![]() ![]() Results with FP32 precision, maintaining FP32 dynamic range. Torch.float32 tensors by rounding input data to have 10 bits of mantissa, and accumulating TF32 tensor cores are designed to achieve better performance on matmul and convolutions on This flag controls whether PyTorch is allowed to use the TensorFloat32 (TF32) tensor cores,Īvailable on new NVIDIA GPUs since Ampere, internally to compute matmul (matrix multipliesĪnd batched matrix multiplies) and convolutions. This flagĭefaults to True in PyTorch 1.7 to PyTorch 1.11, and False in PyTorch 1.12 and later. Starting in PyTorch 1.7, there is a new flag called allow_tf32. cuda ( cuda2 ) # d.device, e.device, and f.device are all device(type='cuda', index=2) TensorFloat-32(TF32) on Ampere devices ¶ 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. ![]() ![]() Extending torch.func with autograd.FunctionĬuda = torch.CPU threading and TorchScript inference. ![]()
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