}, expected1, run_ep_decomposition=True)
def test_prelu():
@@ -995,16 +1061,19 @@ def test_prelu():
x: 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.prelu(
- x, R.const([0.25], dtype="float32"), axis=1
+ lv: R.Tensor((1, 1, 1, 1), dtype="float32") = R.reshape(
+ R.const([0.25], dtype="float32"), R.shape([1, 1, 1, 1])
)
- gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv,)
+ lv1: R.Tensor((1, 3, 10, 10), dtype="bool") = R.greater(x, R.const(0.0, "float32"))
+ lv2: R.Tensor((1, 3, 10, 10), dtype="float32") = R.multiply(lv, x)
+ lv3: R.Tensor((1, 3, 10, 10), dtype="float32") = R.where(lv1, x, lv2)
+ gv: R.Tuple(R.Tensor((1, 3, 10, 10), dtype="float32")) = (lv3,)
R.output(gv)
return gv
example_args = (torch.randn(1, 3, 10, 10, dtype=torch.float32),)
- verify_model(Prelu1(), example_args, {}, expected)
- verify_model(Prelu2(), example_args, {}, expected)
+ verify_model(Prelu1(), example_args, {}, expected, run_ep_decomposition=True)
+ verify_model(Prelu2(), example_args, {}, expected, run_ep_decomposition=True)
def test_softmax():