Qwen2-VL Doesn't Execute on TPUs

[radna0]

System Info

• transformers version: 4.45.0.dev0
• Platform: Linux-5.4.0-1043-gcp-x86_64-with-glibc2.31
• Python version: 3.10.14
• Huggingface_hub version: 0.24.6
• Safetensors version: 0.4.4
• Accelerate version: 0.33.0
• Accelerate config: not found
• PyTorch version (GPU?): 2.5.0.dev20240830+cpu (False)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using distributed or parallel set-up in script?:

Who can help?

No response

Information

• [x] The official example scripts
• [ ] My own modified scripts

Tasks

• [ ] An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• [ ] My own task or dataset (give details below)

Reproduction

Following this Qwen2-VL guide => https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct#quickstart

1. Script

   
   from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
   from qwen_vl_utils import process_vision_info
   import numpy as np
   import torch
   import torch_xla as xla
   import torch_xla.core.xla_model as xm
   import torch_xla.distributed.spmd as xs

from torch.distributed._tensor import DeviceMesh, distribute_module from torch_xla.distributed.spmd import auto_policy

from torch_xla import runtime as xr from torch_xla.experimental.spmd_fully_sharded_data_parallel import ( _prepare_spmd_partition_spec, SpmdFullyShardedDataParallel as FSDPv2, )

import time

start = time.time()

device = xm.xla_device()

default: Load the model on the available device(s)

model = Qwen2VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2-VL-2B-Instruct", torch_dtype=torch.bfloat16, attn_implementation="eager", ).to(device)

print(model.device)

default processer

processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4")

message = [ { "role": "user", "content": [ { "type": "image", "image": "image1.jpg", }, {"type": "text", "text": "Describe this image in detail."}, ], } ]

allmessages = [[message] for in range(1)] for messages in all_messages:

# Preparation for inference
texts = [
    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
    for msg in messages
]

image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=texts,
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
    out_ids[len(in_ids) :]
    for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False,
)
for i, text in enumerate(output_text):
    print(f"Output {i}: {text}")

print(f"Time taken: {time.time() - start}")

2. Output Logs

kojoe@t1v-n-cb70f560-w-0:~/EasyAnimate/easyanimate/image_caption$ python caption.py WARNING:root:libtpu.so and TPU device found. Setting PJRT_DEVICE=TPU. Loading checkpoint shards: 100%|██████████████████████████████████████████████████| 2/2 [00:00<00:00, 4.39it/s] xla:0

### Expected behavior

The model works fine when chaging ```device``` to ```"cpu"```, but stuck executing on TPUs. The model should run on TPUs

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[ArthurZucker]

Hey! Don't think we officially test nor support TPU for this model 🤗 I can't really reproduce 😢 @tengomucho might have an idea

[tengomucho]

@radna0 transformers does not support officially TPU, but I think things might work if:

• you use the raw model forward API, not generate
• you use the static KV cache, that works best on TPUs with Pytorch XLA. I haven't tried this model myself, but you can try, using inspiration from this script. If it does not work, feel free to open a PR on optimum-tpu and we will try to help you.