[MODEL] Qwen Multimodal Support (Qwen-VL / Qwen-VL-Chat)

[alex-jw-brooks]

FIX #962
FIX #7017
FIX #7192

Currently Qwen models in VLLM skip loading the visual transformer weights. This PR adds support for loading the visual weights (if they're present) and adds multimodal support, e.g., for qwen-vl and qwen-vl-chat.

This PR only concerns Qwen-VL (version 1). For Qwen2-VL, please refer to #7905.

Summary:

• Adds multimodal input mapper/processor for Qwen models
• Ports the visual encoder from qwen-vl/chat
• Only only initializes the visual model and processes multimodal components if the model has a visual config
• Enables .chat for qwen models, adds an example for qwen-vl to the offline visual language samples
• Switches the existing Qwen test to Qwen/Qwen-7B-Chat to make sure we can still load non multimodal Qwen models

Some examples that may be helpful:

i. Running qwen-vl as a model in the offline inference vision language examples:

$ python examples/offline_inference_vision_language.py --model_type qwen_vl

Sample output The Tokyo Skytree tower is seen through cherry blossoms.

ii. Example of running a text only model:

from vllm import LLM, SamplingParams llm = LLM(model="Qwen/Qwen-7B-Chat", trust_remote_code=True) prompt = "<|im_start|>user\nWho were the founders of Microsoft?\n<|im_end|>\n<|im_start|>assistant\n" stop_token_ids = None sampling_params = SamplingParams(temperature=0.2, max_tokens=64, stop_token_ids=stop_token_ids) inputs = [{"prompt": prompt}] outputs = llm.generate(inputs, sampling_params=sampling_params) for o in outputs: generated_text = o.outputs[0].text print(generated_text)

Sample output: Microsoft was founded by Bill Gates and Paul Allen in 1975.<|im_end|>

iii. Visual embeddings example
Multiple pictures may be passed as embeddings. In general, these should be of shape # image, 256, 4096, since Qwen-vl/chat encode images into fixed 256 token contexts. Sample and output below.

from vllm import LLM, SamplingParams import torch # Embeddings for 2 images (i.e., [2, 256, 4096]) # One of these images it the VLLM tokyo skytree pic, the other is # the example used in Qwen model docs of a girl and her dog. embeds = torch.load(...) llm = LLM(model="Qwen/Qwen-VL-Chat", trust_remote_code=True) # NOTE: You don't need to put anything between <img> / </img> since in VLLM, # the loaded multimodal data is provided separately. get_img_prompt = lambda img_num: f"Picture {img_num}: <img></img>\n" prompt = f"<|im_start|>Picture 1: {get_img_prompt(1)} {get_img_prompt(2)} Can you compare these two pictures in english?\n<|im_end|>\n<|im_start|>assistant\n" stop_token_ids = None sampling_params = SamplingParams(temperature=0.2, max_tokens=64, stop_token_ids=stop_token_ids) inputs = [{"prompt": prompt, "multi_modal_data": {"image": embeds}}] outputs = llm.generate(inputs, sampling_params=sampling_params) for o in outputs: generated_text = o.outputs[0].text print(generated_text)

Sample output: Picture 1 is of a woman sitting on the beach with her dog, both of them holding hands and smiling at each other. Picture 2 is of the Tokyo Skytree tower in Japan, surrounded by pink cherry blossom trees.<|im_end|>

iv. Chat example
Here's an example of calling qwen-vl-chat with an image with OpenAPI and the sample chatml template.

Start the server:

python vllm/entrypoints/openai/api_server.py \ --device cuda \ --model Qwen/Qwen-VL-Chat \ --tokenizer Qwen/Qwen-VL-Chat \ --trust-remote-code \ --api-key token-abc123 \ --chat-template examples/template_chatml.jinja &

Client example:

from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123") completion = client.chat.completions.create( model="Qwen/Qwen-VL-Chat", messages=[ { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"}}, {"type": "text", "text": "Describe this image in English. "}, ] } ] ) print(completion.choices[0].message)

Example Response:

ChatCompletionMessage(content="A radar chart is shown with several axes, including 'VQA2v3', 'GQA', 'LmivaBench', 'SEED-Bench', 'VizWiz', 'SQA-IMG', 'MMBench-CN', 'TextVQA', 'BLIP-2', 'InstructBLIP', 'Qwen-VL-Chat', and 'LLA-VA.1.5'. Each axis has a value associated with it, with 'VQA2v3' being the highest, and 'LmivaBench' being the lowest. Some axes also have negative values.<|im_end|>\n<|im_start|>\n", refusal=None, role='assistant', function_call=None, tool_calls=[])

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[github-actions]

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[alex-jw-brooks]

/ready

[alex-jw-brooks]

Cool, sounds good, thanks @DarkLight1337! 🤞

I saw you had resolved this comment: #8029 (comment) - I added parallel linear layers for the MLP in the visual encoder, but am still trying to rework the VisualAttention to use QKVParallelLinear and memory_efficient_attention_forward from xformers, like most of the other visual encoders implemented in VLLM currently do.

Did you want me to try to make that get that into this PR once the test is resolved, or would it be better off in a follow-up PR to optimize this model? I think the rest of changes should be taken care of 🙂

[DarkLight1337]

Cool, sounds good, thanks @DarkLight1337! 🤞

I saw you had resolved this comment: #8029 (comment) - I added parallel linear layers for the MLP in the visual encoder, but am still trying to rework the VisualAttention to use QKVParallelLinear and memory_efficient_attention_forward from xformers, like most of the other visual encoders implemented in VLLM currently do.

Did you want me to try to make that get that into this PR once the test is resolved, or would it be better off in a follow-up PR to optimize this model? I think the rest of changes should be taken care of 🙂

I wanted to parallelize the MLP first as it's easier. We can parallelize the attention module layer in another PR as it's a bit more complicated.

[alex-jw-brooks]

Cool, that sounds good to me!

[DarkLight1337]

For the dummy data, you should pad the input with text tokens so that (combined with the image tokens) there are at least a total of seq_len tokens. You can see dummy_seq_data_for_clip for an example.

[alex-jw-brooks]

Nice catch! Pushed the fix to pad it if the image prompt isn't long enough

[DarkLight1337]

VLM tests pass now. Thanks again for your effort!

[DarkLight1337]

The PR has been merged. Some follow-ups to be done:

• TP support for vision encoder, particularly the transformer module.
• Testing multi-image input for Qwen-VL so we can officially support it in the docs.

[zhangfan-algo]

Can we support qwen2-vl-7B?

[DarkLight1337]

Can we support qwen2-vl-7B?

It is WIP in #7905

[syngokhan]

I want to launch the model as an API server. But I am catching this as an error. Here I have downloaded and installed vllm in its latest form. (v.0.6.0 and after)

(image) root@gapxivrgpup03:/home/glb90108385# CUDA_VISIBLE_DEVICES=1 python /opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py --model /opt/GPT/MODELS/Qwen2-VL-7B-Instruct/ --host 10.12.112.162 --port 9002 --tensor-parallel-size 1 --trust-remote-code --max-model-len 32000 --enforce-eager --gpu-memory-utilization 1.0 INFO 09-06 06:36:52 api_server.py:495] vLLM API server version 0.6.0 INFO 09-06 06:36:52 api_server.py:496] args: Namespace(host='10.12.112.162', port=9002, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_auto_tool_choice=False, tool_call_parser=None, model='/opt/GPT/MODELS/Qwen2-VL-7B-Instruct/', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=32000, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=1.0, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None) Traceback (most recent call last): File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 531, in <module> asyncio.run(run_server(args)) File "/opt/anaconda3/envs/image/lib/python3.10/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/opt/anaconda3/envs/image/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete return future.result() File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 498, in run_server async with build_async_engine_client(args) as async_engine_client: File "/opt/anaconda3/envs/image/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 110, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/opt/anaconda3/envs/image/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 132, in build_async_engine_client_from_engine_args if (model_is_embedding(engine_args.model, engine_args.trust_remote_code, File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 73, in model_is_embedding return ModelConfig(model=model_name, File "/opt/VLLM_IMAGE/vllm/vllm/config.py", line 224, in __init__ self.max_model_len = _get_and_verify_max_len( File "/opt/VLLM_IMAGE/vllm/vllm/config.py", line 1740, in _get_and_verify_max_len assert "factor" in rope_scaling AssertionError 

[DarkLight1337]

I want to launch the model as an API server. But I am catching this as an error. Here I have downloaded and installed vllm in its latest form. (v.0.6.0 and after)

(image) root@gapxivrgpup03:/home/glb90108385# CUDA_VISIBLE_DEVICES=1 python /opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py --model /opt/GPT/MODELS/Qwen2-VL-7B-Instruct/ --host 10.12.112.162 --port 9002 --tensor-parallel-size 1 --trust-remote-code --max-model-len 32000 --enforce-eager --gpu-memory-utilization 1.0 INFO 09-06 06:36:52 api_server.py:495] vLLM API server version 0.6.0 INFO 09-06 06:36:52 api_server.py:496] args: Namespace(host='10.12.112.162', port=9002, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_auto_tool_choice=False, tool_call_parser=None, model='/opt/GPT/MODELS/Qwen2-VL-7B-Instruct/', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=32000, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=1.0, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None) Traceback (most recent call last): File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 531, in <module> asyncio.run(run_server(args)) File "/opt/anaconda3/envs/image/lib/python3.10/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/opt/anaconda3/envs/image/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete return future.result() File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 498, in run_server async with build_async_engine_client(args) as async_engine_client: File "/opt/anaconda3/envs/image/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 110, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/opt/anaconda3/envs/image/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 132, in build_async_engine_client_from_engine_args if (model_is_embedding(engine_args.model, engine_args.trust_remote_code, File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 73, in model_is_embedding return ModelConfig(model=model_name, File "/opt/VLLM_IMAGE/vllm/vllm/config.py", line 224, in __init__ self.max_model_len = _get_and_verify_max_len( File "/opt/VLLM_IMAGE/vllm/vllm/config.py", line 1740, in _get_and_verify_max_len assert "factor" in rope_scaling AssertionError 

This PR only adds support for Qwen-VL (version 1). For Qwen2-VL, please refer to #7905 .

[zhangfan-algo]

I want to launch the model as an API server. But I am catching this as an error. Here I have downloaded and installed vllm in its latest form. (v.0.6.0 and after)

(image) root@gapxivrgpup03:/home/glb90108385# CUDA_VISIBLE_DEVICES=1 python /opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py --model /opt/GPT/MODELS/Qwen2-VL-7B-Instruct/ --host 10.12.112.162 --port 9002 --tensor-parallel-size 1 --trust-remote-code --max-model-len 32000 --enforce-eager --gpu-memory-utilization 1.0 INFO 09-06 06:36:52 api_server.py:495] vLLM API server version 0.6.0 INFO 09-06 06:36:52 api_server.py:496] args: Namespace(host='10.12.112.162', port=9002, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_auto_tool_choice=False, tool_call_parser=None, model='/opt/GPT/MODELS/Qwen2-VL-7B-Instruct/', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=True, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=32000, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=1.0, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=True, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_model=None, speculative_model_quantization=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, disable_logprobs_during_spec_decoding=None, model_loader_extra_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, override_neuron_config=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None) Traceback (most recent call last): File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 531, in <module> asyncio.run(run_server(args)) File "/opt/anaconda3/envs/image/lib/python3.10/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/opt/anaconda3/envs/image/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete return future.result() File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 498, in run_server async with build_async_engine_client(args) as async_engine_client: File "/opt/anaconda3/envs/image/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 110, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/opt/anaconda3/envs/image/lib/python3.10/contextlib.py", line 199, in __aenter__ return await anext(self.gen) File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 132, in build_async_engine_client_from_engine_args if (model_is_embedding(engine_args.model, engine_args.trust_remote_code, File "/opt/VLLM_IMAGE/vllm/vllm/entrypoints/openai/api_server.py", line 73, in model_is_embedding return ModelConfig(model=model_name, File "/opt/VLLM_IMAGE/vllm/vllm/config.py", line 224, in __init__ self.max_model_len = _get_and_verify_max_len( File "/opt/VLLM_IMAGE/vllm/vllm/config.py", line 1740, in _get_and_verify_max_len assert "factor" in rope_scaling AssertionError 

This PR only adds support for Qwen-VL (version 1). For Qwen2-VL, please refer to #7905 .

I still have the same bug after pulling down the latest github code

[DarkLight1337]

It's not a bug. Qwen2-VL hasn't been added to vLLM yet. Please read my above comment.

[zhangfan-algo]

When do we expect to support qwen2 vl series?

[DarkLight1337]

We are waiting for transformers to update so that we can load Qwen2-VL from their config directly.

[MotorBottle]

FIX #962 FIX #7017 FIX #7192

Currently Qwen models in VLLM skip loading the visual transformer weights. This PR adds support for loading the visual weights (if they're present) and adds multimodal support, e.g., for qwen-vl and qwen-vl-chat.

This PR only concerns Qwen-VL (version 1). For Qwen2-VL, please refer to #7905.

Summary:

• Adds multimodal input mapper/processor for Qwen models
• Ports the visual encoder from qwen-vl/chat
• Only only initializes the visual model and processes multimodal components if the model has a visual config
• Enables .chat for qwen models, adds an example for qwen-vl to the offline visual language samples
• Switches the existing Qwen test to Qwen/Qwen-7B-Chat to make sure we can still load non multimodal Qwen models

Some examples that may be helpful:

i. Running qwen-vl as a model in the offline inference vision language examples:

$ python examples/offline_inference_vision_language.py --model_type qwen_vl

Sample output The Tokyo Skytree tower is seen through cherry blossoms.

ii. Example of running a text only model:

from vllm import LLM, SamplingParams llm = LLM(model="Qwen/Qwen-7B-Chat", trust_remote_code=True) prompt = "<|im_start|>user\nWho were the founders of Microsoft?\n<|im_end|>\n<|im_start|>assistant\n" stop_token_ids = None sampling_params = SamplingParams(temperature=0.2, max_tokens=64, stop_token_ids=stop_token_ids) inputs = [{"prompt": prompt}] outputs = llm.generate(inputs, sampling_params=sampling_params) for o in outputs: generated_text = o.outputs[0].text print(generated_text)

Sample output: Microsoft was founded by Bill Gates and Paul Allen in 1975.<|im_end|>

iii. Visual embeddings example Multiple pictures may be passed as embeddings. In general, these should be of shape # image, 256, 4096, since Qwen-vl/chat encode images into fixed 256 token contexts. Sample and output below.

from vllm import LLM, SamplingParams import torch # Embeddings for 2 images (i.e., [2, 256, 4096]) # One of these images it the VLLM tokyo skytree pic, the other is # the example used in Qwen model docs of a girl and her dog. embeds = torch.load(...) llm = LLM(model="Qwen/Qwen-VL-Chat", trust_remote_code=True) # NOTE: You don't need to put anything between <img> / </img> since in VLLM, # the loaded multimodal data is provided separately. get_img_prompt = lambda img_num: f"Picture {img_num}: <img></img>\n" prompt = f"<|im_start|>Picture 1: {get_img_prompt(1)} {get_img_prompt(2)} Can you compare these two pictures in english?\n<|im_end|>\n<|im_start|>assistant\n" stop_token_ids = None sampling_params = SamplingParams(temperature=0.2, max_tokens=64, stop_token_ids=stop_token_ids) inputs = [{"prompt": prompt, "multi_modal_data": {"image": embeds}}] outputs = llm.generate(inputs, sampling_params=sampling_params) for o in outputs: generated_text = o.outputs[0].text print(generated_text)

Sample output: Picture 1 is of a woman sitting on the beach with her dog, both of them holding hands and smiling at each other. Picture 2 is of the Tokyo Skytree tower in Japan, surrounded by pink cherry blossom trees.<|im_end|>

iv. Chat example Here's an example of calling qwen-vl-chat with an image with OpenAPI and the sample chatml template.

Start the server:

python vllm/entrypoints/openai/api_server.py \ --device cuda \ --model Qwen/Qwen-VL-Chat \ --tokenizer Qwen/Qwen-VL-Chat \ --trust-remote-code \ --api-key token-abc123 \ --chat-template examples/template_chatml.jinja &

Client example:

from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123") completion = client.chat.completions.create( model="Qwen/Qwen-VL-Chat", messages=[ { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"}}, {"type": "text", "text": "Describe this image in English. "}, ] } ] ) print(completion.choices[0].message)

Example Response:

ChatCompletionMessage(content="A radar chart is shown with several axes, including 'VQA2v3', 'GQA', 'LmivaBench', 'SEED-Bench', 'VizWiz', 'SQA-IMG', 'MMBench-CN', 'TextVQA', 'BLIP-2', 'InstructBLIP', 'Qwen-VL-Chat', and 'LLA-VA.1.5'. Each axis has a value associated with it, with 'VQA2v3' being the highest, and 'LmivaBench' being the lowest. Some axes also have negative values.<|im_end|>\n<|im_start|>\n", refusal=None, role='assistant', function_call=None, tool_calls=[])

BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE

PR Checklist (Click to Expand)

Hi and I followed the sample code to deploy Qwen-VL-Chat with vllm docker. While deployment was successful, I kept getting ou of vocabulary OOV errors no matter how I test my inputs.

How I deployed:

sudo docker run --runtime nvidia --gpus '"device=0,1"' --ipc=host -p 18434:8000 -v hf_cache:/root/.cache/huggingface -d -e HF_ENDPOINT=https://hf-mirror.com -e HF_HUB_ENABLE_HF_TRANSFER=0 --name Qwen-VL-Chat vllm/vllm-openai:latest --model Qwen/Qwen-VL-Chat --tokenizer Qwen/Qwen-VL-Chat --tensor-parallel-size 2 --trust-remote-code --chat-template examples/template_chatml.jinja --dtype='half' 

Error msg:

Error in API call: 400 {"object":"error","message":"Token id 151859 is out of vocabulary","type":"BadRequestError","param":null,"code":400} 

Test code:

import requests import base64 import time # Function to encode the image to base64 def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode("utf-8") def main(): # Path to your image image_path = "test2.jpg" base64_image = encode_image(image_path) # API configuration api_base = "http://192.168.50.18:18434/v1/chat/completions" model_name = "Qwen/Qwen-VL-Chat" # Input prompt user_prompt_text = ( "What's inside the image?" ) # Prepare the payload payload_template = { "model": model_name, "messages": [ { "role": "user", "content": [ # {"type": "image_url", "image_url": {"url": "https://i.imgur.com/T3S0cvu.jpeg"}}, {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}, {"type": "text", "text": user_prompt_text} ] } ], "max_tokens": 300 } for i in range(1, 2): print(f"===== API called {i} times =====") startTime = time.time() response = requests.post(api_base, json=payload_template) if response.status_code != 200: print("Error in API call:", response.status_code, response.text) else: completion = response.json()["choices"][0]["message"]["content"] tokens = response.json()["usage"]["prompt_tokens"] print("Model Response:", completion) print("tokens:", tokens) print("time used: {:.2f} 秒".format(time.time() - startTime)) print() if __name__ == "__main__": main() 

I tried to search the whole observable web and could not find any similar case. So I'm replying here for possible help.

Much appreciated!