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from transformers import Pipeline, AutoTokenizer, AutoModelForCausalLM
import torch
import re
from contextlib import nullcontext
from neurons.miners.model.prompts import (
RELEVANCY_PROMPT,
HALLUCINATION_PROMPT,
HALLUCINATION_MISTAKES_PROMPT,
ATTRIBUTION_PROMPT,
ATTRIBUTION_MISTAKES_PROMPT,
SUMMARY_COMPLETENESS_PROMPT,
SUMMARY_MISTAKES_PROMPT
)
import time
class DeValPipeline(Pipeline):
def __init__(self, model=None, tokenizer=None, model_dir = None, **kwargs):
self.max_tokens = 250
self.temperature = 0.5
self.top_p = 0.95
self.top_k = 0
self.system_prompt = "You are an evaluation LLM. Your job is generate a score demonstrating how well the LLM you are evaluating responded and to identify its mistakes."
# init tokenizer and model then attach
self.device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForCausalLM.from_pretrained(
model_dir,
device_map=self.device,
torch_dtype=torch.float16 if self.device == "cuda" else torch.float32
)
print(f"putting model to {self.device}")
super().__init__(model=model, tokenizer=tokenizer, **kwargs)
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
for k, v in kwargs.items():
preprocess_kwargs[k] = kwargs[k]
return preprocess_kwargs, {}, {}
def _gen_input_ids(self, prompt: str) -> str:
messages = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": prompt},
]
input_ids = self.tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(self.device)
return input_ids
def _get_prompt(
self,
task: str,
) -> str:
if task == "attribution":
return {
"score": ATTRIBUTION_PROMPT,
"mistakes": ATTRIBUTION_MISTAKES_PROMPT,
}
elif task == 'summary_completeness':
return {
"score": SUMMARY_COMPLETENESS_PROMPT,
"mistakes": SUMMARY_MISTAKES_PROMPT,
}
elif task == "hallucination":
return {
"score": HALLUCINATION_PROMPT,
"mistakes": HALLUCINATION_MISTAKES_PROMPT,
}
elif task == "relevancy":
return {"score": RELEVANCY_PROMPT}
else:
raise ValueError(f"Unable to find the correct task: {task}")
def _parse_score_response(self, response: str) -> float:
float_regex = "((0\.\d+?|1\.0+?|0|1|\.\d+))"
match = re.search(f"response: {float_regex}", response.lower())
if match:
score = match.group(1)
print("score ", score)
return float(score.strip()) if score != "" else -1.0
else:
print("Unable to parse eval score using regex")
return -1.0
def _parse_mistakes_response(self, response: str) -> list[str]:
response = response.split("\n")
response = [r.strip() for r in response]
return [r for r in response if r != '']
def preprocess(
self,
inputs,
tasks: list[str],
rag_context: str,
query: str | None,
llm_response: str,
):
# generate our prompts
prompts = self._get_prompt(
task=tasks[0],
)
# prep score evaluation
score_prompt = prompts.get("score")
score_prompt = score_prompt.format(rag_context = rag_context, query = query, llm_response = llm_response)
score_input_ids =self._gen_input_ids(score_prompt)
# prep mistake identification
mistakes_prompt = prompts.get("mistakes", None)
# we do not evaluate for all tasks
if mistakes_prompt:
mistakes_prompt = mistakes_prompt.format(rag_context = rag_context, llm_response = llm_response)
mistakes_input_ids =self._gen_input_ids(mistakes_prompt)
else:
mistakes_input_ids = None
return {
"score_input_ids": score_input_ids,
"mistakes_input_ids": mistakes_input_ids,
}
def _forward(self, model_inputs):
score_input_ids = model_inputs['score_input_ids']
mistake_input_ids = model_inputs.get('mistakes_input_ids', None)
terminators = [
self.tokenizer.eos_token_id,
self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
with torch.cuda.amp.autocast() if self.device == "cuda" else nullcontext():
start_score_time = time.time()
# run eval score
score_outputs = self.model.generate(
input_ids=score_input_ids,
max_new_tokens=self.max_tokens,
eos_token_id=terminators,
do_sample=True,
temperature=self.temperature,
top_p=self.top_p,
)
score_response = score_outputs[0][score_input_ids.shape[-1]:]
print(f"Score generation time: {time.time()-start_score_time}")
# run mistakes eval score
start_mistakes_time = time.time()
mistakes_response = None
if mistake_input_ids is not None:
mistakes_outputs = self.model.generate(
input_ids=mistake_input_ids,
max_new_tokens=self.max_tokens,
eos_token_id=terminators,
do_sample=True,
temperature=self.temperature,
top_p=self.top_p,
)
mistakes_response = mistakes_outputs[0][score_input_ids.shape[-1]:]
print(f"Mistakes generation time: {time.time()-start_mistakes_time}")
return {
"score_response": score_response,
"mistakes_response": mistakes_response
}
def postprocess(self, response):
score_response = response.get('score_response')
mistakes_response = response.get('mistakes_response', None)
# decode and parse score
score_decoded = self.tokenizer.decode(score_response, skip_special_tokens=True)
score_completion = self._parse_score_response(score_decoded)
# decode and parse mistakes
mistakes_completion = None
if mistakes_response is not None:
mistakes_decoded = self.tokenizer.decode(mistakes_response, skip_special_tokens = True)
mistakes_completion = self._parse_mistakes_response(mistakes_decoded)
return {
'score_completion': score_completion,
'mistakes_completion': mistakes_completion
}
if __name__ == "__main__":
from transformers.pipelines import PIPELINE_REGISTRY
PIPELINE_REGISTRY.register_pipeline("de_val", pipeline_class=DeValPipeline)
model_dir = "../model"
tasks = ['relevancy']
# rag_context = "The earth is round. The sky is Blue."
# llm_response = "The earth is flat."
# query = "What color is the sky"
rag_context = "water is liquid. Tree leaves are green in summer."
llm_response = "water is solid."
query = "what color are tree leaves in summer"
pipe = DeValPipeline("de_val", model_dir = model_dir)
print(pipe("", tasks=tasks, rag_context=rag_context, query=query, llm_response=llm_response))
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