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fix(zero): detach flat buffer to prevent autograd inplace error on CP… - #7948
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…U accelerator The on-device flatten path (introduced in deepspeedai#7828) passes nn.Parameter objects with requires_grad=True to torch.cat(), creating a flat buffer with CatBackward0 grad_fn. Later, _unflatten_dense_tensors produces SplitBackward0 views that are assigned to model params. Inplace copy_() on these views during optimizer step raises: RuntimeError: Output 0 of SplitBackward0 is a view and is being modified inplace. This especially affects CPU training where CPU_Accelerator.is_available() returns True and available_memory() returns system RAM, so the on-device path is always taken. Fix: add .detach() to the flattened buffer, matching the implicit detach behavior of the CPU-offload path (param.data.cpu() + .to(device)). Also rename flatten_on_gpu -> flatten_on_accelerator and replace GPU-specific terminology in comments/logs with accelerator-generic equivalents. Signed-off-by: Guokai Ma <guokai.ma@intel.com>
Signed-off-by: Guokai Ma <guokai.ma@intel.com>
Signed-off-by: Guokai Ma <guokai.ma@intel.com>
tohtana
approved these changes
Apr 3, 2026
| assert flat.grad_fn is None, ("Flat buffer must be detached from autograd graph" | ||
| " to prevent inplace-modification errors during optimizer step") | ||
| data_loader = random_dataloader(model=engine, total_samples=8, hidden_dim=hidden_dim, device=engine.device) |
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Shouldn't random_dataloader take dtype? The default is preferred_dtype(), which could mismatch dtype.
…sertion - Pass explicit dtype to random_dataloader to avoid mismatch when preferred_dtype() (bfloat16 on CPU) differs from the test config dtype. Fixes fp32 test failure on CPU-only CI where data was bfloat16 but model expected float32. - Tighten log check from 'sufficient' to '(sufficient memory)' so it does not accidentally match '(insufficient memory)'. Signed-off-by: Guokai Ma <guokai.ma@intel.com> Signed-off-by: Ma, Guokai <guokai.ma@gmail.com>
Signed-off-by: Guokai Ma <guokai.ma@intel.com> Signed-off-by: Ma, Guokai <guokai.ma@gmail.com>
delock
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Apr 3, 2026
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@tohtana Thanks for the comments! I also verified that the newly added test will fail before applying this PR. |
delock
enabled auto-merge (squash)
April 3, 2026 02:40
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Apr 4, 2026
deepspeedai#7948) …U accelerator The on-device flatten path (introduced in deepspeedai#7828) passes nn.Parameter objects with requires_grad=True to torch.cat(), creating a flat buffer with CatBackward0 grad_fn. Later, _unflatten_dense_tensors produces SplitBackward0 views that are assigned to model params. Inplace copy_() on these views during optimizer step raises: RuntimeError: Output 0 of SplitBackward0 is a view and is being modified inplace. This especially affects CPU training where CPU_Accelerator.is_available() returns True and available_memory() returns system RAM, so the on-device path is always taken. Fix: add .detach() to the flattened buffer, matching the implicit detach behavior of the CPU-offload path (param.data.cpu() + .to(device)). Also rename flatten_on_gpu -> flatten_on_accelerator and replace GPU-specific terminology in comments/logs with accelerator-generic equivalents. --------- Signed-off-by: Guokai Ma <guokai.ma@intel.com> Signed-off-by: Ma, Guokai <guokai.ma@gmail.com>
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…U accelerator
The on-device flatten path (introduced in #7828) passes nn.Parameter objects with requires_grad=True to torch.cat(), creating a flat buffer with CatBackward0 grad_fn. Later, unflatten_dense_tensors produces SplitBackward0 views that are assigned to model params. Inplace copy() on these views during optimizer step raises:
RuntimeError: Output 0 of SplitBackward0 is a view and is being modified inplace.
This especially affects CPU training where CPU_Accelerator.is_available() returns True and available_memory() returns system RAM, so the on-device path is always taken.
Fix: add .detach() to the flattened buffer, matching the implicit detach behavior of the CPU-offload path (param.data.cpu() + .to(device)).
Also rename flatten_on_gpu -> flatten_on_accelerator and replace GPU-specific terminology in comments/logs with accelerator-generic equivalents.