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Upcast Celu for it to support more dtypes | feat(torchlib) (#1158) #492

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Upcast Celu for it to support more dtypes | feat(torchlib) (#1158)

Upcast Celu for it to support more dtypes | feat(torchlib) (#1158) #492

GitHub Actions / Test Results failed Nov 21, 2023 in 0s

54 fail, 2 797 skipped, 8 343 pass in 1h 5m 31s

         18 files  ±  0         18 suites  ±0   1h 5m 31s ⏱️ + 6m 2s
  11 194 tests +  7    8 343 ✔️ +  5      2 797 💤 +  2       54 ❌ ±0 
160 176 runs  +63  36 743 ✔️ +45  121 384 💤 +18  2 049 ❌ ±0 

Results for commit 660b9f4. ± Comparison against earlier commit eb73ec2.

Annotations

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__ops_aten__softmax_half_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
Failed: Unexpected success
Unexpected success

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 5s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:355: in aten_all_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 12s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 3s]
Raw output 
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/core.py:478: in aten_any_dim
    dims = op.Reshape(dim, op.Constant(value_ints=[-1]))
onnxscript/onnx_opset/_impl/opset14.py:909: in Reshape
    return op(*self._prepare_inputs(schema, data, shape), allowzero=allowzero)
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:471: in _call_ort
    model, session_run_input, inputs = _prepare_model_and_inputs_for_eager(
onnxscript/evaluator.py:411: in _prepare_model_and_inputs_for_eager
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:411: in <listcomp>
    args = [_onnxscript_to_numpy_value(x) for x in args]
onnxscript/evaluator.py:377: in _onnxscript_to_numpy_value
    raise TypeError(
E   TypeError: Unexpected onnxscript value type '<class 'tuple'>'.Valid value types are 'Tensor | list[Tensor] | None | np.ndarray | list[np.ndarray]'

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64 input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64 input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16 input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16 input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16 input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16 input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32 input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (all_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32 input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_all_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (all_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64 input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64 input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32 input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
   ir_version: 8,
   opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
   _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
  domain: "pkg.onnxscript.torch_lib",
  opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
   cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
   result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
      result = Cast <to: int = 9> (self)
   }, else_branch: graph = elseGraph_5 () => ( result_0) {
      self_bool = Cast <to: int = 9> (self)
      self_int = Cast <to: int = 7> (self_bool)
      dim = Constant <value_int: int = @dim> ()
      tmp = Constant <value_ints: ints = [-1]> ()
      dims = Reshape (dim, tmp)
      any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
      result_0 = Cast <to: int = 9> (any_true)
   }>
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32 input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,5] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
    onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
    C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E   onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E   (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
    raise AssertionError(
E   AssertionError: ONNX model is invalid. Model:
E   <
E      ir_version: 8,
E      opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E      producer_name: "pytorch",
E      producer_version: "2.2.0"
E   >
E   main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E      _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib",
E     opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E   >
E   aten_any_dim <dim>(self) => (result_1)
E   {
E      cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E      result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E         result = Cast <to: int = 9> (self)
E      }, else_branch: graph = elseGraph_5 () => ( result_0) {
E         self_bool = Cast <to: int = 9> (self)
E         self_int = Cast <to: int = 7> (self_bool)
E         dim = Constant <value_int: int = @dim> ()
E         tmp = Constant <value_ints: ints = [-1]> ()
E         dims = Reshape (dim, tmp)
E         any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E         result_0 = Cast <to: int = 9> (any_true)
E      }>
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   Rank (input) => (return_val)
E   {
E      tmp = Shape (input)
E      return_val = Size (tmp)
E   }
E   <
E     domain: "pkg.onnxscript.torch_lib.common",
E     opset_import: ["" : 18]
E   >
E   IsScalar (input) => (return_val)
E   {
E      tmp = Shape (input)
E      tmp_0 = Size (tmp)
E      tmp_1 = Constant <value_int: int = 0> ()
E      return_val = Equal (tmp_0, tmp_1)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

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@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__nn_functional_adaptive_avg_pool1d_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output 
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .GlobalAveragePool op with onnx model
<
   ir_version: 3,
   opset_import: ["" : 1]
>
node_graph (float16[3,8,8] input0) => (float16[3,8,1] output0) {
   output0 = GlobalAveragePool (input0)
}
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .GlobalAveragePool op with onnx model
<
   ir_version: 3,
   opset_import: ["" : 1]
>
node_graph (float16[1,8,8] input0) => (float16[1,8,1] output0) {
   output0 = GlobalAveragePool (input0)
}
onnxscript/evaluator.py:476: in _call_ort
    session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:463: in _create_inference_session
    sess.initialize_session(providers, provider_options, disabled_optimizers)
E   onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. In Node, ("InsertedCast_input0", Cast, "", -1) : ("input0": tensor(float16),) -> ("InsertedCast_input0": tensor(float),) , Error Mismatched attribute type in 'InsertedCast_input0 : to'

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:56: in aten_adaptive_avg_pool1d
    result = op.GlobalAveragePool(self)
onnxscript/onnx_opset/_impl/opset1.py:1288: in GlobalAveragePool
    return op(*self._prepare_inputs(schema, X))
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
    raise EagerModeError(
E   onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .GlobalAveragePool op with onnx model
E   <
E      ir_version: 3,
E      opset_import: ["" : 1]
E   >
E   node_graph (float16[3,8,8] input0) => (float16[3,8,1] output0) {
E      output0 = GlobalAveragePool (input0)
E   }
onnxscript/evaluator.py:476: in _call_ort
    session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:463: in _create_inference_session
    sess.initialize_session(providers, provider_options, disabled_optimizers)
E   onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. In Node, ("InsertedCast_input0", Cast, "", -1) : ("input0": tensor(float16),) -> ("InsertedCast_input0": tensor(float),) , Error Mismatched attribute type in 'InsertedCast_input0 : to'

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:53: in aten_adaptive_avg_pool1d
    pooled = op.GlobalAveragePool(self)
onnxscript/onnx_opset/_impl/opset1.py:1288: in GlobalAveragePool
    return op(*self._prepare_inputs(schema, X))
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
    raise EagerModeError(
E   onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .GlobalAveragePool op with onnx model
E   <
E      ir_version: 3,
E      opset_import: ["" : 1]
E   >
E   node_graph (float16[1,8,8] input0) => (float16[1,8,1] output0) {
E      output0 = GlobalAveragePool (input0)
E   }

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__native_batch_norm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 10 / 125 (8.0%)
E   Greatest absolute difference: 0.002197265625 at index (2, 1, 2) (up to 1e-05 allowed)
E   Greatest relative difference: 0.01470947265625 at index (1, 0, 0) (up to 0.001 allowed)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 1 / 3 (33.3%)
E   Greatest absolute difference: 0.000732421875 at index (1, 0) (up to 1e-05 allowed)
E   Greatest relative difference: 0.0014848709106445312 at index (1, 0) (up to 0.001 allowed)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 2 / 72 (2.8%)
E   Greatest absolute difference: 0.000732421875 at index (0, 0, 0, 2) (up to 1e-05 allowed)
E   Greatest relative difference: 0.0090484619140625 at index (1, 0, 0, 0) (up to 0.001 allowed)

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
    raise AssertionError(f"Output {j} mismatch") from e
E   AssertionError: Output 0 mismatch

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

See this annotation in the file changed.

@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__log_softmax_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output 
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.

Check warning on line 0 in onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU

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@github-actions github-actions / Test Results

All 3 runs failed: test_output_match_opinfo__nn_functional_scaled_dot_product_attention_bool_mask_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU) 

artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output 
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
<
   ir_version: 7,
   opset_import: ["" : 14]
>
node_graph (float16[4,3,6] input0, float[3,6] input1) => (float16[4,3,6] output0) {
   output0 = Add (input0, input1)
}
AssertionError: Tensor-likes are not close!

Mismatched elements: 18 / 96 (18.8%)
Greatest absolute difference: 0.0390625 at index (1, 2, 0) (up to 1e-05 allowed)
Greatest relative difference: 0.052001953125 at index (1, 2, 2) (up to 0.001 allowed)
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
<
   ir_version: 7,
   opset_import: ["" : 14]
>
node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
   output0 = Add (input0, input1)
}
AssertionError: Tensor-likes are not close!

Mismatched elements: 56 / 384 (14.6%)
Greatest absolute difference: 0.03515625 at index (1, 0, 1, 5) (up to 1e-05 allowed)
Greatest relative difference: 0.11883544921875 at index (2, 0, 0, 0) (up to 0.001 allowed)
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
<
   ir_version: 7,
   opset_import: ["" : 14]
>
node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
   output0 = Add (input0, input1)
}
AssertionError: Tensor-likes are not close!

Mismatched elements: 51 / 384 (13.3%)
Greatest absolute difference: 0.102294921875 at index (3, 2, 0, 2) (up to 1e-05 allowed)
Greatest relative difference: 4.1796875 at index (0, 0, 0, 2) (up to 0.001 allowed)
onnxscript/evaluator.py:476: in _call_ort
    session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:454: in _create_inference_session
    sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E   onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Type Error: Type parameter (T) of Optype (Add) bound to different types (tensor(float16) and tensor(float) in node ().

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:573: in __call__
    return self.func(*args, **kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1797: in aten_scaled_dot_product_attention_bool_mask
    return _aten_scaled_dot_product_attention_float_mask_onnx(
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1913: in _aten_scaled_dot_product_attention_float_mask_onnx
    op.Add(op.MatMul(query_scaled, key_transposed_scaled), attn_mask),
onnxscript/onnx_opset/_impl/opset14.py:82: in Add
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
    raise EagerModeError(
E   onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
E   <
E      ir_version: 7,
E      opset_import: ["" : 14]
E   >
E   node_graph (float16[4,3,6] input0, float[3,6] input1) => (float16[4,3,6] output0) {
E      output0 = Add (input0, input1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 18 / 96 (18.8%)
E   Greatest absolute difference: 0.0390625 at index (1, 2, 0) (up to 1e-05 allowed)
E   Greatest relative difference: 0.052001953125 at index (1, 2, 2) (up to 0.001 allowed)
onnxscript/evaluator.py:476: in _call_ort
    session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:454: in _create_inference_session
    sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E   onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Type Error: Type parameter (T) of Optype (Add) bound to different types (tensor(float16) and tensor(float) in node ().

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:573: in __call__
    return self.func(*args, **kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1797: in aten_scaled_dot_product_attention_bool_mask
    return _aten_scaled_dot_product_attention_float_mask_onnx(
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1913: in _aten_scaled_dot_product_attention_float_mask_onnx
    op.Add(op.MatMul(query_scaled, key_transposed_scaled), attn_mask),
onnxscript/onnx_opset/_impl/opset14.py:82: in Add
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
    raise EagerModeError(
E   onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
E   <
E      ir_version: 7,
E      opset_import: ["" : 14]
E   >
E   node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
E      output0 = Add (input0, input1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 56 / 384 (14.6%)
E   Greatest absolute difference: 0.03515625 at index (1, 0, 1, 5) (up to 1e-05 allowed)
E   Greatest relative difference: 0.11883544921875 at index (2, 0, 0, 0) (up to 0.001 allowed)
onnxscript/evaluator.py:476: in _call_ort
    session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:454: in _create_inference_session
    sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E   onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Type Error: Type parameter (T) of Optype (Add) bound to different types (tensor(float16) and tensor(float) in node ().

The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
    function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
    return function(*args, **kwargs)
onnxscript/values.py:573: in __call__
    return self.func(*args, **kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1797: in aten_scaled_dot_product_attention_bool_mask
    return _aten_scaled_dot_product_attention_float_mask_onnx(
onnxscript/values.py:525: in __call__
    return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
    result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1913: in _aten_scaled_dot_product_attention_float_mask_onnx
    op.Add(op.MatMul(query_scaled, key_transposed_scaled), attn_mask),
onnxscript/onnx_opset/_impl/opset14.py:82: in Add
    return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:303: in __call__
    return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
    outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
    return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
    raise EagerModeError(
E   onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
E   <
E      ir_version: 7,
E      opset_import: ["" : 14]
E   >
E   node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
E      output0 = Add (input0, input1)
E   }
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
    torch.testing.assert_close(
E   AssertionError: Tensor-likes are not close!
E   
E   Mismatched elements: 51 / 384 (13.3%)
E   Greatest absolute difference: 0.102294921875 at index (3, 2, 0, 2) (up to 1e-05 allowed)
E   Greatest relative difference: 4.1796875 at index (0, 0, 0, 2) (up to 0.001 allowed)
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