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## main #148 +/- ##
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- Coverage 90.58% 85.69% -4.90%
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Files 59 59
Lines 4440 4446 +6
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- Hits 4022 3810 -212
- Misses 418 636 +218
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Summary: Pull Request resolved: meta-pytorch#148 Attempt to fix torchsnapshot CI: https://github.com/pytorch/torchsnapshot/actions/runs/5766115388/job/15694536972 ``` tests/test_uvm_tensor.py::test_uvm_tensor FAILED [100%] =================================== FAILURES =================================== _______________________________ test_uvm_tensor ________________________________ pytest.mark.cpu_and_gpu def test_uvm_tensor() -> None: if torch.cuda.is_available() and _UVM_TENSOR_AVAILABLE: uvm_tensor = torch.rand( (64, 64), > out=new_managed_tensor( torch.empty(0, dtype=torch.float32, device="cuda:0"), [64, 64], ), ) tests/test_uvm_tensor.py:25: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <OpOverloadPacket(op='fbgemm.new_managed_tensor')> args = (tensor([], device='cuda:0'), [64, 64]), kwargs = {} def __call__(self, *args, **kwargs): # overloading __call__ to ensure torch.ops.foo.bar() # is still callable from JIT # We save the function ptr as the `op` attribute on # OpOverloadPacket to access it here. > return self._op(*args, **kwargs or {}) E RuntimeError: CUDA error: invalid device ordinal E CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. E For debugging consider passing CUDA_LAUNCH_BLOCKING=1. E Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. ``` Differential Revision: D48135206 fbshipit-source-id: 77ed51cd66efd98fd485c3bbb0cd20216fb294a9
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Summary: Pull Request resolved: meta-pytorch#148 Attempt to fix torchsnapshot CI: https://github.com/pytorch/torchsnapshot/actions/runs/5766115388/job/15694536972 ``` tests/test_uvm_tensor.py::test_uvm_tensor FAILED [100%] =================================== FAILURES =================================== _______________________________ test_uvm_tensor ________________________________ pytest.mark.cpu_and_gpu def test_uvm_tensor() -> None: if torch.cuda.is_available() and _UVM_TENSOR_AVAILABLE: uvm_tensor = torch.rand( (64, 64), > out=new_managed_tensor( torch.empty(0, dtype=torch.float32, device="cuda:0"), [64, 64], ), ) tests/test_uvm_tensor.py:25: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <OpOverloadPacket(op='fbgemm.new_managed_tensor')> args = (tensor([], device='cuda:0'), [64, 64]), kwargs = {} def __call__(self, *args, **kwargs): # overloading __call__ to ensure torch.ops.foo.bar() # is still callable from JIT # We save the function ptr as the `op` attribute on # OpOverloadPacket to access it here. > return self._op(*args, **kwargs or {}) E RuntimeError: CUDA error: invalid device ordinal E CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. E For debugging consider passing CUDA_LAUNCH_BLOCKING=1. E Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. ``` Differential Revision: D48135206 fbshipit-source-id: 1b62888ca317d4ce84f8cefc95d8f83fc27da6c4
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Summary: Pull Request resolved: meta-pytorch#148 Attempt to fix torchsnapshot CI: https://github.com/pytorch/torchsnapshot/actions/runs/5766115388/job/15694536972 ``` tests/test_uvm_tensor.py::test_uvm_tensor FAILED [100%] =================================== FAILURES =================================== _______________________________ test_uvm_tensor ________________________________ pytest.mark.cpu_and_gpu def test_uvm_tensor() -> None: if torch.cuda.is_available() and _UVM_TENSOR_AVAILABLE: uvm_tensor = torch.rand( (64, 64), > out=new_managed_tensor( torch.empty(0, dtype=torch.float32, device="cuda:0"), [64, 64], ), ) tests/test_uvm_tensor.py:25: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <OpOverloadPacket(op='fbgemm.new_managed_tensor')> args = (tensor([], device='cuda:0'), [64, 64]), kwargs = {} def __call__(self, *args, **kwargs): # overloading __call__ to ensure torch.ops.foo.bar() # is still callable from JIT # We save the function ptr as the `op` attribute on # OpOverloadPacket to access it here. > return self._op(*args, **kwargs or {}) E RuntimeError: CUDA error: invalid device ordinal E CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. E For debugging consider passing CUDA_LAUNCH_BLOCKING=1. E Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. ``` Differential Revision: D48135206 fbshipit-source-id: f3e3006c940026f7cfc5176ed611faba21683faf
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Summary: Pull Request resolved: meta-pytorch#148 Attempt to fix torchsnapshot CI: https://github.com/pytorch/torchsnapshot/actions/runs/5766115388/job/15694536972 ``` tests/test_uvm_tensor.py::test_uvm_tensor FAILED [100%] =================================== FAILURES =================================== _______________________________ test_uvm_tensor ________________________________ pytest.mark.cpu_and_gpu def test_uvm_tensor() -> None: if torch.cuda.is_available() and _UVM_TENSOR_AVAILABLE: uvm_tensor = torch.rand( (64, 64), > out=new_managed_tensor( torch.empty(0, dtype=torch.float32, device="cuda:0"), [64, 64], ), ) tests/test_uvm_tensor.py:25: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <OpOverloadPacket(op='fbgemm.new_managed_tensor')> args = (tensor([], device='cuda:0'), [64, 64]), kwargs = {} def __call__(self, *args, **kwargs): # overloading __call__ to ensure torch.ops.foo.bar() # is still callable from JIT # We save the function ptr as the `op` attribute on # OpOverloadPacket to access it here. > return self._op(*args, **kwargs or {}) E RuntimeError: CUDA error: invalid device ordinal E CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. E For debugging consider passing CUDA_LAUNCH_BLOCKING=1. E Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. ``` Differential Revision: D48135206 fbshipit-source-id: 46cbe45ebc8e135740b9d752abc2c1a2a1042cc9
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Summary: Pull Request resolved: meta-pytorch#148 Attempt to fix torchsnapshot CI: https://github.com/pytorch/torchsnapshot/actions/runs/5766115388/job/15694536972 ``` tests/test_uvm_tensor.py::test_uvm_tensor FAILED [100%] =================================== FAILURES =================================== _______________________________ test_uvm_tensor ________________________________ pytest.mark.cpu_and_gpu def test_uvm_tensor() -> None: if torch.cuda.is_available() and _UVM_TENSOR_AVAILABLE: uvm_tensor = torch.rand( (64, 64), > out=new_managed_tensor( torch.empty(0, dtype=torch.float32, device="cuda:0"), [64, 64], ), ) tests/test_uvm_tensor.py:25: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <OpOverloadPacket(op='fbgemm.new_managed_tensor')> args = (tensor([], device='cuda:0'), [64, 64]), kwargs = {} def __call__(self, *args, **kwargs): # overloading __call__ to ensure torch.ops.foo.bar() # is still callable from JIT # We save the function ptr as the `op` attribute on # OpOverloadPacket to access it here. > return self._op(*args, **kwargs or {}) E RuntimeError: CUDA error: invalid device ordinal E CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. E For debugging consider passing CUDA_LAUNCH_BLOCKING=1. E Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. ``` Differential Revision: D48135206 fbshipit-source-id: 15d5cb361416a0e890278ced430d00d4b9dfb6f2
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Summary: Pull Request resolved: meta-pytorch#148 Attempt to fix torchsnapshot CI: https://github.com/pytorch/torchsnapshot/actions/runs/5766115388/job/15694536972 ``` tests/test_uvm_tensor.py::test_uvm_tensor FAILED [100%] =================================== FAILURES =================================== _______________________________ test_uvm_tensor ________________________________ pytest.mark.cpu_and_gpu def test_uvm_tensor() -> None: if torch.cuda.is_available() and _UVM_TENSOR_AVAILABLE: uvm_tensor = torch.rand( (64, 64), > out=new_managed_tensor( torch.empty(0, dtype=torch.float32, device="cuda:0"), [64, 64], ), ) tests/test_uvm_tensor.py:25: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <OpOverloadPacket(op='fbgemm.new_managed_tensor')> args = (tensor([], device='cuda:0'), [64, 64]), kwargs = {} def __call__(self, *args, **kwargs): # overloading __call__ to ensure torch.ops.foo.bar() # is still callable from JIT # We save the function ptr as the `op` attribute on # OpOverloadPacket to access it here. > return self._op(*args, **kwargs or {}) E RuntimeError: CUDA error: invalid device ordinal E CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. E For debugging consider passing CUDA_LAUNCH_BLOCKING=1. E Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. ``` Differential Revision: D48135206 fbshipit-source-id: 5f75bb830e3cb9057b5803fe09d83e391c60a365
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Summary:
Attempt to fix torchsnapshot CI: https://github.com/pytorch/torchsnapshot/actions/runs/5766115388/job/15694536972
Differential Revision: D48135206