DeepSpeed 0.14.x Regression: 'Expected all tensors to be on the same device' (ZeRO-3 / Adam Offload)
After upgrading to DeepSpeed >0.14.0 (e.g. 0.14.2), ZeRO-3 / optimizer-offload training crashes with 'Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!'. A regression in the 0.14.x ZeRO-3 path left some tensors on CPU; upgrading past the fix (or downgrading) resolves it.
After upgrading to DeepSpeed >0.
What this failure is
DeepSpeed 0.14.x Regression: 'Expected all tensors to be on the same device' (ZeRO-3 / Adam Offload) is a Distributed Training failure seen during ML training runs. After upgrading to DeepSpeed >0.14.0 (e.g. 0.14.2), ZeRO-3 / optimizer-offload training crashes with 'Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!'. A regression in the 0.14.x ZeRO-3 path left some tensors on CPU; upgrading past the fix (or downgrading) resolves it. Common tags: Deepspeed, Zero 3, Optimizer Offload, Device Mismatch.
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Why it happens (the mechanism)
A regression introduced in DeepSpeed 0.14.x ZeRO-3/optimizer-offload code (a single line in stage3.py) left some optimizer/parameter tensors on CPU while siblings moved to CUDA. The mismatched devices then collide during an elementwise/optimizer op; the line was later reverted/fixed on master. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Training that worked on 0.13.x crashes after upgrading DeepSpeed to 0.14.x
- Device-mismatch RuntimeError during the optimizer step or ZeRO-3 resume
- Adam offload / ZeRO-3 fp16/bf16 affected
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! | A regression introduced in DeepSpeed 0.14.x ZeRO-3/optimizer-offload code (a single line in stage3.py) left some optimizer/parameter tensors on CPU while siblings moved to CUDA |
| Traceback through deepspeed/runtime/zero/stage3.py | The mismatched devices then collide during an elementwise/optimizer op; the line was later reverted/fixed on master |
| Only on DeepSpeed >0.14.0 | A regression introduced in DeepSpeed 0.14.x ZeRO-3/optimizer-offload code (a single line in stage3.py) left some optimizer/parameter tensors on CPU while siblings moved to CUDA |
Which systems are affected
- DeepSpeed 0.14.x with ZeRO-3 and/or optimizer offload
- HF Trainer resume tests with ds_optim/ds_scheduler
- Mixed CPU-offload + CUDA optimizer state
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
- ✓Verified signal present: Traceback through deepspeed/runtime/zero/stage3.py
- ✓Verified signal present: Only on DeepSpeed >0.14.0
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Install the free VS Code extensionDeepSpeed errors in context
DeepSpeed changes when parameters, gradients and optimizer state are created, partitioned, gathered and offloaded. The hub separates ZeRO, memory, checkpoint and pipeline failures by lifecycle phase.
Compare every deepspeed error side by sideRelated failures to investigate next
Root cause
- A regression introduced in DeepSpeed 0.14.x ZeRO-3/optimizer-offload code (a single line in stage3.py) left some optimizer/parameter tensors on CPU while siblings moved to CUDA
- The mismatched devices then collide during an elementwise/optimizer op; the line was later reverted/fixed on master
The fix and how to prevent it
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