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Configuration error
| """Wrapper around HuggingFace APIs.""" | |
| import torch | |
| from typing import Any, Dict, List, Mapping, Optional | |
| from pydantic import BaseModel, Extra, root_validator | |
| from langchain.llms.base import LLM | |
| from langchain.llms.utils import enforce_stop_tokens | |
| from langchain.utils import get_from_dict_or_env | |
| from peft import PeftModel | |
| from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig | |
| DEFAULT_REPO_ID = "gpt2" | |
| VALID_TASKS = ("text2text-generation", "text-generation") | |
| class LlamaHuggingFace: | |
| def __init__(self, | |
| base_model, | |
| lora_model, | |
| task='text-generation', | |
| device='cpu', | |
| max_new_tokens=512, | |
| temperature=0.1, | |
| top_p=0.75, | |
| top_k=40, | |
| num_beams=1): | |
| self.task = task | |
| self.device = device | |
| self.temperature = temperature | |
| self.max_new_tokens = max_new_tokens | |
| self.top_p = top_p | |
| self.top_k = top_k | |
| self.num_beams = num_beams | |
| self.tokenizer = LlamaTokenizer.from_pretrained( | |
| base_model, use_fast=False) | |
| model = LlamaForCausalLM.from_pretrained( | |
| base_model, | |
| torch_dtype=torch.float16) | |
| self.model = PeftModel.from_pretrained( | |
| model, | |
| lora_model, | |
| torch_dtype=torch.float16) | |
| self.model.to(device) | |
| self.tokenizer.pad_token_id = 0 | |
| self.model.config.pad_token_id = 0 | |
| self.model.config.bos_token_id = 1 | |
| self.model.config.eos_token_id = 2 | |
| if device == "cpu": | |
| self.model.float() | |
| else: | |
| self.model.half() | |
| self.model.eval() | |
| def __call__(self, inputs, params): | |
| if inputs.endswith('Thought:'): | |
| inputs = inputs[:-len('Thought:')] | |
| inputs = inputs.replace('Observation:\n\nObservation:', 'Observation:') | |
| inputs = inputs + '### ASSISTANT:\n' | |
| input_ids = self.tokenizer(inputs, return_tensors="pt").to(self.device).input_ids | |
| generation_config = GenerationConfig( | |
| temperature=self.temperature, | |
| top_p=self.top_p, | |
| top_k=self.top_k, | |
| num_beams=self.num_beams) | |
| generate_ids = self.model.generate( | |
| input_ids=input_ids, | |
| generation_config=generation_config, | |
| max_new_tokens=self.max_new_tokens) | |
| response = self.tokenizer.batch_decode( | |
| generate_ids, | |
| skip_special_tokens=True, | |
| clean_up_tokenization_spaces=False) | |
| response = [res.replace('### ASSISTANT:\n', '') for res in response] | |
| response = [{'generated_text': res} for res in response] | |
| return response | |
| class Llama(LLM, BaseModel): | |
| """Wrapper around LLAMA models. | |
| """ | |
| client: Any #: :meta private: | |
| repo_id: str = DEFAULT_REPO_ID | |
| """Model name to use.""" | |
| task: Optional[str] = "text-generation" | |
| """Task to call the model with. Should be a task that returns `generated_text`.""" | |
| model_kwargs: Optional[dict] = None | |
| """Key word arguments to pass to the model.""" | |
| class Config: | |
| """Configuration for this pydantic object.""" | |
| extra = Extra.forbid | |
| def validate_environment(cls, values: Dict) -> Dict: | |
| """Validate that api key and python package exists in environment.""" | |
| repo_id = values["repo_id"] | |
| model_kwargs = values.get("model_kwargs") | |
| client = LlamaHuggingFace( | |
| base_model=model_kwargs.get("base_model"), | |
| lora_model=model_kwargs.get("lora_model"), | |
| task=values.get("task"), | |
| device=model_kwargs.get("device"), | |
| max_new_tokens=model_kwargs.get("max_new_tokens"), | |
| temperature=model_kwargs.get("temperature"), | |
| top_p=model_kwargs.get("top_p"), | |
| top_k=model_kwargs.get("top_k"), | |
| num_beams=model_kwargs.get("num_beams") | |
| ) | |
| if client.task not in VALID_TASKS: | |
| raise ValueError( | |
| f"Got invalid task {client.task}, " | |
| f"currently only {VALID_TASKS} are supported" | |
| ) | |
| values["client"] = client | |
| return values | |
| def _identifying_params(self) -> Mapping[str, Any]: | |
| """Get the identifying parameters.""" | |
| _model_kwargs = self.model_kwargs or {} | |
| return { | |
| **{"repo_id": self.repo_id, "task": self.task}, | |
| **{"model_kwargs": _model_kwargs}, | |
| } | |
| def _llm_type(self) -> str: | |
| """Return type of llm.""" | |
| return "huggingface_hub" | |
| def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: | |
| """Call out to HuggingFace Hub's inference endpoint. | |
| Args: | |
| prompt: The prompt to pass into the model. | |
| stop: Optional list of stop words to use when generating. | |
| Returns: | |
| The string generated by the model. | |
| Example: | |
| .. code-block:: python | |
| response = hf("Tell me a joke.") | |
| """ | |
| _model_kwargs = self.model_kwargs or {} | |
| response = self.client(inputs=prompt, params=_model_kwargs) | |
| if "error" in response: | |
| raise ValueError(f"Error raised by inference API: {response['error']}") | |
| if self.client.task == "text-generation": | |
| # Text generation return includes the starter text. | |
| text = response[0]["generated_text"][len(prompt) :] | |
| elif self.client.task == "text2text-generation": | |
| text = response[0]["generated_text"] | |
| else: | |
| raise ValueError( | |
| f"Got invalid task {self.client.task}, " | |
| f"currently only {VALID_TASKS} are supported" | |
| ) | |
| if stop is not None: | |
| # This is a bit hacky, but I can't figure out a better way to enforce | |
| # stop tokens when making calls to huggingface_hub. | |
| text = enforce_stop_tokens(text, stop) | |
| return text | |