[Model]: Add transformers backend support (#11330)

# Adds support for `transformers` as a backend

Following https://github.com/huggingface/transformers/pull/35235, a
bunch of models should already be supported, we are ramping up support
for more models.

Thanks @Isotr0py for the TP support, and @hmellor for his help as well!
This includes: 
- `trust_remote_code=True` support: any model on the hub, if it
implements attention the correct way can be natively supported!!
- tensor parallel support

---------

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Signed-off-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Isotr0py <41363108+Isotr0py@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/source/models/supported_models.md

@@ -40,6 +40,82 @@ If vLLM successfully returns text (for generative models) or hidden states (for
 Otherwise, please refer to [Adding a New Model](#new-model) for instructions on how to implement your model in vLLM.
 Alternatively, you can [open an issue on GitHub](https://github.com/vllm-project/vllm/issues/new/choose) to request vLLM support.
 
+### Transformers fallback
+
+After the merge of <gh-pr:11330>, `vllm` can fallback to models that are available in `transformers`. This does not work for all models for now, but most decoder language models are supported, and vision language model support is planned!
+
+To check if the backend is `transformers`, you can simply do this:
+
+```python 
+from vllm import LLM
+llm = LLM(model=..., task="generate")  # Name or path of your model
+llm.apply_model(lambda model: print(model.__class__))
+```
+
+If it is `TransformersModel` then it means it's based on `transformers`!
+
+#### Supported features
+
+##### LORA and quantization
+
+Both are not supported yet! Make sure to open an issue and we'll work on this together with the `transformers` team!
+
+Usually `transformers` model load weights via the `load_adapters` API, that depends on PEFT. We need to work a bit to either use this api (for now this would result in some weights not being marked as loaded) or replace modules accordingly.
+
+Hints as to how this would look like:
+
+```python
+class TransformersModel(nn.Module, SupportsLoRA):
+  def __init__(*):
+    ...
+    self.model.load_adapter(vllm_config.load_config.model_loader_extra_config["qlora_adapter_name_or_path"])
+```
+
+Blocker is that you need to specify supported lora layers, when we would ideally want to load whatever is inside the checkpoint!
+
+##### Remote code
+
+This fallback also means that any model on the hub that can be used in `transformers` with `trust_remote_code=True` that correctly implements attention can be used in production!
+
+```python 
+from vllm import LLM
+llm = LLM(model=..., task="generate", trust_remote_code=True)  # Name or path of your model
+llm.apply_model(lambda model: print(model.__class__))
+```
+
+A model just needs the following two things:
+
+```python
+from transformers import PreTrainedModel
+from torch import nn
+
+class MyAttention(nn.Module):
+
+  def forward(self, hidden_states, **kwargs): # <- kwargs are required
+
+    ...
+    attention_interface = attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+    attn_output, attn_weights = attention_interface(
+      self,
+      query_states,
+      key_states,
+      value_states,
+      **kwargs,
+    )
+    ...
+
+class MyModel(PreTrainedModel):
+  _supports_attention_backend = True
+```
+
+Here is what happens in the background:
+
+1. The config is loaded
+2. `MyModel` python class is loaded from the `auto_map`, and we check that the model `_supports_attention_backend`.
+3. The `TransformersModel` backend is used. See `/model_executors/models/transformers`, which leverage `self.config._attn_implementation = "vllm"`, thus the need to use `ALL_ATTENTION_FUNCTION`.
+
+That's it!
+
 ### ModelScope
 
 To use models from [ModelScope](https://www.modelscope.cn) instead of HuggingFace Hub, set an environment variable: