This functionality does not seem to be used
and there are some requests to update dependency.
Add `third_party` to torch_cpu include directories if compiling with
Caffe2 support, as `caffe2/quantization/server/conv_dnnlowp_op.cc` depends on `third_party/fbgemm/src/RefImplementations.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75394
Approved by: https://github.com/janeyx99, https://github.com/seemethere
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55647
This adds [breakpad](https://github.com/google/breakpad) which comes with out-of-the-box utilities to register a signal handler that writes out a minidump on an unhandled exception. Right now this is gated behind a flag in `torch.utils`, but in the future it could be on by default. Sizewise this adds aboute 500k to `libtorch_cpu.so` (187275968 B to 187810016 B).
```bash
$ cat <<EOF > test.py
import torch
torch.utils.enable_minidump_collection()
# temporary util that just segfaults
torch._C._crash()
EOF
$ python test.py
Wrote minidump to /tmp/pytorch_crashes/6a829041-50e9-4247-ea992f99-a74cf47a.dmp
fish: “python test.py” terminated by signal SIGSEGV (Address boundary error)
$ minidump-2-core /tmp/pytorch_crashes/6a829041-50e9-4247-ea992f99-a74cf47a.dmp -o core.dmp
$ gdb python core.dmp
... commence debugging ...
```
Right now all exceptions that get passed up to Python don't trigger the signal handler (which by default only
handles [these](https://github.com/google/breakpad/blob/main/src/client/linux/handler/exception_handler.cc#L115)). It would be possible for PyTorch exceptions to explicitly write a minidump when passed up to Python (maybe only when the exception is unhandled or something).
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D27679767
Pulled By: driazati
fbshipit-source-id: 1ab3b5160b6dc405f5097eb25acc644d533358d7
Summary:
This is useful for measuring inference performance of your
models. This is a very basic benchmark for now. We don't support
batching on the benchmark side, no inter and intra op parallelizm is
supported yet, just caller based parallelizm.
Main phylosophy here is that user should be able to provide inputs
from python and just stack them within the benchmark. API should be
exactly the same as passing inputs to module.forward.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20766
Test Plan: Added a new unit test
Differential Revision: D15435461
Pulled By: salexspb
fbshipit-source-id: db08829dc3f4398bb1d8aa16cc4a58b6c72f16c6
