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[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding#38053

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Krishnachaitanyakc:fix/minicpmo-audio-feature-lens-type-error
Jun 2, 2026
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[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding#38053
vllm-bot merged 6 commits into
vllm-project:mainfrom
Krishnachaitanyakc:fix/minicpmo-audio-feature-lens-type-error

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@Krishnachaitanyakc

@Krishnachaitanyakc Krishnachaitanyakc commented Mar 25, 2026

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Summary

Fixes #37981

When processing audio inputs for MiniCPM-o-4.5, process_audios in minicpmo.py raises:

TypeError: only integer tensors of a single element can be converted to an index

The root cause is that audio_feature_lens from the HF processor is a list of 1D tensors (one per audio, each containing per-chunk frame lengths), but the code used these tensors directly as slice indices in feat[:, :feature_len]. PyTorch cannot use multi-element tensors as slice bounds.

Additionally, audio_feature_lens is structured per-audio while audio_features is structured per-chunk, so the previous zip could silently misalign when an audio has multiple chunks (audio > 30s).

Changes

  • Flatten audio_feature_lens into a flat list of Python integers (one per chunk) before using them as slice indices
  • Handles 0-dim tensors, 1D tensors, and plain numbers robustly
  • This correctly aligns with the per-chunk first dimension of audio_features

Duplicate-work check

Test plan

  • Verified syntax with python3 -m py_compile vllm/model_executor/models/minicpmo.py
  • Reproduce the original error by serving MiniCPM-o-4.5 and sending audio via the chat completions API
  • Verify the fix resolves the TypeError for both short audio (single chunk) and long audio (multiple chunks)

AI assistance was used (Claude) in analyzing the root cause and drafting this fix. The human submitter has reviewed all changed lines.

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@mergify mergify Bot added the bug Something isn't working label Mar 25, 2026
@Krishnachaitanyakc Krishnachaitanyakc marked this pull request as ready for review March 25, 2026 02:11
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Code Review

This pull request refactors the process_audios function in vllm/model_executor/models/minicpmo.py to correctly flatten audio_feature_lens into a list of integer lengths for unpadding audio features. The current implementation, however, is not robust against higher-dimensional tensors within audio_feature_lens, which could lead to a TypeError downstream. A more robust approach using tensor.flatten().tolist() is recommended.

Comment thread vllm/model_executor/models/minicpmo.py
Comment on lines +374 to +380
if isinstance(lens, torch.Tensor):
if lens.dim() == 0:
flat_feature_lens.append(lens.item())
else:
flat_feature_lens.extend(lens.tolist())
else:
flat_feature_lens.append(int(lens))

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high

The current implementation for flattening audio_feature_lens handles 0D and 1D tensors, but it is not robust against tensors with more than one dimension. If lens were a 2D tensor, for example, lens.tolist() would return a list of lists. flat_feature_lens.extend() would then add these lists, resulting in flat_feature_lens containing lists instead of only integers. This would cause a TypeError in the list comprehension that follows, which is similar to the bug this PR intends to fix.

To make the code more robust and simpler, you can use flatten() to flatten the tensor regardless of its dimensionality before converting it to a list.

                if isinstance(lens, torch.Tensor):
                    flat_feature_lens.extend(lens.flatten().tolist())
                else:
                    flat_feature_lens.append(int(lens))

@DarkLight1337

DarkLight1337 commented Mar 25, 2026

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cc @tc-mb

@tc-mb

tc-mb commented Mar 26, 2026

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@DarkLight1337
Thanks for your reminder, I will take a look at this code today.

@tc-mb

tc-mb commented Mar 26, 2026

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Thanks for this fix! I reviewed the code and ran some tests locally with MiniCPM-o-4.5. Here's a summary:

The fix to process_audios is correct. @DarkLight1337

However, there is a second layer of the same bug. After process_audios returns, the result flows back through _call_hf_processor → _cached_apply_hf_processor → MultiModalKwargsItems.from_hf_inputs, which applies batched("audio") config again and validates that all fields in the audio modality have the same batch size. For multi-chunk audio, audio_features has total_chunks elements while audio_feature_lens has N (number of audios) elements, causing:

ValueError: Cannot merge different batch sizes for modality='audio'!
Found: batch_sizes={'audio_feature_lens': 1, 'audio_features': 3}
This is a pre-existing issue (masked by the TypeError your PR fixes) — multi-chunk audio (>30s) has never worked end-to-end for MiniCPM-O in vLLM. To fully fix it, the field config for audio_feature_lens in _minicpmo_field_config likely needs to use a different strategy than batched("audio") (e.g. flat or a custom config) so the batch size validation passes.

@Krishnachaitanyakc
Would you like to extend this PR to address the from_hf_inputs validation as well, or would you prefer I open a follow-up PR for that part?

@Krishnachaitanyakc Krishnachaitanyakc force-pushed the fix/minicpmo-audio-feature-lens-type-error branch 2 times, most recently from a3edb71 to d96c00e Compare March 26, 2026 20:35
@Krishnachaitanyakc
[BugFix] Fix TypeError in MiniCPM-O audio processing when slicing wit…
dff6a79 
…h tensor indices

The `process_audios` method used `audio_feature_lens` values (which are
tensors) directly as slice indices in `feat[:, :feature_len]`. This fails
with `TypeError: only integer tensors of a single element can be converted
to an index` because PyTorch cannot use multi-element tensors as slice
bounds.

Additionally, `audio_feature_lens` is structured as a list of 1D tensors
(one per audio, each containing per-chunk frame lengths), while
`audio_features` is indexed per chunk. The previous code zipped these
mismatched dimensions together, which would silently truncate data when
an audio has multiple chunks.

Fix: flatten `audio_feature_lens` into a flat list of Python integers
(one per chunk) before using them as slice indices. This correctly aligns
with the per-chunk dimension of `audio_features` and avoids the TypeError.

Fixes vllm-project#37981

Signed-off-by: Krishna Chaitanya Balusu <krishnabkc15@gmail.com>
Co-authored-by: Claude
Signed-off-by: Krishna Chaitanya Balusu <krishnabkc15@gmail.com>
@Krishnachaitanyakc
Use flatten().tolist() for robust tensor flattening in process_audios
bedb568 
Address Gemini code review feedback: replace separate 0-D/1-D tensor
handling with a single lens.flatten().tolist() call that uniformly
handles tensors of any dimensionality, preventing potential TypeErrors
from higher-dimensional tensors producing nested lists.

Signed-off-by: Krishna Chaitanya Balusu <krishnabkc15@gmail.com>
@Krishnachaitanyakc
Fix batch size mismatch for multi-chunk audio in MiniCPM-O field config
277ee7f 
For audio >30s, the HF processor splits into multiple chunks.
After process_audios, audio_features has total_chunks items (one per
chunk) while audio_feature_lens has N items (one per audio). Both using
batched("audio") causes a ValueError in from_hf_inputs because the
batch sizes differ.

Use flat_from_sizes for audio_features when a mismatch is detected,
grouping chunks back by audio so both fields share batch size N.
This follows the same pattern used by kimi_vl and qwen2_vl for
flat pixel_values with batched grid metadata.

Co-authored-by: Claude
Signed-off-by: Krishna Chaitanya Balusu <krishnabkc15@gmail.com>
@Krishnachaitanyakc Krishnachaitanyakc force-pushed the fix/minicpmo-audio-feature-lens-type-error branch from d96c00e to 277ee7f Compare March 26, 2026 20:39
@wjinxu wjinxu mentioned this pull request May 25, 2026
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@wjinxu

wjinxu commented May 25, 2026

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Hi @Krishnachaitanyakc, thanks for working on this!

I'm an intern on the MiniCPM-V team (working with @tc-mb). We've been verifying this PR end-to-end on MiniCPM-o-4.5 with real long-audio input, and the first two commits work as expected.

While testing the multi-chunk path (audio > 30s), we noticed the third commit still trips an AssertionError: torch.Tensor is required for multiple slices downstream in MultiModalKwargsItems.from_hf_inputs. I've filed a small follow-up PR (Krishnachaitanyakc#1, ~10 lines) against your branch with a fix. After this patch, short/medium/long audio all generate correctly on MiniCPM-o-4.5.

Happy to iterate on the patch if you'd like — or you can just merge it into your branch if it looks good. Thanks again!

@wjinxu @Krishnachaitanyakc @cursoragent
Use flat() with plain slices for multi-chunk audio_features
7dd617e 
flat_from_sizes wraps each slice in a single-element tuple, which makes
MultiModalFlatField.build_elems require data to be a single torch.Tensor.
After process_audios unpads each chunk to its real length, audio_features
is a list[Tensor] with variable per-chunk time lengths that cannot be
stacked into a single tensor, so the assertion fires in
MultiModalKwargsItems.from_hf_inputs and multi-chunk audio still breaks
end-to-end.

Replace flat_from_sizes with flat() and plain slice objects.  build_elems
then takes the is_list_of(slices, slice) fast-path that skips the
assertion and falls back to standard data[start:stop] slicing, which
works on Python lists too.

Verified on MiniCPM-o-4.5 with a 45s test audio: short/medium/long
inputs all generate cleanly with this patch.

Co-authored-by: Krishna Chaitanya Balusu <krishnabkc15@gmail.com>
Signed-off-by: wjinxu <1299461899@qq.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
@wjinxu

wjinxu commented May 26, 2026

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Hi @Krishnachaitanyakc , sorry — I forgot the DCO sign-off on my commit, which is failing the DCO check.

I've amended it with --signoff and force-pushed to my branch (new SHA: 7dd617eb). Could you sync it into this PR when convenient? Thanks!

@Krishnachaitanyakc Krishnachaitanyakc force-pushed the fix/minicpmo-audio-feature-lens-type-error branch from 9b826f4 to 7dd617e Compare May 26, 2026 05:10
@wjinxu

wjinxu commented Jun 1, 2026 •
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Status update — I think this is ready for CI now.

To summarize where things stand:

  • The original TypeError fix (process_audios) and the flatten().tolist() robustness improvement are in.
  • The multi-chunk path (audio > 30s) is also fixed: my follow-up patch replaces the tensor-index slicing with flat() + plain slices, and the DCO-signed commit (7dd617e) has now been synced into this branch.
  • We've verified end-to-end on MiniCPM-o-4.5: short, medium, and long (multi-chunk) audio all generate correctly.
  • @tc-mb (MiniCPM-V team) has reviewed and confirmed the approach.

@DarkLight1337 would you be able to add the ready label so CI can run? Happy to address anything that comes up. Thanks!

@DarkLight1337 DarkLight1337 added the ready ONLY add when PR is ready to merge/full CI is needed label Jun 1, 2026
DarkLight1337
DarkLight1337 approved these changes Jun 1, 2026
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Thanks for helping validate this!

@DarkLight1337 DarkLight1337 enabled auto-merge (squash) June 1, 2026 11:26
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Hi @Krishnachaitanyakc, the pre-commit checks have failed. Please run:

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pre-commit install
pre-commit run --all-files

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[BugFix] Apply ruff-format to minicpmo audio field config
f9dd06d 
Reformat the audio_features / audio_feature_lens length resolution in
_minicpmo_field_config to match ruff-format's preferred line wrapping,
fixing the failing pre-commit check on this PR.

Signed-off-by: Kc Balusu <kcbalusu@users.noreply.github.com>
auto-merge was automatically disabled June 1, 2026 17:22

Head branch was pushed to by a user without write access

@DarkLight1337 DarkLight1337 enabled auto-merge (squash) June 1, 2026 17:26
@wjinxu

wjinxu commented Jun 2, 2026

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@DarkLight1337 thanks for enabling auto-merge! The two failing CI jobs look unrelated to this change:

  • metrics-tracing-2-gpus → tests/v1/tracing/test_tracing.py::test_traces: runs on facebook/opt-125m (text-only). The test body actually succeeds (inference output is produced); the exit 139 is a segfault during gRPC/OpenTelemetry thread teardown at process exit.
  • async-engine-inputs-utils-worker → only tests/utils_/test_import_utils.py::TestHasModule (2 cases) fail, with RuntimeError: CUDA driver version is insufficient / OSError: cannot load library — i.e. a CI runner driver/env issue.

This PR only touches _minicpmo_field_config in minicpmo.py. Could you retry these two jobs when convenient? Thanks!

@vllm-bot vllm-bot merged commit 279d25f into vllm-project:main Jun 2, 2026
62 of 65 checks passed
@DarkLight1337

DarkLight1337 commented Jun 2, 2026

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Force merged

hynky1999 added a commit to macrodata-labs/vllm that referenced this pull request Jun 2, 2026
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* Skip docs build if PR doesn't affect docs (#43972)

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* [Bugfix] Fix RMSNorm kernels to multiply in weight's native dtype (#42379)

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---------

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@cursoragent @DarkLight1337 @mvanhorn
[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding (vllm-pro…
dd62ba9 
…ject#38053)

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bnellnm pushed a commit to neuralmagic/vllm that referenced this pull request Jun 4, 2026
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[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding (vllm-pro…
4387894 
…ject#38053)

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[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding (vllm-pro…
8f97c1a 
…ject#38053)

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[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding (vllm-pro…
81a0571 
…ject#38053)

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[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding (vllm-pro…
6b54f0d 
…ject#38053)

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[BugFix] Fix TypeError in MiniCPM-O audio feature unpadding (vllm-pro…
180b93a 
…ject#38053)

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Merged
tc-mb pushed a commit to tc-mb/vllm-omni that referenced this pull request Jun 12, 2026
@wjinxu
fix: handle multi-chunk (>30s) audio in MiniCPM-o 2.6/4.5 process_audios
a778f94 
The vendored MiniCPM-o process_audios used audio_feature_lens tensors
directly as slice indices in feat[:, :feature_len], which raises
"TypeError: only integer tensors of a single element can be converted to
an index" for audio >30s (HF processor splits it into multiple 30s
chunks, so audio_feature_lens is a list of multi-element tensors).

Flatten audio_feature_lens into a flat list of per-chunk integer lengths
that aligns with the per-chunk first dim of audio_features, and group
audio_features by audio via MultiModalFieldConfig.flat so the audio
modality batch sizes match in from_hf_inputs.

Mirrors the upstream vLLM fix (vllm-project/vllm#38053) for the
vendored 2.6/4.5 copies. Verified end-to-end on MiniCPM-o-2.6 with a
49.9s input (HTTP 400 -> 200, correct transcription); 4.5 mirrors the
same change.

Signed-off-by: wjinxu <1299461899@qq.com>
@tc-mb tc-mb mentioned this pull request Jun 12, 2026
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@DarkLight1337 DarkLight1337 DarkLight1337 approved these changes

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[Bug]: v0.18.0 fails to run MiniCPM-o-4.5

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@Krishnachaitanyakc @DarkLight1337 @tc-mb @wjinxu @vllm-bot