Because Clang-tidy 19 has more powerful clang-analyzer checks to detect subtle bugs. New checks such as misc-use-internal-linkage can help identify potential static variables or functions, thus reducing binary sizes.
Some new checks are disabled temporarily for later enabling. Additional warnings have been fixed or suppressed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148648
Approved by: https://github.com/Skylion007
Summary: Currently we process events in the regular allocation path and we call cudaEventQuery to check on the events and this path can take some locks in libcuda driver. Its not entirely needed to do process events in the allocation path, we could move this to a background thread and keep processing events regularly and put the freed block to the free list.
Differential Revision: D62396585
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135524
Approved by: https://github.com/zyan0
Summary:
This diff adds an option to round the non-split blocks in caching allocator so that they can be reused without causing lots of fragmentation for large memory segments.
For example, if we specify max_split memory size as 400MB, then all allocations more than 400MB will not be split. Lets say, we allocated some 1024MB blocks and these are cached in the allocator blocks. If we request a new 500MB block, we round it to nearest power-2-division, thats 512MB, we add default kLargeBuffer of 20MB, that will be 532MB and since 532MB is less than existing 1024MB block, the 1024MB will not be used for this allocation, instead a new 512MB block will be created. In this diff, we provide an option to cofigure the kLargeBuffer for rounding and expose as a configurable option, so 512MB + max_non_split_rounding_size and if thats greater than 1024MB, we will use te 1024MB and we wont create a new 512MB block using cudaMalloc. This option is added so that we can pre-allocate some large blocks so that we can reuse them as much as possible and we dont stall on calling cudaMalloc.
Differential Revision: D62758758
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136174
Approved by: https://github.com/zyan0
Follow up to #110123, removing the CUDA_VERSION check for ROCm because HIP already has hipMallocAsync() and doesn't need the version check there.
Follow up to #108488, fixing the unit failing unit tests by accepting either a "cuda" or "hip" attribute for the caching allocator options. This is aligned to the masquerading strategy for ROCm/HIP.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110715
Approved by: https://github.com/ezyang
Summary: This diff refactors the code by moving CUDAAllocatorConfig into the header file. This config refactoring is done so that we can use the same config code for CUDA pinned memory as well.
Test Plan: sandcastle
Differential Revision: D49653265
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110123
Approved by: https://github.com/zdevito
