torch.use_deterministic_algorithms(True) torch.use_deterministic_algorithms(True)允许你配置PyTorch在可用的情况下使用确定性算法而不是非确定性算法,并且如果已知某个操作是不确定的(并且没有确定的替代方法),则抛出RuntimeError错误。 请查看torch.use_deterministic_algorithms()的文档,以获取受影响操作的完整列表。...
torch.use_deterministic_algorithms(True) torch.backends.cudnn.enabled = False # ---》引起问题的代码行 torch.backends.cudnn.benchmark = False #禁用benchmark,保证可复现 #torch.backends.cudnn.benchmark = True #恢复benchmark,提升效果 torch.backends.cudnn.deterministic = True os.environ['CUBLAS_WO...
RuntimeError: Deterministic behavior was enabled with either `torch.use_deterministic_algorithms(True)` or `at::Context::setDeterministicAlgorithms(true)`, 在CORL的代码中,出现了一种error: 可经过如下方法解决: cuda 10.1 os.environ['CUDA_LAUNCH_BLOCKING'] ='1'cuda10.2及以上 os.environ['CUBLAS_WOR...
torch.use_deterministic_algorithms(True) 将此行代码加入到你的代码中后,会自动帮助你查找你的代码中的不确定性算法。如果存在不确定性算法,代码就会终止运行并抛出一个error,如: RuntimeError: index_add_cuda_ does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True...
torch.use_deterministic_algorithms(False) return grad_outputs class DeterministicAlgorithmsEndOp(Function): @staticmethod def forward(ctx, tensor): if isinstance(tensor, FakeTensor): raise NotImplementedError( "torch.npu.disable_deterministic_algorithms do not support to graph." + pta_...
torch.use_deterministic_algorithms 是 torch.are_deterministic_algorithms_enabled 是 torch.set_deterministic_debug_mode 是 torch.get_deterministic_debug_mode 是 torch.set_warn_always 是 torch.is_warn_always_enabled 是 torch.vmap 是 torch._assert 是 torch.sym_float 是 支...
而另一部分deterministic=True,最终被传到了_set_torch_flags,其中关键是: torch.cuda.cudnn.benchmark = False torch.use_deterministic_algorithms(True) os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" def_set_torch_flags(*, deterministic:Optional[Union[bool, _LITERAL_WARN]] =None, benchmark:Opt...
torch.use_deterministic_algorithms(False) elif _TORCH_GREATER_EQUAL_1_7: else: torch.set_deterministic(False) else: # the minimum version Lightning supports is PyTorch 1.6 torch._set_deterministic(False) @pytest.fixture 5 changes: 1 addition & 4 deletions 5 tests/core/test_metric_result_integ...
(args.params.deterministic && perfResults.determinism != CUDNN_DETERMINISTIC)) {// if benchmarking, map the original params with the found algo+math type for re-useif(benchmark) {// cache 只存需要 benchmark 的结果cache.insert(args.params, perfResults);// Free the cached blocks in our ...
🐛 Describe the bug In order to ensure deterministic runs, we want to set: torch.use_deterministic_algorithms(True) However, if this is used with torch.compile and while using FP8...then receive: torch._dynamo.exc.BackendCompilerFailed: b...