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Fix ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops #88504

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Fix ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops #88504

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220 changes: 134 additions & 86 deletions test/onnx/test_pytorch_onnx_no_runtime.py
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Open in desktop
Expand Up @@ -18,7 +18,7 @@
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.onnx import symbolic_helper, utils
from torch.onnx import OperatorExportTypes, symbolic_helper, utils
from torch.onnx._globals import GLOBALS
from torch.onnx._internal import registration
from torch.testing._internal import common_quantization, common_utils, jit_utils
Expand Down Expand Up @@ -935,6 +935,139 @@ def forward(self, x, w):

torch.onnx.export_to_pretty_string(Mod(), (torch.rand(3, 4), torch.rand(4, 5)))

@common_utils.skipIfNoCaffe2
def test_caffe2_aten_fallback_must_fallback(self):
class ModelWithAtenNotONNXOp(torch.nn.Module):
def forward(self, x, y):
abcd = x + y
defg = torch.linalg.qr(abcd)
return defg

# TODO: Refactor common_utils._decide_skip_caffe2 to support parametrize
for operator_export_type in (
OperatorExportTypes.ONNX_ATEN,
OperatorExportTypes.ONNX_ATEN_FALLBACK,
):
x = torch.rand(3, 4)
y = torch.rand(3, 4)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenNotONNXOp(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=operator_export_type,
# support for linalg.qr was added in later op set versions.
opset_version=9,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
self.assertAtenOp(onnx_model, "linalg_qr")

@common_utils.skipIfNoCaffe2
def test_caffe2_onnx_aten_must_not_fallback(self):
class ModelWithAtenFmod(torch.nn.Module):
def forward(self, x, y):
return torch.fmod(x, y)

# TODO: Refactor common_utils._decide_skip_caffe2 to support parametrize
for operator_export_type in (
OperatorExportTypes.ONNX_ATEN_FALLBACK,
OperatorExportTypes.ONNX_ATEN,
):
x = torch.randn(3, 4, dtype=torch.float32)
y = torch.randn(3, 4, dtype=torch.float32)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenFmod(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=operator_export_type,
opset_version=10, # or higher
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
assert onnx_model.graph.node[0].op_type == "Mod"

@common_utils.skipIfCaffe2
def test_aten_fallback_must_fallback(self):
class ModelWithAtenNotONNXOp(torch.nn.Module):
def forward(self, x, y):
abcd = x + y
defg = torch.linalg.qr(abcd)
return defg

x = torch.rand(3, 4)
y = torch.rand(3, 4)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenNotONNXOp(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
# support for linalg.qr was added in later op set versions.
opset_version=9,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
self.assertAtenOp(onnx_model, "linalg_qr")

@common_utils.skipIfCaffe2
def test_onnx_aten(self):
class ModelWithAtenFmod(torch.nn.Module):
def forward(self, x, y):
return torch.fmod(x, y)

x = torch.randn(3, 4, dtype=torch.float32)
y = torch.randn(3, 4, dtype=torch.float32)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenFmod(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
self.assertAtenOp(onnx_model, "fmod", "Tensor")

@common_utils.skipIfCaffe2
def test_onnx_aten_fallback_must_not_fallback(self):
# For BUILD_CAFFE2=0, aten fallback only when not exportable
class ONNXExportable(torch.nn.Module):
def __init__(self):
super(ONNXExportable, self).__init__()
self.quant = torch.quantization.QuantStub()
self.fc1 = torch.nn.Linear(12, 8)
self.fc2 = torch.nn.Linear(8, 4)
self.fc3 = torch.nn.Linear(4, 6)
self.dequant = torch.quantization.DeQuantStub()

def forward(self, x):
x = self.quant(x)
x = x.view((-1, 12))
h = F.relu(self.fc1(x))
h = F.relu(self.fc2(h))
h = F.relu(self.fc3(h))
h = self.dequant(h)
return h

dummy_input = torch.randn(12)
f = io.BytesIO()
torch.onnx.export(
ONNXExportable(),
(dummy_input,),
f,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
all_aten_nodes = [
p
for p in onnx_model.graph.node
if p.op_type == "ATen" and p.domain == "org.pytorch.aten"
]
self.assertEqual(len(all_aten_nodes), 0)


class TestQuantizeEagerONNXExport(common_utils.TestCase):
def _test_lower_graph_impl(self, model, data):
Expand Down Expand Up @@ -997,91 +1130,6 @@ def test_lower_graph_conv3d(self):
data = torch.from_numpy(data_numpy).to(dtype=torch.float)
self._test_lower_graph_impl(model, data)

@common_utils.skipIfNoCaffe2
def test_caffe2_aten_fallback(self):
class ModelWithAtenNotONNXOp(torch.nn.Module):
def forward(self, x, y):
abcd = x + y
defg = torch.linalg.qr(abcd)
return defg

x = torch.rand(3, 4)
y = torch.rand(3, 4)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenNotONNXOp(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
# support for linalg.qr was added in later op set versions.
opset_version=9,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
self.assertAtenOp(onnx_model, "linalg_qr")

@common_utils.skipIfNoCaffe2
def test_caffe2_onnx_aten(self):
class ModelWithAtenFmod(torch.nn.Module):
def forward(self, x, y):
return torch.fmod(x, y)

x = torch.randn(3, 4, dtype=torch.float32)
y = torch.randn(3, 4, dtype=torch.float32)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenFmod(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN,
opset_version=10, # or higher
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
assert onnx_model.graph.node[0].op_type == "Mod"

@common_utils.skipIfCaffe2
def test_aten_fallback(self):
class ModelWithAtenNotONNXOp(torch.nn.Module):
def forward(self, x, y):
abcd = x + y
defg = torch.linalg.qr(abcd)
return defg

x = torch.rand(3, 4)
y = torch.rand(3, 4)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenNotONNXOp(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK,
# support for linalg.qr was added in later op set versions.
opset_version=9,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
self.assertAtenOp(onnx_model, "linalg_qr")

@common_utils.skipIfCaffe2
def test_onnx_aten(self):
class ModelWithAtenFmod(torch.nn.Module):
def forward(self, x, y):
return torch.fmod(x, y)

x = torch.randn(3, 4, dtype=torch.float32)
y = torch.randn(3, 4, dtype=torch.float32)
f = io.BytesIO()
torch.onnx.export(
ModelWithAtenFmod(),
(x, y),
f,
do_constant_folding=False,
operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN,
)
onnx_model = onnx.load(io.BytesIO(f.getvalue()))
self.assertAtenOp(onnx_model, "fmod", "Tensor")


if __name__ == "__main__":
common_utils.run_tests()
19 changes: 15 additions & 4 deletions torch/onnx/utils.py
View file
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Expand Up @@ -1752,10 +1752,21 @@ def _should_aten_fallback(
)
is_caffe2_build = _C_onnx._CAFFE2_ATEN_FALLBACK

return name.startswith("aten::") and (
((is_onnx_aten_export or is_aten_fallback_export) and not is_caffe2_build)
or (not is_exportable_aten_op and is_aten_fallback_export)
)
if not name.startswith("aten::"):
return False

if is_caffe2_build:
if (
is_onnx_aten_export or is_aten_fallback_export
) and not is_exportable_aten_op:
return True
else:
if is_onnx_aten_export or (
is_aten_fallback_export and not is_exportable_aten_op
):
return True

return False


@_beartype.beartype
Expand Down