Workaround at::scalar_tensor with BFloat16. #2536
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ailzhang
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Do you think this also affects normal float16, or is specifically related to bfloat16 using more bits for precision? |
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@cjolivier01 It might :P Since XLA doesn't support Half so it doesn't matter much here I think. I'll try to send a fix on pytorch side. |
davidel
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Oct 8, 2020
| @@ -226,8 +226,13 @@ xla::ComputationClient::DataPtr GetDeviceData(const at::Tensor& tensor, | |||
| xla::ComputationClient::DataPtr GetDeviceData(at::Scalar value, | |||
| at::ScalarType scalar_type, | |||
| const Device& device) { | |||
| return GetDeviceData(at::scalar_tensor(value, at::TensorOptions(scalar_type)), | |||
| device); | |||
| // Workaround since at::scalar_tensor doesn't support bfloat16 yet. | |||
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This should be a utility function in torch_util.cpp/h, and a bug open with a reminder to remove the hack once scalar_tensor supports bfloat16.
Actually, it may be argued that this should be in pytorch main directly.
| value, at::TensorOptions(scalar_type == at::ScalarType::BFloat16 | ||
| ? at::ScalarType::Float | ||
| : scalar_type)); | ||
| if (scalar_type == at::ScalarType::BFloat16) t = t.to(scalar_type); |
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This requires brackets around the single statement 😉
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fixes #2535