![]() DataLoader ¶ĭataLoader will reseed workers following Randomness in multi-process data loading algorithm. See torch.nn.RNN() and torch.nn.LSTM() for details and workarounds. In some versions of CUDA, RNNs and LSTM networks may have non-deterministic behavior. Which will make other PyTorch operations behave deterministically, too. We analyze a one-parameter class of models with a hopping rate N a and determine the large time behavior of the average range, as well as its complete distribution in two limit cases. The latter settingĬontrols only this behavior, unlike e_deterministic_algorithms() We introduce range-controlled random walks with hopping rates depending on the range N, that is, the total number of previously distinct visited sites. Itself may be nondeterministic, unless either While disabling CUDA convolution benchmarking (discussed above) ensures thatĬUDA selects the same algorithm each time an application is run, that algorithm ![]() Should set the environment variable CUBLAS_WORKSPACE_CONFIG according to CUDA documentation: cuda ()) tensor(, ],, ]], device='cuda:0')įurthermore, if you are using CUDA tensors, and your CUDA version is 10.2 or greater, you use_deterministic_algorithms ( True ) > torch. Of an operation that does not have one, please submit an issue:įor example, running the nondeterministic CUDA implementation of _add_() If an operation does not act correctlyĪccording to the documentation, or if you need a deterministic implementation Please check the documentation for e_deterministic_algorithms()įor a full list of affected operations. To throw an error if an operation is known to be nondeterministic (and without e_deterministic_algorithms() lets you configure PyTorch to useĭeterministic algorithms instead of nondeterministic ones where available, and Note that this setting is different from the Then performance might improve if the benchmarking feature is enabled with However, if you do not need reproducibility across multiple executions of your application, Then, the fastest algorithm will be usedĬonsistently during the rest of the process for the corresponding set of size parameters.ĭue to benchmarking noise and different hardware, the benchmark may select differentĪlgorithms on subsequent runs, even on the same machine.ĭisabling the benchmarking feature with = FalseĬauses cuDNN to deterministically select an algorithm, possibly at the cost of reduced New set of size parameters, an optional feature can run multiple convolution algorithms,īenchmarking them to find the fastest one. When a cuDNN convolution is called with a The cuDNN library, used by CUDA convolution operations, can be a source of nondeterminismĪcross multiple executions of an application. ![]() The documentation for those libraries to see how to set consistent seeds for them. C++ and binary code libraries for generating floating point and integer random numbers with uniform and non-uniform. If you are using any other libraries that use random number generators, refer to However, some applications and libraries may use NumPy Random Generator objects, ![]() Extending torch.func with autograd.Function.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples. ![]()
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