Fix EMAModel.restore() foreach path crashing with device mismatch when model is on GPU

[Dev-X25874]

What does this PR do?

Fixes a runtime crash in EMAModel.restore() when foreach=True and the model lives on a non-CPU device (e.g. CUDA).

store() always saves parameters to CPU (param.detach().cpu().clone()). The foreach path in restore() then passed those raw CPU tensors directly to torch._foreach_copy_(), which requires all tensors to be on the same device:

# before (broken on GPU)
torch._foreach_copy_(
    [param.data for param in parameters],
    [c_param.data for c_param in self.temp_stored_params],  # always CPU
)

This raises RuntimeError: Expected all tensors to be on same device for any user who calls the standard EMA validation pattern (store → copy_to → restore) with foreach=True on a GPU machine.

The fix mirrors the pattern already used correctly in copy_to()'s foreach path (line 780), which moves each shadow param to the target device before the copy:

# after (matches copy_to() pattern)
torch._foreach_copy_(
    [param.data for param in parameters],
    [c_param.to(param.device).data for c_param, param in zip(self.temp_stored_params, parameters)],
)

Also adds test_store_restore to both EMAModelTests and EMAModelTestsForeach — the store/restore round-trip was completely untested prior to this PR.

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Who can review?

@sayakpaul

[sayakpaul]

How can I minimally reproduce the bug?

[Dev-X25874]

Hi @sayakpaul, here's a minimal repro (requires a CUDA GPU):

import torch
import torch.nn as nn
from diffusers.training_utils import EMAModel

model = nn.Linear(4, 4).cuda()
ema = EMAModel(model.parameters(), foreach=True)

# Simulate a training step so shadow params differ from model params
with torch.no_grad():
    for p in model.parameters():
        p.add_(torch.randn_like(p))
ema.step(model.parameters())

# Standard EMA validation pattern
ema.store(model.parameters())     # saves to CPU
ema.copy_to(model.parameters())  # works fine
ema.restore(model.parameters())  # RuntimeError: Expected all tensors to be on same device

The crash happens because store() always clones params to CPU, but the foreach path in restore() feeds those raw CPU tensors into torch._foreach_copy_() alongside the GPU model params. The non-foreach path is unaffected since copy_() handles cross-device copies natively.

[sayakpaul]

This seems duplicated.

[Dev-X25874]

My bad, the foreach test_store_restore was mistakenly placed inside EMAModelTests instead of EMAModelTestsForeach. Fixed now.

[sayakpaul]

How come this works?

import torch

gpu = [torch.zeros(3, device="cuda")]
cpu = [torch.arange(3, dtype=torch.float32)]
torch._foreach_copy_(gpu, cpu)
print(gpu[0])

Prints:

tensor([0., 1., 2.], device='cuda:0')

[Dev-X25874]

Sorry for the noise, converting this to draft while I investigate further.

[Dev-X25874]

Verified on PyTorch 2.10.0+cu128 — no crash. You're right, torch._foreach_copy_() handles cross-device fine on modern PyTorch. Closing this PR. Sorry for the noise.