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[Bug]: PixtralHF inference broken since #11396 #11726

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mgoin opened this issue Jan 3, 2025 · 4 comments · May be fixed by #11741
Open
1 task done

[Bug]: PixtralHF inference broken since #11396 #11726

mgoin opened this issue Jan 3, 2025 · 4 comments · May be fixed by #11741
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@DarkLight1337
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@mgoin
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mgoin commented Jan 3, 2025

Your current environment

The output of `python collect_env.py` 
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.31.2
Libc version: glibc-2.35

Python version: 3.12.4 (main, Jul 25 2024, 22:42:01) [Clang 18.1.8 ] (64-bit runtime)
Python platform: Linux-6.5.0-35-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 555.42.02
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             128
On-line CPU(s) list:                0-127
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8462Y+
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          2
Stepping:                           8
CPU max MHz:                        4100.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5600.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          3 MiB (64 instances)
L1i cache:                          2 MiB (64 instances)
L2 cache:                           128 MiB (64 instances)
L3 cache:                           120 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-31,64-95
NUMA node1 CPU(s):                  32-63,96-127
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] flashinfer==0.1.6+cu124torch2.4
[pip3] mypy==1.11.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.0
[pip3] sentence-transformers==3.2.1
[pip3] torch==2.5.1
[pip3] torchao==0.6.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.47.1
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.1.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.6.post2.dev60+ge1a5c2f0a
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    PIX     NODE    NODE    NODE    SYS     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    NODE    PIX     NODE    NODE    SYS     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    NODE    NODE    PIX     NODE    SYS     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    NODE    NODE    NODE    PIX     SYS     SYS     SYS     SYS     0-31,64-95      0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    SYS     SYS     SYS     SYS     PIX     NODE    NODE    NODE    32-63,96-127    1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    SYS     SYS     SYS     SYS     NODE    PIX     NODE    NODE    32-63,96-127    1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    SYS     SYS     SYS     SYS     NODE    NODE    PIX     NODE    32-63,96-127    1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      SYS     SYS     SYS     SYS     NODE    NODE    NODE    PIX     32-63,96-127    1               N/A
NIC0    PIX     NODE    NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE    SYS     SYS     SYS     SYS
NIC1    NODE    PIX     NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      NODE    NODE    SYS     SYS     SYS     SYS
NIC2    NODE    NODE    PIX     NODE    SYS     SYS     SYS     SYS     NODE    NODE     X      NODE    SYS     SYS     SYS     SYS
NIC3    NODE    NODE    NODE    PIX     SYS     SYS     SYS     SYS     NODE    NODE    NODE     X      SYS     SYS     SYS     SYS
NIC4    SYS     SYS     SYS     SYS     PIX     NODE    NODE    NODE    SYS     SYS     SYS     SYS      X      NODE    NODE    NODE
NIC5    SYS     SYS     SYS     SYS     NODE    PIX     NODE    NODE    SYS     SYS     SYS     SYS     NODE     X      NODE    NODE
NIC6    SYS     SYS     SYS     SYS     NODE    NODE    PIX     NODE    SYS     SYS     SYS     SYS     NODE    NODE     X      NODE
NIC7    SYS     SYS     SYS     SYS     NODE    NODE    NODE    PIX     SYS     SYS     SYS     SYS     NODE    NODE    NODE     X 

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7

LD_LIBRARY_PATH=/home/mgoin/venvs/vllm/lib/python3.12/site-packages/cv2/../../lib64:/usr/local/cuda-12.5/lib64:/usr/local/cuda-12.5/lib64:
CUDA_VISIBLE_DEVICES=0
CUDA_VISIBLE_DEVICES=0
CUDA_MODULE_LOADING=LAZY

Model Input Dumps

No response

🐛 Describe the bug

PixtralHF models fail when given non [1024x1024] images or when given multiple images due to a regression introduced in #11396.

I validated these errors exist for that commit on main (101418096ffe3c83b6d541e1303b10e9d5e03861) and don't exist for the commit before (5ce4627a7ec4cf4e19ff4be7f030883ef486393f).

Pixel values shape error with single image:

from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset

sampling_params = SamplingParams(temperature=0.0, max_tokens=10)

model_name = "nm-testing/pixtral-12b-FP8-dynamic"
llm = LLM(
    model=model_name,
    max_num_seqs=1,
    enforce_eager=True,
    max_model_len=10000,
)

image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
messages = [
    {
        "role":
        "user",
        "content": [
            {"type": "text", "text": "Describe the image."},
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]
outputs = llm.chat(messages, sampling_params=sampling_params)

for output in outputs:
    print(output.outputs[0].text)
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/model_runner_base.py", line 116, in _wrapper
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/model_runner.py", line 1691, in execute_model
[rank0]:     hidden_or_intermediate_states = model_executable(
[rank0]:                                     ^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/venvs/vllm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/venvs/vllm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/llava.py", line 542, in forward
[rank0]:     vision_embeddings = self.get_multimodal_embeddings(**kwargs)
[rank0]:                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/llava.py", line 473, in get_multimodal_embeddings
[rank0]:     image_input = self._parse_and_validate_image_input(**kwargs)
[rank0]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/llava.py", line 412, in _parse_and_validate_image_input
[rank0]:     data=self._validate_pixel_values(
[rank0]:          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/llava.py", line 391, in _validate_pixel_values
[rank0]:     raise ValueError(
[rank0]: ValueError: The expected shape of pixel values is ('batch_size', '3', '1024', '1024'). You supplied (1, 3, 672, 1024).

[rank0]: The above exception was the direct cause of the following exception:

[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/mgoin/code/vllm/pixtral_hf.py", line 243, in <module>
[rank0]:     pixtralhf_fp8_one_image()
[rank0]:   File "/home/mgoin/code/vllm/pixtral_hf.py", line 234, in pixtralhf_fp8_one_image
[rank0]:     outputs = llm.chat(messages, sampling_params=sampling_params)
[rank0]:               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/entrypoints/llm.py", line 701, in chat
[rank0]:     return self.generate(
[rank0]:            ^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/utils.py", line 1044, in inner
[rank0]:     return fn(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/entrypoints/llm.py", line 462, in generate
[rank0]:     outputs = self._run_engine(use_tqdm=use_tqdm)
[rank0]:               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/entrypoints/llm.py", line 1242, in _run_engine
[rank0]:     step_outputs = self.llm_engine.step()
[rank0]:                    ^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/engine/llm_engine.py", line 1390, in step
[rank0]:     outputs = self.model_executor.execute_model(
[rank0]:               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/executor/gpu_executor.py", line 88, in execute_model
[rank0]:     output = self.driver_worker.execute_model(execute_model_req)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/worker_base.py", line 343, in execute_model
[rank0]:     output = self.model_runner.execute_model(
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/venvs/vllm/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/model_runner_base.py", line 152, in _wrapper
[rank0]:     raise type(err)(
[rank0]: ValueError: Error in model execution (input dumped to /tmp/err_execute_model_input_20250103-200404.pkl): The expected shape of pixel values is ('batch_size', '3', '1024', '1024'). You supplied (1, 3, 672, 1024).

Multi-image error with flatten_bn:

from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset

sampling_params = SamplingParams(temperature=0.0, max_tokens=10)

model_name = "nm-testing/pixtral-12b-FP8-dynamic"
llm = LLM(model=model_name,
            max_num_seqs=4,
            enforce_eager=True,
            max_model_len=30000,
            limit_mm_per_prompt={"image": 4})

image1 = ImageAsset("cherry_blossom").pil_image.convert("RGB")
image2 = ImageAsset("stop_sign").pil_image.convert("RGB")
input1 = {
    "prompt": "<s>[INST][IMG]Describe the image.[/INST]",
    "multi_modal_data": {"image": image1},
}
input2 = {
    "prompt": "<s>[INST][IMG][IMG][IMG][IMG]Describe the images.[/INST]",
    "multi_modal_data": {"image": [image1, image2, image1, image2]},
}
input3 = {
    "prompt": "<s>[INST][IMG][IMG]Are the images the same?[/INST]",
    "multi_modal_data": {"image": [image1, image1]},
}
outputs = llm.generate([input1, input2, input3], sampling_params=sampling_params)

for output in outputs:
    print(output.outputs[0].text)
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/model_runner_base.py", line 116, in _wrapper
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/model_runner.py", line 1691, in execute_model
[rank0]:     hidden_or_intermediate_states = model_executable(
[rank0]:                                     ^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/venvs/vllm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/venvs/vllm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/llava.py", line 542, in forward
[rank0]:     vision_embeddings = self.get_multimodal_embeddings(**kwargs)
[rank0]:                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/llava.py", line 473, in get_multimodal_embeddings
[rank0]:     image_input = self._parse_and_validate_image_input(**kwargs)
[rank0]:                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/llava.py", line 413, in _parse_and_validate_image_input
[rank0]:     flatten_bn(pixel_values, concat=True)),
[rank0]:     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/model_executor/models/utils.py", line 298, in flatten_bn
[rank0]:     return torch.cat(x)
[rank0]:            ^^^^^^^^^^^^
[rank0]: TypeError: expected Tensor as element 1 in argument 0, but got list

[rank0]: The above exception was the direct cause of the following exception:

[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/mgoin/code/vllm/pixtral_hf.py", line 248, in <module>
[rank0]:     pixtralhf_four_image()
[rank0]:   File "/home/mgoin/code/vllm/pixtral_hf.py", line 199, in pixtralhf_four_image
[rank0]:     outputs = llm.generate([input1, input2, input3], sampling_params=sampling_params)
[rank0]:               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/utils.py", line 1044, in inner
[rank0]:     return fn(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/entrypoints/llm.py", line 462, in generate
[rank0]:     outputs = self._run_engine(use_tqdm=use_tqdm)
[rank0]:               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/entrypoints/llm.py", line 1242, in _run_engine
[rank0]:     step_outputs = self.llm_engine.step()
[rank0]:                    ^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/engine/llm_engine.py", line 1390, in step
[rank0]:     outputs = self.model_executor.execute_model(
[rank0]:               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/executor/gpu_executor.py", line 88, in execute_model
[rank0]:     output = self.driver_worker.execute_model(execute_model_req)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/worker_base.py", line 343, in execute_model
[rank0]:     output = self.model_runner.execute_model(
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/venvs/vllm/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/mgoin/code/vllm/vllm/worker/model_runner_base.py", line 152, in _wrapper
[rank0]:     raise type(err)(
[rank0]: TypeError: Error in model execution (input dumped to /tmp/err_execute_model_input_20250103-200236.pkl): expected Tensor as element 1 in argument 0, but got list

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@mgoin mgoin added the bug Something isn't working label Jan 3, 2025
@DarkLight1337 DarkLight1337 self-assigned this Jan 4, 2025
@DarkLight1337
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Looks like the error occurs because we now call _validate_pixel_values even for Pixtral-HF inputs. Does the model work correctly if you remove that check?

@mgoin
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mgoin commented Jan 4, 2025

@DarkLight1337 that might fix the first example but for the second example the issue is that the logic for reassembling multiple images was removed entirely

@DarkLight1337
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To simplify things, let's try to keep this logic inside the multi-modal processor instead of the model itself.

@DarkLight1337 DarkLight1337 linked a pull request Jan 4, 2025 that will close this issue
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DarkLight1337 commented Jan 4, 2025 •
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I have opened a PR to fix it, but have only handled single-image input so far. Can you add the example to examples/offline_inference_vision_language_multi_image.py for easier testing? I have to go now. Thanks!

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[Bugfix] Fix image input for Pixtral-HF DarkLight1337/vllm
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@mgoin @DarkLight1337