FSDP2 Unwrapped Model Still Has DTensor Weights, save_pretrained Fails
After unwrapping an FSDP2 (fully_shard) model the parameters are still DTensors, so save_pretrained or a plain state_dict produces invalid storage or sharded tensors. You must gather a full, unsharded state dict before saving and write only on rank 0.
After unwrapping an FSDP2 (fully_shard) model the parameters are still DTensors, so save_pretrained or a plain state_dict produces invalid storage or sharded tensors.
What this failure is
FSDP2 Unwrapped Model Still Has DTensor Weights. Save_pretrained Fails is a Distributed Training failure seen during ML training runs. After unwrapping an FSDP2 (fully_shard) model the parameters are still DTensors, so save_pretrained or a plain state_dict produces invalid storage or sharded tensors. You must gather a full, unsharded state dict before saving and write only on rank 0. Common tags: Accelerate, Fsdp2, Dtensor, Save_pretrained.
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Why it happens (the mechanism)
FSDP2 keeps parameters as sharded DTensors; simply unwrapping the module does not materialize a full unsharded tensor. Save_pretrained expects plain torch.Tensors, so it chokes on DTensors or saves only the local shard. 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
- Unwrapped FSDP2 model parameters are DTensors, not plain tensors
- save_pretrained / get_state_dict raises invalid storage errors
- Saved checkpoint is sharded or missing weights
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| model weights are torch.distributed.tensor.DTensor after unwrap | FSDP2 keeps parameters as sharded DTensors; simply unwrapping the module does not materialize a full unsharded tensor |
| Invalid storage error when calling get_state_dict | save_pretrained expects plain torch.Tensors, so it chokes on DTensors or saves only the local shard |
| Checkpoint cannot be reloaded with from_pretrained | FSDP2 keeps parameters as sharded DTensors; simply unwrapping the module does not materialize a full unsharded tensor |
Which systems are affected
- Accelerate FSDP2 (fully_shard) training
- PyTorch DTensor-based sharding
- save_pretrained on a sharded model
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: model weights are torch.distributed.tensor.DTensor after unwrap
- ✓Verified signal present: Invalid storage error when calling get_state_dict
- ✓Verified signal present: Checkpoint cannot be reloaded with from_pretrained
- ✓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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Root cause
- FSDP2 keeps parameters as sharded DTensors; simply unwrapping the module does not materialize a full unsharded tensor
- save_pretrained expects plain torch.Tensors, so it chokes on DTensors or saves only the local shard
The fix and how to prevent it
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