[Bugfix][MiniCPM-o] Fix cuda/cpu device mismatch in Resampler2_5 pos_embed

[parthash0804]

Summary

Resampler2_5.forward adds the cached positional embedding to the input without moving it to the input's device:

pos_embed = self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)
...
x + pos_embed   # <-- x is on cuda, pos_embed is on cpu

When self.pos_embed (a non-persistent buffer) stays on CPU while the hidden states x are on the GPU, this raises:

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

Root cause

pos_embed is registered as a non-persistent buffer, so it is not moved by the normal weight-loading device placement and stays on CPU unless something explicitly relocates it. Factors that decide whether the bug actually surfaces:

Patch grid size vs. the cached pos_embed size (max_size, default 70×70).
_adjust_pos_cache only rebuilds pos_embed (on the input device) when the target grid exceeds the cached size. For images whose grid stays within 70×70, the original CPU buffer is used as-is — exposing the mismatch. Very large images can incidentally rebuild the buffer on the GPU and hide the bug.

Fix

One line: pass device=device so pos_embed matches x.

Steps to Reproduce

Use vLLM's built-in synthetic multimodal benchmark. The random-mm bucket key is (height, width, num_frames); (800, 1024, 1) produces a single 800×1024 image per request, keeping the resampler grid under 70×70 so the buggy CPU pos_embed is used directly.

vllm bench throughput \
  --model openbmb/MiniCPM-o-2_6 --trust-remote-code --dtype float16 \
  --max-model-len 4096 --max-num-seqs 1 --enforce-eager \
  --num-prompts 1 --input-len 512 --output-len 32 \
  --dataset-name random-mm \
  --random-mm-base-items-per-request 1 \
  --random-mm-limit-mm-per-prompt '{"image": 1}' \
  --limit-mm-per-prompt '{"image": 1}' \
  --random-mm-bucket-config '{(800, 1024, 1): 1.0}' \
  --mm-processor-cache-gb 0

Test Result

Before the fix — crash during engine init (exit code 1)

File "/.../vllm/model_executor/models/minicpmv.py", line 1586, in get_vision_hidden_states
    return self.resampler(vision_embedding, tgt_sizes)
File "/.../vllm/model_executor/models/minicpmv.py", line 233, in forward
    x + pos_embed,  # L * B * D +  L * B * D
    ~~^~~~~~~~~~~
RuntimeError: Expected all tensors to be on the same device,
but found at least two devices, cuda:0 and cpu!
...
RuntimeError: Engine core initialization failed. See root cause above.

After the fix — benchmark completes (exit code 0)

Processed prompts: 100%|██████████| 1/1 [00:09<00:00,  9.12s/it,
    est. speed input: 112.30 toks/s, output: 14.04 toks/s]
Throughput: 0.11 requests/s, 122.49 total tokens/s, 13.61 output tokens/s

Scope

1. Single-line change in Resampler2_5.forward; mirrors the already-correct
   Resampler4_5.forward.
2. No API or behavior change

---

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[DarkLight1337]

cc @tc-mb

[tc-mb]

cc @tc-mb

Thank you for reminding me, I'll help verify it.

[tc-mb]

@parthash0804 Thanks for the PR! (And thanks @DarkLight1337 for pinging me.)

I verified this locally. The bug is real and the fix is correct.

Root cause: Resampler2_5._set_2d_pos_cache creates pos_embed on CPU by default (device="cpu"). In forward, _adjust_pos_cache only rebuilds the buffer on the input device when the target grid exceeds max_size (70×70). For normal-sized images, the CPU buffer is reused as-is. Line 220 does .to(dtype) but omits device, so pos_embed stays on CPU while x is on GPU — causing RuntimeError: Expected all tensors to be on the same device.

Fix: change .to(dtype) → .to(device=device, dtype=dtype). This matches the already-correct pattern in Resampler4_5.forward (line 383).

Verification: I confirmed that before the fix, the sliced pos_embed stays on CPU; after the fix, it correctly moves to the GPU. Both small grids (within max_size, the buggy path) and large grids (exceeding max_size, which was accidentally working) produce correct results.

LGTM, approved.