backward () use grads # Safe, with synchronization with torch. backward () use grads # Unsafe with torch. Stream () # Safe, grads are used in the same stream context as backward() with torch. Have the same stream-semantics relationship as any group of ops: Optionally populating initial gradient(s), Tensor.backward(., gradient=initial_grad)), The backward pass inserts internal syncs to ensure this even whenīackward ops run on multiple streams as described in the previous paragraph.Īnd optionally supplying CUDA tensor(s) as the initial gradient(s) (e.g.,Īutograd.backward(., grad_tensors=initial_grads),Īad(., grad_outputs=initial_grads), or The stream semantics of a backward call with respect to surrounding ops are the sameĪs for any other call. This helps the backward pass exploit that same parallelism. If your forward pass runs independent ops in parallel on different streams, To get precise measurements, one should eitherĬall () before measuring, or use Įach backward CUDA op runs on the same stream that was used for its corresponding forward op. Operation is actually executed, so the stack trace does not show where it wasĪ consequence of the asynchronous computation is that time measurements without
(With asynchronous execution, such an error isn’t reported until after the This can be handy when an error occurs on the GPU. You can force synchronous computation by setting environment variableĬUDA_LAUNCH_BLOCKING=1. Hence, computation will proceed as ifĮvery operation was executed synchronously. (2) PyTorch automatically performs necessary synchronization when copying dataīetween CPU and GPU or between two GPUs. In general, the effect of asynchronous computation is invisible to the caller,īecause (1) each device executes operations in the order they are queued, and In parallel, including operations on CPU or other GPUs. This allows us to execute more computations Uses the GPU, the operations are enqueued to the particular device, but not cuda ( cuda2 ) # d.device, e.device, and f.device are all device(type='cuda', index=2)īy default, GPU operations are asynchronous. to ( device = cuda ) # b.device and b2.device are device(type='cuda', index=1) c = a + b # c.device is device(type='cuda', index=1) z = x + y # z.device is device(type='cuda', index=0) # even within a context, you can specify the device # (or give a GPU index to the. cuda () # a.device and b.device are device(type='cuda', index=1) # You can also use ``Tensor.to`` to transfer a tensor: b2 = torch. tensor (, device = cuda ) # transfers a tensor from CPU to GPU 1 b = torch.
device ( 1 ): # allocates a tensor on GPU 1 a = torch. cuda () # y.device is device(type='cuda', index=0) with torch. tensor (, device = cuda0 ) # x.device is device(type='cuda', index=0) y = torch. device ( 'cuda:2' ) # GPU 2 (these are 0-indexed) x = torch. device ( 'cuda' ) # Default CUDA device cuda0 = torch. PyTorch Governance | Persons of InterestĬuda = torch.
CPU threading and TorchScript inference.
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