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This PR introduces `_detect_dma_connectivity` - a utility for detecting DMA connectivity among devices. The "DMA connectivity" in this context is more stringent than the ability to perform memory copy without CPU involvement. We define it as the ability for a device to issue load/store instructions and perform atomic operations on memory that resides on connected devices. The ability translates to the ability to run most aten GPU operations with operands backed by remote memory. `_detect_dma_connectivity` can help PyTorch and its users to determine whether certain DMA-based optimizations are possible. `_detect_dma_connectivity` takes a `(device_type, connection_type)` pair and returns a matrix describing the connectivity. Connectivity detectors are statically registered on a `(device_type, connection_type)` basis. This PR implements the detector for `(CUDA, "nvlink")`. Later, detectors for pairs such as `(ROCM, "infinity_fabric")` can be introduced. Example: ```python3 >>> from torch._C._autograd import DeviceType >>> from torch._C._distributed_c10d import _detect_dma_connectivity >>> connectivity = _detect_dma_connectivity(DeviceType.CUDA, "nvlink") >>> for row in connectivity.matrix: ... print(row) ... [0, 18, 18, 18, 18, 18, 18, 18] [18, 0, 18, 18, 18, 18, 18, 18] [18, 18, 0, 18, 18, 18, 18, 18] [18, 18, 18, 0, 18, 18, 18, 18] [18, 18, 18, 18, 0, 18, 18, 18] [18, 18, 18, 18, 18, 0, 18, 18] [18, 18, 18, 18, 18, 18, 0, 18] [18, 18, 18, 18, 18, 18, 18, 0] ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129510 Approved by: https://github.com/weifengpy |
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| core | ||
| perfkernels | ||
| serialize | ||
| utils | ||
| .clang-format | ||
| CMakeLists.txt | ||
| unexported_symbols.lds | ||
| version_script.lds | ||