# Motivation
Fix https://github.com/pytorch/pytorch/issues/143543
# Solution
We should raise python exception instead of aborting...
# Additional Context
without this PR:
```python
>>> import torch
>>> torch.accelerator.current_stream(torch.accelerator.device_count())
terminate called after throwing an instance of 'c10::Error'
what(): device is out of range, device is 2, total number of device is 2.
Exception raised from check_device_index at /home/dvrogozh/git/pytorch/pytorch/c10/xpu/XPUFunctions.h:36 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0xac (0x7f30707eb95c in /home/dvrogozh/git/pytorch/pytorch/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xf3 (0x7f307078fc57 in /home/dvrogozh/git/pytorch/pytorch/torch/lib/libc10.so)
frame #2: <unknown function> + 0x19a3e (0x7f3070c2ba3e in /home/dvrogozh/git/pytorch/pytorch/torch/lib/libc10_xpu.so)
frame #3: c10::xpu::getCurrentXPUStream(signed char) + 0x2f (0x7f3070c2c83f in /home/dvrogozh/git/pytorch/pytorch/torch/lib/libc10_xpu.so)
frame #4: <unknown function> + 0x1ca35 (0x7f3070c2ea35 in /home/dvrogozh/git/pytorch/pytorch/torch/lib/libc10_xpu.so)
frame #5: <unknown function> + 0x653f15 (0x7f3083391f15 in /home/dvrogozh/git/pytorch/pytorch/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x39e5f2 (0x7f30830dc5f2 in /home/dvrogozh/git/pytorch/pytorch/torch/lib/libtorch_python.so)
<omitting python frames>
frame #20: <unknown function> + 0x29d90 (0x7f308b19bd90 in /lib/x86_64-linux-gnu/libc.so.6)
frame #21: __libc_start_main + 0x80 (0x7f308b19be40 in /lib/x86_64-linux-gnu/libc.so.6)
Aborted (core dumped)
```
with this PR:
```python
>>> import torch
>>> torch.accelerator.current_stream(torch.accelerator.device_count())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/pt-gpu/4T-4652/guangyey/stock-pytorch/torch/accelerator/__init__.py", line 123, in current_stream
return torch._C._accelerator_getStream(device_index)
RuntimeError: The device index is out of range. It must be in [0, 2), but got 2.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143550
Approved by: https://github.com/EikanWang, https://github.com/dvrogozh, https://github.com/albanD
Otherwise certain sequences of tests will fail with OOM e.g.,
```
# python test/test_cuda.py -k max_split_expandable -k test_assigning_back_deleter_fns_to_tensor --repeat 100 .. ---------------------------------------------------------------------- Ran 2 tests in 0.311s OK E. ====================================================================== ERROR: test_assigning_back_deleter_fns_to_tensor (__main__.TestBlockStateAbsorption.test_assigning_back_deleter_fns_to_tensor)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/workspace/pytorch/torch/testing/_internal/common_utils.py", line 3058, in wrapper
method(*args, **kwargs)
File "/workspace/pytorch/test/test_cuda.py", line 4320, in test_assigning_back_deleter_fns_to_tensor
graph, outputs = cudagraphify(foo, [inp])
^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/pytorch/test/test_cuda.py", line 4080, in cudagraphify
fn(*inputs)
File "/workspace/pytorch/test/test_cuda.py", line 4316, in foo
int8_cuda(LARGE_BUFFER) + x,
~~~~~~~~~~~~~~~~~~~~~~~~^~~
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 160.00 MiB. GPU 0 has a total capacity of 31.73 GiB of which 31.30 GiB is free. Process 2916661 has 442.00 MiB memory in use. 120.00 MiB allowed; Of the allocated memory 52.00 MiB is allocated by PyTorch, and 6.00 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
To execute this test, run the following from the base repo dir:
python test/test_cuda.py TestBlockStateAbsorption.test_assigning_back_deleter_fns_to_tensor
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
----------------------------------------------------------------------
Ran 2 tests in 0.136s
FAILED (errors=1)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140852
Approved by: https://github.com/Skylion007
Certain `cpp_wrapper`-enabled tests were OOM-ing in the CI pipeline, with error messages suggesting that sufficient memory was accessible. This ultimately resulted from an internal memory limitation that was not queryable in the API. This PR adds querying for that limit.
Additionally, the failing tests had incorrect memory availability checks, and are updated with measured memory requirements.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140620
Approved by: https://github.com/malfet, https://github.com/eqy
ghstack dependencies: #141367
Canonically, the empty_cache API releases all cached blocks of the CUDACachingAllocator. There is no API that can release only the cached blocks of a given pool.
In this PR, we extend the functionality of empty_cache API such that it only releases the cached blocks of an active pool. When empty_cache API is called under a MemPoolContext, we only release the cached blocks that correspond to the pool id of the active pool.
Part of https://github.com/pytorch/pytorch/issues/124807.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133602
Approved by: https://github.com/ezyang
Canonically, the snapshot API returns the entire memory state of the CUDACachingAllocator (using `get_all_blocks`). There is no API that can only return the memory state of a given pool.
In this PR, we extend the functionality of snapshot API such that it can only return the memory addresses of an active pool. When snapshot API is called under a MemPoolContext, we only return the blocks that correspond to the pool id of the active pool.
Part of https://github.com/pytorch/pytorch/issues/124807.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133601
Approved by: https://github.com/ezyang
Canonically, the snapshot API returns the entire memory state of the CUDACachingAllocator (using `get_all_blocks`). There is no API that can only return the memory state of a given pool.
In this PR, we extend the functionality of snapshot API such that it can only return the memory addresses of an active pool. When snapshot API is called under a MemPoolContext, we only return the blocks that correspond to the pool id of the active pool.
Part of https://github.com/pytorch/pytorch/issues/124807.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133601
Approved by: https://github.com/ezyang
This PR refactors some ref-counting functionality out of `beginAllocateToPool` and `releasePool`. The ref-counting logic is then used in construction and destruction of `torch.cuda.MemPool`.
The `use_count` variable in the CUDACachingAllocator is essentially a refcount of how many context managers are using the pool. Since we are now lifting up the MemPool abstraction to the user, the MemPool object itself now needs to hold a an extra reference as well.
Part of https://github.com/pytorch/pytorch/issues/124807.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133600
Approved by: https://github.com/eqy, https://github.com/ezyang
# Motivation
This PR intends to make device-specific Event inherit from the generic torch.Event. The benefit is providing a generic abstract class `torch.Event` for different devices, like `torch.Stream`. This make it easier for Dynamo to capture the Event of different devices, like torch.cuda.Event and torch.xpu.Event.
And the next PR would like to remove previous useless base class `_StreamBase` and `_EventBase` to avoid multiple Inheritance.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134845
Approved by: https://github.com/albanD, https://github.com/EikanWang
Seems like some other tests are holding onto memory that is not gc'able (e.g., cuBLAS workspaces), so these tests while working in isolation fail when run as e.g., `python test/test_cuda.py -k able`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136496
Approved by: https://github.com/ezyang
Summary: Fixed a bunch of fbcode imports that happened to work but confused autodeps. After this autodeps still suggests "improvements" to TARGETS (which breaks our builds) but at least it can find all the imports.
Test Plan:
```
fbpython fbcode/tools/build/buck/linters/lint_autoformat.py --linter=autodeps --default-exec-timeout=1800 -- fbcode/caffe2/TARGETS fbcode/caffe2/test/TARGETS
```
Before:
```
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/testing.py:229) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fbur$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export.py:87) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_serdes.py:9) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fb$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_serdes.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_retraceability.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https:$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_retraceability.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See ht$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_nonstrict.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See http$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_nonstrict.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:8) when processing rule "test_export". Please make sure it's listed in the srcs parameter of an$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Found "//python/typeshed_internal:typeshed_internal_library" owner for "cv2" but it is protected by visibility rules: [] (from caffe2/test/test_bundled_images.py:7) when processing rule "test_bundled_$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "caffe2.test.profiler_test_cpp_thread_lib" (from caffe2/test/profiler/test_cpp_thread.py:29) when processing rule "profiler_test_cpp_thread". Please make sure it's listed in t$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_custom_ops.py:23) when processing rule "custom_ops". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_public_bindings.py:13) when processing rule "public_bindings". Please make sure it's listed in the srcs paramete$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.symbolize_tracebacks" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.gather_traceback" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another rule$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for include <torch/csrc/autograd/profiler_kineto.h> (from caffe2/test/profiler/test_cpp_thread.cpp:2) when processing profiler_test_cpp_thread_lib. Some things to try:
```
Differential Revision: D62049222
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135614
Approved by: https://github.com/oulgen, https://github.com/laithsakka
Previously setting garbage_collection_threshold or max_split_size_mb along with expandable_segments:True could cause the allocator to hit assert failures when running nearly out of memory. This PR ensures garbage_collection and max_split freeing do not accidentally try to release expandable segments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134338
Approved by: https://github.com/ezyang
