Error when running NVILA-8B-Video

[yuanyehome]

When I evaluated NVILA-8B-Video on lmms-longvideobench with this script:

#!/bin/bash
set -e

MODEL_NAMES=(
    "NVILA-8B-Video"
)
SELECTED_TASKS=(
    "lmms-longvideobench_val_v"
)
TASK_STR=$(
    IFS=,
    echo "${SELECTED_TASKS[*]}"
)
echo "TASK_STR: $TASK_STR"

START_TIME=$(date +%s)
echo "START_TIME: $(date -d @"$START_TIME")"

for MODEL_NAME in "${MODEL_NAMES[@]}"; do
    MODEL_ID="../my-models/models/$MODEL_NAME"
    vila-eval \
        --model-name "$MODEL_NAME" \
        --model-path "$MODEL_ID" \
        --conv-mode auto \
        --tags-include local \
        --nproc-per-node 2 \
        --tasks "$TASK_STR" \
        --output-dir "./runs/run-eval-20250112/$MODEL_NAME"
done

END_TIME=$(date +%s)
echo "END_TIME: $(date -d @"$END_TIME")"
echo "TIME_TAKEN: $((END_TIME - START_TIME)) seconds"

I've encountered this error:

2025-01-16 23:23:30.871 | WARNING  | llava.utils.media:_load_video:59 - Failed to read frame 8014 from video '/data/yy/.cache/huggingface/longvideobench/videos/BktEeBeA7a8.mp4'. Skipped.
2025-01-16 23:23:30.875 | WARNING  | llava.utils.media:_load_video:59 - Failed to read frame 8014 from video '/data/yy/.cache/huggingface/longvideobench/videos/BktEeBeA7a8.mp4'. Skipped.
Traceback (most recent call last):
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/lmms_eval/__main__.py", line 329, in cli_evaluate
    results, samples = cli_evaluate_single(args)
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/lmms_eval/__main__.py", line 470, in cli_evaluate_single
    results = evaluator.simple_evaluate(
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/lmms_eval/utils.py", line 533, in _wrapper
    return fn(*args, **kwargs)
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/lmms_eval/evaluator.py", line 243, in simple_evaluate
    results = evaluate(
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/lmms_eval/utils.py", line 533, in _wrapper
    return fn(*args, **kwargs)
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/lmms_eval/evaluator.py", line 457, in evaluate
    resps = getattr(lm, reqtype)(cloned_reqs)  # Choiszt run generate until
  File "/data/yy/anker/nvila/VILA/llava/eval/lmms/models/vila_internal.py", line 106, in generate_until
    response = self.model.generate_content(prompt, generation_config=generation_config)
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/data/yy/anker/nvila/VILA/llava/model/llava_arch.py", line 834, in generate_content
    output_ids = self.generate(
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/data/yy/anker/nvila/VILA/llava/model/llava_arch.py", line 783, in generate
    inputs_embeds, _, attention_mask = self._embed(input_ids, media, media_config, None, attention_mask)
  File "/data/yy/anker/nvila/VILA/llava/model/llava_arch.py", line 415, in _embed
    media_embeds = self.__embed_media_tokens(media, media_config)
  File "/data/yy/anker/nvila/VILA/llava/model/llava_arch.py", line 488, in __embed_media_tokens
    embeds[name] = deque(self.encoders[name](media[name], media_config[name]))
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/yy/anaconda3/envs/vila/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/yy/anker/nvila/VILA/llava/model/encoders/video/tsp.py", line 64, in forward
    return [process_features(f) for f in features]
  File "/data/yy/anker/nvila/VILA/llava/model/encoders/video/tsp.py", line 64, in <listcomp>
    return [process_features(f) for f in features]
  File "/data/yy/anker/nvila/VILA/llava/model/encoders/video/tsp.py", line 41, in _process_features
    features = pool(features, p, dim=dim)
  File "/data/yy/anker/nvila/VILA/llava/model/encoders/video/tsp.py", line 12, in pool
    return x.view(x.shape[:dim] + (-1, size) + x.shape[dim + 1 :]).mean(dim + 1)
RuntimeError: shape '[-1, 8, 16, 16, 3584]' is invalid for input of size 6422528
2025-01-16 23:23:31.233 | ERROR    | __main__:cli_evaluate:348 - Error during evaluation: shape '[-1, 8, 16, 16, 3584]' is invalid for input of size 6422528. Please set `--verbosity=DEBUG` to get more information.

What is this TSPVideoEncoder? And how to avoid this error?