Improve padding util for compile #7355
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Signed-off-by: Masahiro Tanaka <[email protected]>
sfc-gh-truwase
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Jun 14, 2025
deepcharm
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Jun 16, 2025
This PR improves `pad_tensors` in `deepspeed/compile/util.py`, which pads tensors so that all ranks have tensors with the same shape. Previously, this function only adjusts tensor shapes, but tensor strides could differ across ranks, leading to recompilation on only some ranks. As DeepCompile inserts communication operators in the graph, the communication collective easily gets stuck. To address this issue, this PR replaces the use of `torch.nn.functional.pad` with a new approach that ensures consistent strides and avoids communication issues during distributed operations. Signed-off-by: Masahiro Tanaka <[email protected]> Signed-off-by: Max Kovalenko <[email protected]>
Antlera
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Jun 27, 2025
This PR improves `pad_tensors` in `deepspeed/compile/util.py`, which pads tensors so that all ranks have tensors with the same shape. Previously, this function only adjusts tensor shapes, but tensor strides could differ across ranks, leading to recompilation on only some ranks. As DeepCompile inserts communication operators in the graph, the communication collective easily gets stuck. To address this issue, this PR replaces the use of `torch.nn.functional.pad` with a new approach that ensures consistent strides and avoids communication issues during distributed operations. Signed-off-by: Masahiro Tanaka <[email protected]>
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This PR improves
pad_tensorsindeepspeed/compile/util.py, which pads tensors so that all ranks have tensors with the same shape.Previously, this function only adjusts tensor shapes, but tensor strides could differ across ranks, leading to recompilation on only some ranks. As DeepCompile inserts communication operators in the graph, the communication collective easily gets stuck.
To address this issue, this PR replaces the use of
torch.nn.functional.padwith a new approach that ensures consistent strides and avoids communication issues during distributed operations.