PYTORCH . ORG {
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Automatic Mixed Precision package - torch.amp โ PyTorch 2.7 documentation
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Keywords {๐}
float, ops, autocast, type, inputs, autocasting, source, cpu, run, cuda, precision, autocastenabled, dtype, forward, input, bfloat, region, model, mixed, xpu, backward, runs, tensors, gradient, regions, pytorch, tensor, device, behavior, promote, default, output, unlisted, convd, convtransposed, automatic, deprecated, args, multiple, functions, torchrand, devicecuda, enabled, require, torchautocast, opspecific, devicetype, parameters, class, pass,
Topics {โ๏ธ}
/pytorch/pytorch/issues/75956 torch xpu op-specific behavior cuda op-specific behavior cpu op-specific behavior op-specific dtype chosen floating-point tensors produced autocast op reference ๏ฟฝgradient scalingโ multiplies web site terms floating-point tensors floating-point tensors gradient scaling floating-point dtypes widest input type pytorch foundation supports mixed precision depth tutorials complex scenarios internal ops execute type mismatch errors package torch bf16-pretrained models fp16 numerical range docs autocast region back //github fp16 dynamic range locally disabling autocast multiple models/losses current autocast state custom autograd functions autocast-disabled region gradients flowing backward produces float16 output autocast-enabled region autocast-enabled regions inputsโ dtypes match produce float32 output optim torch note amp/fp16 natively promote inputs unlisted ops run pytorch project gradient penalty pytorch foundation produce float16 gradients backward ops run backward gradient unlisted op jit autocast pass
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