Add LlamaForSequenceClassification model

[ariaattar]

[Model] Add LlamaForSequenceClassification model

This PR adds the LlamaForSequenceClassification model to vLLM, adapted from the Hugging Face Transformers implementation. This addition expands vLLM's capabilities to include sequence classification tasks using the Llama architecture.

Specific changes:

1. Implemented LlamaForSequenceClassification based on the Hugging Face Transformers code.
2. Adapted the model to work within the vLLM framework.
3. Added necessary modifications to ensure compatibility with vLLM's architecture.

Notes on implementation:

• The compute_logits method currently returns hidden_states to allow proper functioning.
• Returning last_hidden_state (hidden_states[:, -1]) causes a shape mismatch issue.

TODO:

• Resolve the shape mismatch issue when using last_hidden_state in compute_logits.
• Add appropriate tests for the new model.
• Update documentation to include usage instructions for LlamaForSequenceClassification.
• Add the model in __init__.py.

Reference:
Adapted from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py

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[DarkLight1337]

Regarding how to support this in the core framework, you can look at #6260

[DarkLight1337]

Update: See #9704

[KamranMK]

Any updates on this PR?

[Shiguang-Guo]

hi! any updates?

[DarkLight1337]

Since #11469 , we automatically convert the existing LlamaForCausalLM model to a classification model if you pass --task classify, so this PR isn't necessary anymore. Still, thanks for your efforts!

[Shiguang-Guo]

model_name = "Skywork/Skywork-Reward-Llama-3.1-8B-v0.2"
llm = LLM(model=model_name, task="classify", enforce_eager=True, )  

I have installed vllm from the latest code but still get incompatibility errors.Did I missed anything?

INFO 01-09 22:16:06 __init__.py:179] Automatically detected platform cuda.
Traceback (most recent call last):
  File "HOMEPATH/temp.py", line 5, in <module>
    llm = LLM(model=model_name, task="classify", enforce_eager=True, )  # Name or path of your model
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/utils.py", line 1044, in inner
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/entrypoints/llm.py", line 228, in __init__
    self.llm_engine = self.engine_class.from_engine_args(
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/engine/llm_engine.py", line 514, in from_engine_args
    engine_config = engine_args.create_engine_config(usage_context)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/engine/arg_utils.py", line 1043, in create_engine_config
    model_config = self.create_model_config()
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/engine/arg_utils.py", line 969, in create_model_config
    return ModelConfig(
           ^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/config.py", line 342, in __init__
    self.multimodal_config = self._init_multimodal_config(
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/config.py", line 398, in _init_multimodal_config
    if ModelRegistry.is_multimodal_model(architectures):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/model_executor/models/registry.py", line 428, in is_multimodal_model
    model_cls, _ = self.inspect_model_cls(architectures)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/model_executor/models/registry.py", line 388, in inspect_model_cls
    return self._raise_for_unsupported(architectures)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "HOMEPATH/vllm/vllm/model_executor/models/registry.py", line 349, in _raise_for_unsupported
    raise ValueError(
ValueError: Model architectures ['LlamaForSequenceClassification'] are not supported for now. Supported architectures: dict_keys(['AquilaModel', 'AquilaForCausalLM', 'ArcticForCausalLM', ...

[DarkLight1337]

Can you try setting --hf-overrides {"architectures": ["LlamaForCausalLM"]} so it uses the existing model definition in vLLM?

[Shiguang-Guo]

llm = LLM(model=model_name, task="classify", enforce_eager=True, hf_overrides={"architectures": ["LlamaForCausalLM"]})

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

outputs = llm.classify(prompts)

# Print the outputs.
for prompt, output in zip(prompts, outputs):
    probs = output.outputs.probs
    probs_trimmed = ((str(probs[:16])[:-1] +
                      ", ...]") if len(probs) > 16 else probs)
    print(f"Prompt: {prompt!r} | "
          f"Class Probabilities: {probs_trimmed} (size={len(probs)})")

No matter what I input, it always outputs 1.0

Prompt: 'Hello, my name is' | Class Probabilities: [1.0] (size=1)
Prompt: 'The president of the United States is' | Class Probabilities: [1.0] (size=1)
Prompt: 'The capital of France is' | Class Probabilities: [1.0] (size=1)
Prompt: 'The future of AI is' | Class Probabilities: [1.0] (size=1)

[DarkLight1337]

You should turn off softmax for your model. Try passing --override-pooler-config '{"softmax": false}'.

[Shiguang-Guo]

It works! thank you for your patience, wish you a good day