Implement rand* ops | feat(torchilb) #4718
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failed
Oct 10, 2023 in 0s
29 fail, 2 723 skipped, 8 269 pass in 2h 11m 58s
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github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_like_low_dtype_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:310: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:641: in sample_inputs_randint_like_low_dtype
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_like_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:310: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:624: in sample_inputs_randint_like
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randn_like_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 1s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 1s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 3s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 1s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 1s]
Raw output
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .ConstantOfShape op with onnx model
<
ir_version: 4,
opset_import: ["" : 9]
>
node_graph (float input0) => ( output0) {
output0 = ConstantOfShape (input0)
}
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .ConstantOfShape op with onnx model
<
ir_version: 4,
opset_import: ["" : 9]
>
node_graph (float[5,5] input0) => ( output0) {
output0 = ConstantOfShape (input0)
}
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .ConstantOfShape op with onnx model
<
ir_version: 4,
opset_import: ["" : 9]
>
node_graph (float[0,5,0] input0) => ( output0) {
output0 = ConstantOfShape (input0)
}
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\evaluator.py:476: in _call_ort
session = ort.InferenceSession(
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:462: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (input0) of operator (ConstantOfShape) in node () is invalid.
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:583: in executor
return function(*args, **kwargs)
onnxscript\values.py:519: 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:6351: in aten_randn
shaper = op.ConstantOfShape(size)
onnxscript\onnx_opset\_impl\opset9.py:339: in ConstantOfShape
return op(*self._prepare_inputs(schema, input), value=value)
onnxscript\values.py:297: 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 .ConstantOfShape op with onnx model
E <
E ir_version: 4,
E opset_import: ["" : 9]
E >
E node_graph (float input0) => ( output0) {
E output0 = ConstantOfShape (input0)
E }
onnxscript\evaluator.py:476: in _call_ort
session = ort.InferenceSession(
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:462: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (input0) of operator (ConstantOfShape) in node () is invalid.
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:583: in executor
return function(*args, **kwargs)
onnxscript\values.py:519: 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:6351: in aten_randn
shaper = op.ConstantOfShape(size)
onnxscript\onnx_opset\_impl\opset9.py:339: in ConstantOfShape
return op(*self._prepare_inputs(schema, input), value=value)
onnxscript\values.py:297: 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 .ConstantOfShape op with onnx model
E <
E ir_version: 4,
E opset_import: ["" : 9]
E >
E node_graph (float[5,5] input0) => ( output0) {
E output0 = ConstantOfShape (input0)
E }
onnxscript\evaluator.py:476: in _call_ort
session = ort.InferenceSession(
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:462: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (input0) of operator (ConstantOfShape) in node () is invalid.
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:583: in executor
return function(*args, **kwargs)
onnxscript\values.py:519: 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:6351: in aten_randn
shaper = op.ConstantOfShape(size)
onnxscript\onnx_opset\_impl\opset9.py:339: in ConstantOfShape
return op(*self._prepare_inputs(schema, input), value=value)
onnxscript\values.py:297: 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 .ConstantOfShape op with onnx model
E <
E ir_version: 4,
E opset_import: ["" : 9]
E >
E node_graph (float[0,5,0] input0) => ( output0) {
E output0 = ConstantOfShape (input0)
E }
onnxscript\tests\function_libs\torch_lib\ops_test.py:310: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_rand_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
RuntimeError: Trying to create tensor with negative dimension -2: [6, 7, -2, 8, -1, 1, -4, 5, 7, -6]
RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
onnxscript\tests\function_libs\torch_lib\ops_test.py:210: in run_test_output_match
torch_output = op(*inputs, **cpu_sample.kwargs)
.nox\test\lib\site-packages\torch\testing\_internal\opinfo\core.py:1074: in __call__
return self.op(*args, **kwargs)
.nox\test\lib\site-packages\torch\_ops.py:502: in __call__
return self._op(*args, **kwargs or {})
E RuntimeError: Trying to create tensor with negative dimension -2: [6, 7, -2, 8, -1, 1, -4, 5, 7, -6]
onnxscript\tests\function_libs\torch_lib\ops_test.py:210: in run_test_output_match
torch_output = op(*inputs, **cpu_sample.kwargs)
.nox\test\lib\site-packages\torch\testing\_internal\opinfo\core.py:1074: in __call__
return self.op(*args, **kwargs)
.nox\test\lib\site-packages\torch\_ops.py:502: in __call__
return self._op(*args, **kwargs or {})
E RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
onnxscript\tests\function_libs\torch_lib\ops_test.py:210: in run_test_output_match
torch_output = op(*inputs, **cpu_sample.kwargs)
.nox\test\lib\site-packages\torch\testing\_internal\opinfo\core.py:1074: in __call__
return self.op(*args, **kwargs)
.nox\test\lib\site-packages\torch\_ops.py:502: in __call__
return self._op(*args, **kwargs or {})
E RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_low_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:310: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:614: in sample_inputs_randint_low
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:310: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:605: in sample_inputs_randint
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randn_like_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array(6.880847, dtype=float32)}
Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.0.1"
>
torch_jit (float input_0) => (float _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_randn <dtype = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
aten_randn (size) => (return_val)
{
shaper = ConstantOfShape (size)
return_val = RandomNormalLike <dtype: int = @dtype> (shaper)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[ 1.8161163, -4.3816953, 5.2855434, 7.9338856, -6.6026535],
[ 7.8227654, 1.6844339, 6.6492796, 1.2188749, 4.339693 ],
[-1.270719 , 6.937972 , 1.3302803, -4.201559 , 2.2940845],
[-4.14663 , -1.0554557, -3.655425 , 5.970339 , -7.1043315],
[-4.149093 , -2.5413728, -5.4114523, 0.8494482, -8.889112 ]],
dtype=float32)}
Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.0.1"
>
torch_jit (float[5,5] input_0) => (float[5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_randn <dtype = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
aten_randn (size) => (return_val)
{
shaper = ConstantOfShape (size)
return_val = RandomNormalLike <dtype: int = @dtype> (shaper)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([], shape=(0, 5, 0), dtype=float32)}
Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.0.1"
>
torch_jit (float[0,5,0] input_0) => (float[0,5,0] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_randn <dtype = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
aten_randn (size) => (return_val)
{
shaper = ConstantOfShape (size)
return_val = RandomNormalLike <dtype: int = @dtype> (shaper)
}
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:535: in _capture_graph_and_evaluate_torch_script_evaluator
return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:306: in _ort_session_run
session = ort.InferenceSession(
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:462: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (input_0) of operator (aten_randn) in node (aten_randn_0) is invalid.
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:549: in _capture_graph_and_evaluate_torch_script_evaluator
raise RuntimeError(
E RuntimeError: ONNX Runtime failed to evaluate:
E Inputs:
E {'input_0': array(6.880847, dtype=float32)}
E Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
E producer_name: "pytorch",
E producer_version: "2.0.1"
E >
E torch_jit (float input_0) => (float _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_randn <dtype = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E aten_randn (size) => (return_val)
E {
E shaper = ConstantOfShape (size)
E return_val = RandomNormalLike <dtype: int = @dtype> (shaper)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:535: in _capture_graph_and_evaluate_torch_script_evaluator
return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:306: in _ort_session_run
session = ort.InferenceSession(
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:462: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (input_0) of operator (aten_randn) in node (aten_randn_0) is invalid.
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:549: in _capture_graph_and_evaluate_torch_script_evaluator
raise RuntimeError(
E RuntimeError: ONNX Runtime failed to evaluate:
E Inputs:
E {'input_0': array([[ 1.8161163, -4.3816953, 5.2855434, 7.9338856, -6.6026535],
E [ 7.8227654, 1.6844339, 6.6492796, 1.2188749, 4.339693 ],
E [-1.270719 , 6.937972 , 1.3302803, -4.201559 , 2.2940845],
E [-4.14663 , -1.0554557, -3.655425 , 5.970339 , -7.1043315],
E [-4.149093 , -2.5413728, -5.4114523, 0.8494482, -8.889112 ]],
E dtype=float32)}
E Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
E producer_name: "pytorch",
E producer_version: "2.0.1"
E >
E torch_jit (float[5,5] input_0) => (float[5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_randn <dtype = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E aten_randn (size) => (return_val)
E {
E shaper = ConstantOfShape (size)
E return_val = RandomNormalLike <dtype: int = @dtype> (shaper)
E }
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:535: in _capture_graph_and_evaluate_torch_script_evaluator
return _ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:306: in _ort_session_run
session = ort.InferenceSession(
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox\test\lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:462: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. Type Error: Type 'tensor(float)' of input parameter (input_0) of operator (aten_randn) in node (aten_randn_0) is invalid.
The above exception was the direct cause of the following exception:
onnxscript\tests\function_libs\torch_lib\ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript\tests\function_libs\torch_lib\ops_test_common.py:549: in _capture_graph_and_evaluate_torch_script_evaluator
raise RuntimeError(
E RuntimeError: ONNX Runtime failed to evaluate:
E Inputs:
E {'input_0': array([], shape=(0, 5, 0), dtype=float32)}
E Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
E producer_name: "pytorch",
E producer_version: "2.0.1"
E >
E torch_jit (float[0,5,0] input_0) => (float[0,5,0] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_randn <dtype = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E aten_randn (size) => (return_val)
E {
E shaper = ConstantOfShape (size)
E return_val = RandomNormalLike <dtype: int = @dtype> (shaper)
E }
onnxscript\tests\function_libs\torch_lib\ops_test.py:372: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_rand_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
RuntimeError: Trying to create tensor with negative dimension -2: [6, 7, -2, 8, -1, 1, -4, 5, 7, -6]
RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
[-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
[-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
[-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
[ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
Position: 0
Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
[ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
[-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
[-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
[ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
[[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
[-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
[ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
[-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
[-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
[[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
[ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
[-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
[-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
[-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
[[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
[-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
[ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
[-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
[ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
[[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
[ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
[ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
[ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
[-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
onnxscript\tests\function_libs\torch_lib\ops_test.py:210: in run_test_output_match
torch_output = op(*inputs, **cpu_sample.kwargs)
.nox\test\lib\site-packages\torch\testing\_internal\opinfo\core.py:1074: in __call__
return self.op(*args, **kwargs)
.nox\test\lib\site-packages\torch\_ops.py:502: in __call__
return self._op(*args, **kwargs or {})
E RuntimeError: Trying to create tensor with negative dimension -2: [6, 7, -2, 8, -1, 1, -4, 5, 7, -6]
onnxscript\tests\function_libs\torch_lib\ops_test.py:210: in run_test_output_match
torch_output = op(*inputs, **cpu_sample.kwargs)
.nox\test\lib\site-packages\torch\testing\_internal\opinfo\core.py:1074: in __call__
return self.op(*args, **kwargs)
.nox\test\lib\site-packages\torch\_ops.py:502: in __call__
return self._op(*args, **kwargs or {})
E RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[ 7.8228, 1.6844, 6.6493, 1.2189, 4.3397],
E [-1.2707, 6.9380, 1.3303, -4.2016, 2.2941],
E [-4.1466, -1.0555, -3.6554, 5.9703, -7.1043],
E [-4.1491, -2.5414, -5.4115, 0.8494, -8.8891],
E [ 8.1280, -7.6452, 6.9482, 1.4978, -2.9223]])
E Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
onnxscript\tests\function_libs\torch_lib\ops_test.py:210: in run_test_output_match
torch_output = op(*inputs, **cpu_sample.kwargs)
.nox\test\lib\site-packages\torch\testing\_internal\opinfo\core.py:1074: in __call__
return self.op(*args, **kwargs)
.nox\test\lib\site-packages\torch\_ops.py:502: in __call__
return self._op(*args, **kwargs or {})
E RuntimeError: Overloaded torch operator invoked from Python failed to many any schema:
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.names(SymInt[] size, *, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, str[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.names_out(SymInt[] size, *, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
E
E aten::rand() Expected a value of type 'List[int]' for argument 'size' but instead found type 'Tensor'.
E Position: 0
E Value: tensor([[[ 5.5616, 1.4027, 7.2717, 0.9839, -2.8384],
E [ 2.4182, -2.4406, 3.7877, 8.0354, 5.2025],
E [-3.9346, 5.1954, 1.6103, 4.5705, -5.4855],
E [-8.9092, -3.4772, -6.9032, 7.3848, 2.5923],
E [ 3.7279, 2.8464, -0.1566, 7.0435, -6.3946]],
E
E [[ 0.5667, -6.1429, 2.7752, -3.0994, 2.7577],
E [-1.8751, 7.4645, -5.3343, -5.3676, -5.3679],
E [ 8.0950, 2.9993, 8.6603, -7.4275, -8.9269],
E [-7.0413, -6.0542, 3.6454, 3.2227, 7.4783],
E [-4.6478, -6.1354, 4.7752, -3.6378, 5.4623]],
E
E [[-2.1357, 5.1484, -6.9927, -4.5418, 2.7439],
E [ 1.9027, -2.2946, 5.3646, 6.1183, -6.5266],
E [-4.8048, 8.2410, -3.0369, -3.1906, -8.7084],
E [-5.1540, 2.2482, -1.1879, -6.5330, 0.2111],
E [-6.1477, -7.6356, -4.9560, -7.8769, -5.7306]],
E
E [[ 8.9965, 1.6999, 2.7734, -8.3942, -5.9110],
E [-2.9957, 1.4073, -7.9193, -3.8779, -5.3880],
E [ 0.0249, -3.3489, -0.6237, -6.0987, -6.1776],
E [-5.2506, -3.0807, -7.1035, 7.5462, -1.7862],
E [ 7.7436, 2.8042, -7.6212, 6.2283, -2.4763]],
E
E [[-3.4499, -7.4706, -8.9474, 2.5750, -1.9660],
E [ 3.5039, -7.3860, 6.6819, -6.6065, -1.5541],
E [ 1.8798, 4.6463, 7.2658, 8.1986, -7.1363],
E [ 2.2650, -3.8711, -0.9863, -6.7364, 8.1977],
E [-6.6056, 4.8101, 3.1630, 2.9246, -4.8658]]])
E Declaration: aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, str[]? names, Tensor(a!) out) -> Tensor(a!)
E Cast error details: Unable to cast Python instance to C++ type (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_like_low_dtype_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:372: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:641: in sample_inputs_randint_like_low_dtype
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:372: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:605: in sample_inputs_randint
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_like_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:372: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:624: in sample_inputs_randint_like
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 18 runs failed: test_output_match_opinfo__ops_aten_randint_low_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-onnx-weekly-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-ort-nightly-windows-latest)/pytest.xml [took 0s]
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]
artifacts/Test Results (py310-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py38-windows-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py39-windows-latest)/pytest.xml [took 0s]
Raw output
ValueError: not enough values to unpack (expected 2, got 1)
onnxscript\tests\function_libs\torch_lib\ops_test.py:372: in test_output_match_opinfo_
run_test_output_match(
onnxscript\tests\function_libs\torch_lib\ops_test.py:188: in run_test_output_match
for i, cpu_sample in enumerate(samples):
.nox\test\lib\site-packages\torch\utils\_contextlib.py:56: in generator_context
response = gen.send(request)
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:614: in sample_inputs_randint_low
for sample in sample_inputs_like_fns(self, device, dtype, requires_grad, **kwargs):
onnxscript\tests\function_libs\torch_lib\extra_opinfo.py:579: in sample_inputs_like_fns
for shape, kwargs in inputs:
E ValueError: not enough values to unpack (expected 2, got 1)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__matmul_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 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 5 (20.0%)
Greatest absolute difference: 0.01171875 at index (0,) (up to 1e-05 allowed)
Greatest relative difference: 0.002384185791015625 at index (0,) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 2 / 25 (8.0%)
Greatest absolute difference: 0.03125 at index (2, 3) (up to 1e-05 allowed)
Greatest relative difference: 0.004360198974609375 at index (2, 3) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 38 / 250 (15.2%)
Greatest absolute difference: 0.0625 at index (0, 3, 9) (up to 1e-05 allowed)
Greatest relative difference: 0.0582275390625 at index (3, 4, 6) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 5 (20.0%)
E Greatest absolute difference: 0.01171875 at index (0,) (up to 1e-05 allowed)
E Greatest relative difference: 0.002384185791015625 at index (0,) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 2 / 25 (8.0%)
E Greatest absolute difference: 0.03125 at index (2, 3) (up to 1e-05 allowed)
E Greatest relative difference: 0.004360198974609375 at index (2, 3) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 38 / 250 (15.2%)
E Greatest absolute difference: 0.0625 at index (0, 3, 9) (up to 1e-05 allowed)
E Greatest relative difference: 0.0582275390625 at index (3, 4, 6) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__addbmm_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 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 9 / 50 (18.0%)
Greatest absolute difference: 0.09375 at index (2, 3) (up to 1e-05 allowed)
Greatest relative difference: 0.0338134765625 at index (4, 1) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 5 / 50 (10.0%)
Greatest absolute difference: 0.0625 at index (2, 7) (up to 1e-05 allowed)
Greatest relative difference: 0.01500701904296875 at index (1, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 6 / 50 (12.0%)
Greatest absolute difference: 0.0078125 at index (3, 7) (up to 1e-05 allowed)
Greatest relative difference: 0.00559234619140625 at index (0, 8) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 5 / 50 (10.0%)
Greatest absolute difference: 0.015625 at index (4, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.01526641845703125 at index (1, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 12 / 50 (24.0%)
Greatest absolute difference: 0.0625 at index (3, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.08935546875 at index (4, 7) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 7 / 50 (14.0%)
Greatest absolute difference: 0.013671875 at index (4, 5) (up to 1e-05 allowed)
Greatest relative difference: 0.00673675537109375 at index (2, 6) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 9 / 50 (18.0%)
E Greatest absolute difference: 0.09375 at index (2, 3) (up to 1e-05 allowed)
E Greatest relative difference: 0.0338134765625 at index (4, 1) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 5 / 50 (10.0%)
E Greatest absolute difference: 0.0625 at index (2, 7) (up to 1e-05 allowed)
E Greatest relative difference: 0.01500701904296875 at index (1, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 6 / 50 (12.0%)
E Greatest absolute difference: 0.0078125 at index (3, 7) (up to 1e-05 allowed)
E Greatest relative difference: 0.00559234619140625 at index (0, 8) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 5 / 50 (10.0%)
E Greatest absolute difference: 0.015625 at index (4, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.01526641845703125 at index (1, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 12 / 50 (24.0%)
E Greatest absolute difference: 0.0625 at index (3, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.08935546875 at index (4, 7) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 7 / 50 (14.0%)
E Greatest absolute difference: 0.013671875 at index (4, 5) (up to 1e-05 allowed)
E Greatest relative difference: 0.00673675537109375 at index (2, 6) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__addmv_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 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 5 (20.0%)
Greatest absolute difference: 0.01171875 at index (1,) (up to 1e-05 allowed)
Greatest relative difference: 0.0018596649169921875 at index (1,) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 5 (20.0%)
E Greatest absolute difference: 0.01171875 at index (1,) (up to 1e-05 allowed)
E Greatest relative difference: 0.0018596649169921875 at index (1,) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 9s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 5s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 6s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {-3}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {-3}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {0}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {0}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {1}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {1}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {4}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {4}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {50}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {50}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0, int64 input_1) => (float16[0] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {-3}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = int64 {0}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {-3}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16) {
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = int64 {0}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0, int64 input_1) => (float16[0] _val_16) {
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_…pture_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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[0] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_14): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16) {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[50] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {1}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[50] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_14): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[0] _val_16) {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[0] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {4}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[0] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_14): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16) {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[50] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {4}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[50] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_14): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[0] _val_16) {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[0] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {50}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[0] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_14): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16) {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[50] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {50}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_15): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[50] _val_16) {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:Add, node name: Add_14): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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: ["" : 18],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16) {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
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.TestOutputConsistencyFullGraphCPU)
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: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4) => (float16[3,2,4] _val_6, float16[0] _val_7, float16[0] _val_8) {
_val_5 = Constant <value_ints: ints = [0, 2]> ()
_val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 1e-05, momentum: float = -1.2, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
{
norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
tmp = Shape <end: int = 0, start: int = 0> (input)
empty_mean = Cast <to: int = 1> (tmp)
tmp_0 = Shape <end: int = 0, start: int = 0> (input)
empty_var = Cast <to: int = 1> (tmp_0)
}
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,3,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4) => (float16[3,2,3,4] _val_6, float16[0] _val_7, float16[0] _val_8) {
_val_5 = Constant <value_ints: ints = [0, 2, 3]> ()
_val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 0.5, momentum: float = -1, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
{
norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
tmp = Shape <end: int = 0, start: int = 0> (input)
empty_mean = Cast <to: int = 1> (tmp)
tmp_0 = Shape <end: int = 0, start: int = 0> (input)
empty_var = Cast <to: int = 1> (tmp_0)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[0] _val_7, float16[0] _val_8) {
_val_5 = Constant <value_ints: ints = [0]> ()
_val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
{
norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
tmp = Shape <end: int = 0, start: int = 0> (input)
empty_mean = Cast <to: int = 1> (tmp)
tmp_0 = Shape <end: int = 0, start: int = 0> (input)
empty_var = Cast <to: int = 1> (tmp_0)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[0] _val_7, float16[0] _val_8) {
_val_5 = Constant <value_ints: ints = [0]> ()
_val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
{
norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
tmp = Shape <end: int = 0, start: int = 0> (input)
empty_mean = Cast <to: int = 1> (tmp)
tmp_0 = Shape <end: int = 0, start: int = 0> (input)
empty_var = Cast <to: int = 1> (tmp_0)
}
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: 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:281: 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_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:_aten_native_batch_norm_inference_onnx, node name: _aten_native_batch_norm_inference_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4) => (float16[3,2,4] _val_6, float16[0] _val_7, float16[0] _val_8) {
E _val_5 = Constant <value_ints: ints = [0, 2]> ()
E _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 1e-05, momentum: float = -1.2, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
E {
E norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E tmp = Shape <end: int = 0, start: int = 0> (input)
E empty_mean = Cast <to: int = 1> (tmp)
E tmp_0 = Shape <end: int = 0, start: int = 0> (input)
E empty_var = Cast <to: int = 1> (tmp_0)
E }
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: 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:281: 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:267: 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:281: 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_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:_aten_native_batch_norm_inference_onnx, node name: _aten_native_batch_norm_inference_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,3,4] input_0, float16[2] input_1, float16[2] input_2, float16[2] input_3, float16[2] input_4) => (float16[3,2,3,4] _val_6, float16[0] _val_7, float16[0] _val_8) {
E _val_5 = Constant <value_ints: ints = [0, 2, 3]> ()
E _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 0.5, momentum: float = -1, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
E {
E norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E tmp = Shape <end: int = 0, start: int = 0> (input)
E empty_mean = Cast <to: int = 1> (tmp)
E tmp_0 = Shape <end: int = 0, start: int = 0> (input)
E empty_var = Cast <to: int = 1> (tmp_0)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:_aten_native_batch_norm_inference_onnx, node name: _aten_native_batch_norm_inference_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[0] _val_7, float16[0] _val_8) {
E _val_5 = Constant <value_ints: ints = [0]> ()
E _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
E {
E norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E tmp = Shape <end: int = 0, start: int = 0> (input)
E empty_mean = Cast <to: int = 1> (tmp)
E tmp_0 = Shape <end: int = 0, start: int = 0> (input)
E empty_var = Cast <to: int = 1> (tmp_0)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:521: 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:148: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] (op_type:_aten_native_batch_norm_inference_onnx, node name: _aten_native_batch_norm_inference_onnx_1): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:230: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:523: 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],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[2,1] input_0, float16[1] input_1, float16[1] input_2, float16[1] input_3, float16[1] input_4) => (float16[2,1] _val_6, float16[0] _val_7, float16[0] _val_8) {
E _val_5 = Constant <value_ints: ints = [0]> ()
E _val_6, _val_7, _val_8 = pkg.onnxscript.torch_lib._aten_native_batch_norm_inference_onnx <eps: float = 1e-05, momentum: float = 0.5, training: int = 0> (input_0, input_1, input_2, input_3, input_4)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_native_batch_norm_inference_onnx <training,momentum,eps>(input, weight, bias, running_mean, running_var) => (norm, empty_mean, empty_var)
E {
E norm = BatchNormalization <epsilon: float = @eps, momentum: float = @momentum, training_mode: int = @training> (input, weight, bias, running_mean, running_var)
E tmp = Shape <end: int = 0, start: int = 0> (input)
E empty_mean = Cast <to: int = 1> (tmp)
E tmp_0 = Shape <end: int = 0, start: int = 0> (input)
E empty_var = Cast <to: int = 1> (tmp_0)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_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 7s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 43 / 50 (86.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: 1.0 at index (7,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 43 / 50 (86.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: 1.0 at index (7,)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__addbmm_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: Tensor-likes are not close!
Mismatched elements: 15 / 50 (30.0%)
Greatest absolute difference: 0.125 at index (1, 8) (up to 1e-05 allowed)
Greatest relative difference: 0.013641357421875 at index (4, 1) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 9 / 50 (18.0%)
Greatest absolute difference: 0.125 at index (1, 4) (up to 1e-05 allowed)
Greatest relative difference: 0.038482666015625 at index (1, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 10 / 50 (20.0%)
Greatest absolute difference: 0.03125 at index (1, 5) (up to 1e-05 allowed)
Greatest relative difference: 0.0104827880859375 at index (0, 8) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 10 / 50 (20.0%)
Greatest absolute difference: 0.0234375 at index (4, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.02459716796875 at index (1, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 13 / 50 (26.0%)
Greatest absolute difference: 0.125 at index (2, 6) (up to 1e-05 allowed)
Greatest relative difference: 0.0292816162109375 at index (4, 7) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 12 / 50 (24.0%)
Greatest absolute difference: 0.01171875 at index (1, 1) (up to 1e-05 allowed)
Greatest relative difference: 0.041351318359375 at index (0, 7) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 15 / 50 (30.0%)
E Greatest absolute difference: 0.125 at index (1, 8) (up to 1e-05 allowed)
E Greatest relative difference: 0.013641357421875 at index (4, 1) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 9 / 50 (18.0%)
E Greatest absolute difference: 0.125 at index (1, 4) (up to 1e-05 allowed)
E Greatest relative difference: 0.038482666015625 at index (1, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 10 / 50 (20.0%)
E Greatest absolute difference: 0.03125 at index (1, 5) (up to 1e-05 allowed)
E Greatest relative difference: 0.0104827880859375 at index (0, 8) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 10 / 50 (20.0%)
E Greatest absolute difference: 0.0234375 at index (4, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.02459716796875 at index (1, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 13 / 50 (26.0%)
E Greatest absolute difference: 0.125 at index (2, 6) (up to 1e-05 allowed)
E Greatest relative difference: 0.0292816162109375 at index (4, 7) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 12 / 50 (24.0%)
E Greatest absolute difference: 0.01171875 at index (1, 1) (up to 1e-05 allowed)
E Greatest relative difference: 0.041351318359375 at index (0, 7) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 2s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 8s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 43 / 50 (86.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: 1.0 at index (7,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 43 / 50 (86.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: 1.0 at index (7,)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__baddbmm_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: Tensor-likes are not close!
Mismatched elements: 2 / 250 (0.8%)
Greatest absolute difference: 0.001953125 at index (0, 3, 0) (up to 1e-05 allowed)
Greatest relative difference: 0.006011962890625 at index (3, 4, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 2 / 250 (0.8%)
Greatest absolute difference: 0.001953125 at index (1, 0, 5) (up to 1e-05 allowed)
Greatest relative difference: 0.0017490386962890625 at index (3, 0, 0) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 3 / 250 (1.2%)
Greatest absolute difference: 0.03125 at index (1, 2, 1) (up to 1e-05 allowed)
Greatest relative difference: 0.0034923553466796875 at index (0, 0, 5) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 3 / 250 (1.2%)
Greatest absolute difference: 0.001953125 at index (0, 1, 9) (up to 1e-05 allowed)
Greatest relative difference: 0.01074981689453125 at index (2, 0, 5) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 250 (0.4%)
Greatest absolute difference: 0.015625 at index (3, 1, 8) (up to 1e-05 allowed)
Greatest relative difference: 0.001049041748046875 at index (3, 1, 8) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 2 / 250 (0.8%)
E Greatest absolute difference: 0.001953125 at index (0, 3, 0) (up to 1e-05 allowed)
E Greatest relative difference: 0.006011962890625 at index (3, 4, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 2 / 250 (0.8%)
E Greatest absolute difference: 0.001953125 at index (1, 0, 5) (up to 1e-05 allowed)
E Greatest relative difference: 0.0017490386962890625 at index (3, 0, 0) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 3 / 250 (1.2%)
E Greatest absolute difference: 0.03125 at index (1, 2, 1) (up to 1e-05 allowed)
E Greatest relative difference: 0.0034923553466796875 at index (0, 0, 5) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 3 / 250 (1.2%)
E Greatest absolute difference: 0.001953125 at index (0, 1, 9) (up to 1e-05 allowed)
E Greatest relative difference: 0.01074981689453125 at index (2, 0, 5) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 250 (0.4%)
E Greatest absolute difference: 0.015625 at index (3, 1, 8) (up to 1e-05 allowed)
E Greatest relative difference: 0.001049041748046875 at index (3, 1, 8) (up to 0.001 allowed)
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 1 mismatch
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: 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:281: 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:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:281: in run_test_output_match
raise AssertionError(f"Output {j} mismatch") from e
E AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: 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:281: 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:267: 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:281: 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:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:281: in run_test_output_match
raise AssertionError(f"Output {j} mismatch") from e
E AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:281: in run_test_output_match
raise AssertionError(f"Output {j} mismatch") from e
E AssertionError: Output 1 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:281: in run_test_output_match
raise AssertionError(f"Output {j} mismatch") from e
E AssertionError: Output 1 mismatch
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_int32 (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 2s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 4s]
Raw output
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 43 / 50 (86.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: 1.0 at index (7,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 43 / 50 (86.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: 1.0 at index (7,)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__addmm_decomposed_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: Tensor-likes are not close!
Mismatched elements: 1 / 6 (16.7%)
Greatest absolute difference: 0.00146484375 at index (0, 1) (up to 1e-05 allowed)
Greatest relative difference: 0.0030841827392578125 at index (0, 1) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 6 (16.7%)
E Greatest absolute difference: 0.00146484375 at index (0, 1) (up to 1e-05 allowed)
E Greatest relative difference: 0.0030841827392578125 at index (0, 1) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_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 6s]
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: 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:267: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
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