Avoid unnecessary copy in TensorSource #8849
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Hi @ysiraichi, just follow up on offline discussion on the copy operation. PTAL at the PR, thanks! |
yaochengji
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Mar 19, 2025
ysiraichi
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Mar 19, 2025
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| // The purposes of copy are: | ||
| // 1. Ensure the memory is contiguous, which is expected by PJRT. | ||
| // 2. Move CUDA tensor to CPU since we cannot pass CUDA memory to PJRT now. | ||
| // 3. Cast data type. | ||
| // We can avoid if copy is not needed. | ||
| if (tensor.device() == at::kCPU && tensor.is_contiguous() && | ||
| tensor.dtype() == target_torch_type) { | ||
| tensor_ = std::move(tensor); | ||
| } else { | ||
| // TODO(ysiraichi): check, first, if tensor lives in a device that the | ||
| // current PjRt client has access. If so, we don't need to go through the | ||
| // CPU. | ||
| tensor_ = std::move(tensor.to( | ||
| at::TensorOptions().device(at::kCPU).dtype(target_torch_type), | ||
| /*non_blocking=*/false, | ||
| /*copy=*/true, at::MemoryFormat::Contiguous)); | ||
| } |
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As far as I understand it, tensor.to(...) (without the copy argument) already checks whether it should actually copy or not. So, what do you think of reverting to the old tensor.to(...) usage, but removing the copy argument, instead?
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Hi @ysiraichi, I didn't find a tensor.to(...) without the copy arg in C++, is it only in python?
tengyifei
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Mar 20, 2025
tengyifei
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Mar 20, 2025
tengyifei
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Avoid
at::Tensorcopy inTensorSourceif it's not necessary.The copy operations are needed under 2 cases:
The copy operation needs to be blocking, since the transfer operation depends on the copied tensor.