This method has to be accessible from `c10` to enable CUDA-12 integration.
Implemented by providing private `c10::cuda:_internal::setHasPrimaryContext` that passes the pointer to the implementation (in `torch_cuda`) back to c10.
Use global class constructor/destructor to guarantee RAII.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96800
Approved by: https://github.com/ngimel
Summary:
This creates `torch.cuda.set_warn_on_synchronization()` function that would warn or error when synchronizing operation is performed. We could wrap it in a context manager for ease of use, but it would be a lie, because it sets global, and not thread-local state. Since it's intended for debugging, maybe that's ok though.
As all `torch.cuda.*` functions, it's going through CPython, not pybind, so the argument is converted to long before being passed to c10 function. I'll make python argument a python enum class, but without pybind it'll still have to go thourgh long conversion.
For a test script
```
import torch
torch.cuda.set_warn_on_synchronization(1)
x=torch.randn(10, device="cuda")
x.nonzero()
y=torch.randn((), device="cuda")
if y:
print("something")
torch.multinomial(x.abs(), 10, replacement=False)
torch.randperm(20000, device="cuda")
ind = torch.randint(10, (3,), device="cuda")
mask = torch.randint(2, (10,), device="cuda", dtype=torch.bool)
val = torch.randn((), device="cuda")
x[mask]=1.
x[mask] = val
torch.cuda.synchronize()
```
the output is
```
/../playground/sync_warn_test.py:4: UserWarning: called a synchronizing operation (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:145.)
x.nonzero()
/../playground/sync_warn_test.py:7: UserWarning: called a synchronizing operation (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:145.)
if y:
something
/../playground/sync_warn_test.py:9: UserWarning: called a synchronizing operation (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:145.)
torch.multinomial(x.abs(), 10, replacement=False)
/../playground/sync_warn_test.py:15: UserWarning: called a synchronizing operation (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:145.)
x[mask] = val
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62092
Reviewed By: mruberry
Differential Revision: D29968792
Pulled By: ngimel
fbshipit-source-id: cc6f817212c164727ed99ecf6ab050dc29631b9e
Summary:
This is a first step towards creating context manager that errors out on synchronizing calls.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61889
Reviewed By: albanD
Differential Revision: D29805280
Pulled By: ngimel
fbshipit-source-id: b66400fbe0941b7daa51e6b30abe27b9cccd4e8a
Summary:
After the change async error warnings look as follows:
```
$ python -c "import torch;torch.eye(3,3,device='cuda:777')"
Traceback (most recent call last):
File "<string>", line 1, in <module>
RuntimeError: CUDA error: invalid device ordinal
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59467
Reviewed By: ngimel
Differential Revision: D28904360
Pulled By: malfet
fbshipit-source-id: 2a8fa5affed5b4ffcaa602c8ab2669061cde7db0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57609
Throw c10::CudaError for CUDA Exceptions for better classification of errors
Test Plan: Test locally by running some workflows
Reviewed By: dzhulgakov
Differential Revision: D28209356
fbshipit-source-id: 19a5fc8548433238dc224ea81a5f63a945fc5cc3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56830
Opt into formatting on GitHub and format everything. This is a trial run before turning on formatting for more and eventually all of the codebase.
Test Plan: CI
Reviewed By: zertosh
Differential Revision: D27979080
fbshipit-source-id: a80f0c48691c08ae8ca0af06377b87e6a2351151
Summary:
It frequently happens when PyTorch compiled with CUDA support is installed on machine that does not have NVIDIA GPUs.
Fixes https://github.com/pytorch/pytorch/issues/47038
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51806
Reviewed By: ezyang
Differential Revision: D26285827
Pulled By: malfet
fbshipit-source-id: 9fd5e690d0135a2b219c1afa803fb69de9729f5e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47023
DeviceType pretty clearly only needs 1 byte. DeviceIndex only needs 1 byte given that machines don't have anywhere near 255 GPUs in them as far as I know.
ghstack-source-id: 116901430
Test Plan: Existing tests, added assertion to catch if my assumption about DeviceIndex is incorrect
Reviewed By: dzhulgakov
Differential Revision: D24605460
fbshipit-source-id: 7c9a89027fcf8eebd623b7cdbf6302162c981cd2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42249
Main change is to bring Caffe2's superior error messages for cuda initialization into c10 and use them in all code paths.
Basic logic:
| Case | Call to device_count() | init_cuda, e.g. allocating tensor |
| -- | -- | -- |
| all good | non-zero | just works |
| no gpus | 0, no warning | throw exception with good message |
| driver issues | 0, produce warning | throw exception with good message |
| out of memory with ASAN | 0, produce warning| throw exception with ASAN message |
Previously, the error thrown from init_cuda was very generic and the ASAN warning (if any) was buried in the logs.
Other clean up changes:
* cache device_count() always in a static variable
* move all asan macros in c10
Test Plan:
Hard to unittest because of build modes. Verified manually that the behavior from the table above holds by running the following script in different modes (ASAN/no-ASAN, CUDA_VISIBLE_DEVICES=):
```
print('before import')
import torch
print('after import')
print('devices: ', torch.cuda.device_count())
x = torch.tensor([1,2,3])
print('tensor creation')
x = x.cuda()
print('moved to cuda')
```
Reviewed By: ngimel
Differential Revision: D22824329
fbshipit-source-id: 5314007313a3897fc955b02f8b21b661ae35fdf5
Summary:
* Make c10::cuda functions regular non-inlined functions
* Add driver_version() and device_synchronize() functions
With this change I don't see anymore direct calls to CUDA API when look at Modules.cpp.obj
FYI malfet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42251
Reviewed By: malfet
Differential Revision: D22826505
Pulled By: ziab
fbshipit-source-id: 8dc2f3e209d3710e2ce78411982a10e8c727573c
