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PyTorch export to ONNX Opset 7 and 8 - Cont (#22421)
Summary: This is an extension to the original PR #21765 1. Increase the coverage of different opsets support, comments, and blacklisting. 2. Adding backend tests for both caffe2 and onnxruntime on opset 7 and opset 8. 3. Reusing onnx model tests in caffe2 for onnxruntime. Pull Request resolved: #22421 Reviewed By: zrphercule Differential Revision: D16225518 Pulled By: houseroad fbshipit-source-id: 01ae3eed85111a83a0124e9e95512b80109d6aee
1 parent 9f8e2c0 commit b3147bc

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-73
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‎scripts/onnx/test.sh‎

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@@ -55,6 +55,7 @@ pytest "${args[@]}" \
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'not (TestOperators and test_full_like) and not (TestOperators and test_zeros_like) and not (TestOperators and test_ones_like) and not (TestModels and test_vgg16) and not (TestModels and test_vgg16_bn) and not (TestModels and test_vgg19) and not (TestModels and test_vgg19_bn)' \
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--ignore "$top_dir/test/onnx/test_pytorch_onnx_onnxruntime.py" \
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--ignore "$top_dir/test/onnx/test_custom_ops.py" \
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--ignore "$top_dir/test/onnx/test_models_onnxruntime.py" \
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"${test_paths[@]}"
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# onnxruntime only support py3
@@ -63,5 +64,6 @@ if [[ "$BUILD_ENVIRONMENT" == *py3* ]]; then
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pip install --user onnxruntime
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pytest "${args[@]}" "$top_dir/test/onnx/test_pytorch_onnx_onnxruntime.py"
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pytest "${args[@]}" "$top_dir/test/onnx/test_custom_ops.py"
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pytest "${args[@]}" "$top_dir/test/onnx/test_models_onnxruntime.py"
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fi
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‎test/onnx/test_models_onnxruntime.py‎

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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import onnxruntime # noqa
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from test_models import TestModels
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from test_pytorch_onnx_onnxruntime import run_model_test
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def exportTest(self, model, inputs, rtol=1e-2, atol=1e-7, opset_versions=None):
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opset_versions = opset_versions if opset_versions else [7, 8, 9, 10]
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for opset_version in opset_versions:
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self.opset_version = opset_version
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run_model_test(self, model, False,
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input=inputs, rtol=rtol, atol=atol)
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if __name__ == '__main__':
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TestModels.exportTest = exportTest
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unittest.main()

‎test/onnx/test_onnx_opset.py‎

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@@ -132,6 +132,45 @@ def test_maxpool(self):
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x = torch.randn(20, 16, 50)
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check_onnx_opsets_operator(module, x, ops, opset_versions=[10])
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def test_upsample(self):
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class MyModule(Module):
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def __init__(self):
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super(MyModule, self).__init__()
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def forward(self, x):
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size = [v * 2 for v in x.size()[2:]]
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size = [int(i) for i in size]
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return torch.nn.functional.interpolate(x, size=size, mode='nearest')
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module = MyModule()
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ops8 = [{"op_name" : "Upsample", "attributes" : [{"name": "mode", "s": ("nearest").encode(), "type": 3},
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{"name": "scales", "floats": [1.0, 1.0, 2.0, 2.0], "type": 6}]}]
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ops9 = [{"op_name" : "Constant"},
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{"op_name" : "Upsample", "attributes" : [{"name": "mode", "s": ("nearest").encode(), "type": 3}]}]
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ops = {8 : ops8, 9 : ops9}
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x = torch.randn(2, 2, 2, 2)
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check_onnx_opsets_operator(module, x, ops, opset_versions=[8, 9])
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def test_cast_constant(self):
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class MyModule(Module):
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def __init__(self):
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super(MyModule, self).__init__()
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def forward(self, x):
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return torch._dim_arange(x, 1)
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module = MyModule()
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ops_8 = [{"op_name" : "Shape"}, {"op_name" : "Constant"},
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{"op_name" : "Cast", "attributes": [{"name": "to", "i": 7, "type": 2}]},
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{"op_name" : "Gather", "attributes": [{"name": "axis", "i": 0, "type": 2}]},
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{"op_name" : "Range"}]
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ops_9 = [{"op_name" : "Shape"}, {"op_name" : "Constant"},
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{"op_name" : "Gather", "attributes": [{"name": "axis", "i": 0, "type": 2}]},
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{"op_name" : "Range"}]
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ops = {8 : ops_8, 9 : ops_9}
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x = torch.ones(5, 6)
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check_onnx_opsets_operator(module, x, ops, opset_versions=[8, 9])
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def test_slice(self):
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class MyModule(Module):
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def forward(self, x):

‎test/onnx/test_pytorch_onnx_caffe2.py‎

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@@ -40,7 +40,7 @@
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import caffe2.python.onnx.backend as c2
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from test_pytorch_common import skipIfTravis, skipIfNoLapack, skipIfNoCuda
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from test_pytorch_common import skipIfUnsupportedOpsetVersion
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from test_pytorch_common import skipIfUnsupportedOpsetVersion, skipIfUnsupportedMinOpsetVersion
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import verify
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skip = unittest.skip
@@ -664,6 +664,7 @@ def forward(self, input):
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input = torch.empty(BATCH_SIZE, 10, 10).uniform_(4, 9)
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self.run_model_test(MyModel(), train=False, input=input, batch_size=BATCH_SIZE)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_erf(self):
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class MyModel(torch.nn.Module):
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def __init__(self):
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x = torch.randn(20, 16, 50, 44, 30, requires_grad=True)
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self.run_model_test(model, train=False, input=x, batch_size=BATCH_SIZE)
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@skipIfUnsupportedMinOpsetVersion(8)
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def test_adaptive_max_pool1D(self):
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model = torch.nn.AdaptiveMaxPool1d((5))
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x = torch.randn(20, 16, 50, requires_grad=True)
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self.run_model_test(model, train=False, input=x, batch_size=BATCH_SIZE)
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@skipIfUnsupportedMinOpsetVersion(8)
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def test_adaptive_max_pool2D(self):
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model = torch.nn.AdaptiveMaxPool2d((5, 4))
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x = torch.randn(20, 16, 50, 32, requires_grad=True)
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self.run_model_test(model, train=False, input=x, batch_size=BATCH_SIZE)
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@skipIfUnsupportedMinOpsetVersion(8)
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def test_adaptive_max_pool3D(self):
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model = torch.nn.AdaptiveMaxPool3d((5, 4, 3))
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x = torch.randn(20, 16, 50, 44, 30, requires_grad=True)
@@ -993,7 +997,7 @@ def forward(self, x):
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self.run_model_test(model, train=False, input=(x),
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batch_size=BATCH_SIZE, use_gpu=False)
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@skipIfUnsupportedOpsetVersion([10])
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@skipIfUnsupportedOpsetVersion([7, 8, 10])
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def test_interpolate_upsample_dynamic_sizes(self):
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class MyModel(torch.nn.Module):
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def __init__(self):
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self.run_model_test(FullClass(), train=False, input=(x,), batch_size=BATCH_SIZE,
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use_gpu=False, example_outputs=FullClass()(x))
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_where_functional(self):
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class WhereFunctional(torch.nn.Module):
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def forward(self, x):
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x = torch.randn(3, 4)
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self.run_model_test(WhereFunctional(), train=False, input=(x,), batch_size=BATCH_SIZE, use_gpu=False)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_where_method(self):
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class WhereMethod(torch.nn.Module):
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def forward(self, x):
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self.run_model_test(RsubModel(), train=False, input=(x,),
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batch_size=BATCH_SIZE, use_gpu=False)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_isnan(self):
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class IsNaNModel(torch.nn.Module):
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def forward(self, input):
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x = torch.tensor([1.0, float('nan'), 2.0])
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self.run_model_test(IsNaNModel(), train=False, input=x, batch_size=BATCH_SIZE, use_gpu=False)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_scatter(self):
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class ScatterModel(torch.nn.Module):
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def forward(self, input, indices, values):
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x = torch.randn(4, 4, requires_grad=True)
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self.run_model_test(MaxModel(), train=False, input=x, batch_size=BATCH_SIZE)
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def test_max_keepdim(self):
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class MaxModel(torch.nn.Module):
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def forward(self, input):
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return torch.max(input, dim=1, keepdim=True)
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x = torch.randn(4, 4, requires_grad=True)
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self.run_model_test(MaxModel(), train=False, input=x, batch_size=BATCH_SIZE)
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def test_max_tensors(self):
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class MaxModel(torch.nn.Module):
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def forward(self, input, other):
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return torch.max(input, other)
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x = torch.randn(4, 4, requires_grad=True)
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y = torch.randn(4, 4, requires_grad=True)
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self.run_model_test(MaxModel(), train=False, input=(x, y), batch_size=BATCH_SIZE)
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def test_min(self):
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class MinModel(torch.nn.Module):
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def forward(self, input):
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x = torch.randn(1, 2, 3)
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self.run_model_test(DropoutModel(), train=False, input=x, batch_size=BATCH_SIZE)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_while(self):
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class WhileModel(torch.jit.ScriptModule):
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@torch.jit.script_method
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self.run_model_test(model, train=False, input=(inputs,), batch_size=BATCH_SIZE,
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example_outputs=(outputs,))
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_nested_loops(self):
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class NestedLoopsModel(torch.jit.ScriptModule):
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@torch.jit.script_method
@@ -2013,6 +2040,24 @@ def setup_rnn_tests():
20132040
(unittest.TestCase,),
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dict(TestCaffe2Backend_opset9.__dict__, embed_params=True))
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# opset 7 tests
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TestCaffe2Backend_opset7 = type(str("TestCaffe2Backend_opset7"),
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(unittest.TestCase,),
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dict(TestCaffe2Backend_opset9.__dict__, opset_version=7))
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TestCaffe2BackendEmbed_opset7 = type(str("TestCaffe2BackendEmbed_opset7"),
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(unittest.TestCase,),
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dict(TestCaffe2Backend_opset9.__dict__,
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embed_params=True, opset_version=7))
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# opset 8 tests
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TestCaffe2Backend_opset8 = type(str("TestCaffe2Backend_opset8"),
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(unittest.TestCase,),
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dict(TestCaffe2Backend_opset9.__dict__, opset_version=8))
2056+
TestCaffe2BackendEmbed_opset8 = type(str("TestCaffe2BackendEmbed_opset8"),
2057+
(unittest.TestCase,),
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dict(TestCaffe2Backend_opset9.__dict__,
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embed_params=True, opset_version=8))
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# opset 10 tests
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TestCaffe2Backend_opset10 = type(str("TestCaffe2Backend_opset10"),
20182063
(unittest.TestCase,),

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