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obfuscated/pipeline.py

@@ -0,0 +1,3 @@
+# Pyarmor 8.5.11 (trial), 000000, non-profits, 2024-10-10T13:10:42.002286
+from pyarmor_runtime_000000 import __pyarmor__
+__pyarmor__(__name__, __file__, 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obfuscated/prompts.py

@@ -0,0 +1,3 @@
+# Pyarmor 8.5.11 (trial), 000000, non-profits, 2024-10-10T13:10:41.992722
+from pyarmor_runtime_000000 import __pyarmor__
+__pyarmor__(__name__, __file__, b'PY000000\x00\x03\x0b\x00\xa7\r\r\n\x80\x00\x01\x00\x08\x00\x00\x00\x04\x00\x00\x00@\x00\x00\x00a\x1e\x00\x00\x12\t\x04\x00+g]_?\x9e\xca\xe4\xd6@\xc2l(\x16\xce\x8c\x00\x00\x00\x00\x00\x00\x00\x00\x8f\x83\xb8\x1a\xda\xfb\x19d-N\x11+f\xe5\xb2\xd2 !\x00%\xf8\xa5\xa5^\xedy\xce\x18\xe4\x9bk\x13c:\xc9j\xdf\x19 %A\xbe\x1b\xe0\x84[P\x1c%J}a\x96\xfa\xa7O\xb5\x14\xf7\x11\x84\xe7\xf7\xdf\xfbf\xd6\xd8\xdb\xef\xf3U\xfa\x7f\x1fj\x94\x83\xa3\xcc\x10h\x95L\x00\x14\xf6rV\xe8\xd6\xd4S\xf7{eC\xbbo\xbd\xbc\xefK\x81\xe5\xda\xa6/I\xfd8\x90`\xb2C\xa4\x93\xda\xad\xb8\n[\xfe0\x02}\xfaWn1\x14\xe34"g\xed\xe6\xbf\xe2>\xe6ez\xa5\xa2|)r\xb0\x7f\xd8\x11\x1a\xaf\xcf\x83\xb5\xd2$\xcd\x08\xd3 \x8c-\xbctB\x1d\xc1x\xeb\xeb\x9a-\xbf\x7f!9\xa6\x98E\xfbz\xeeC\x944\xfa\xc8a%5\xf7}\rs\xd5\x12\xf9\x0b\xcc\xc3C\xa9\x03v\\\xc7\x1f 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obfuscated/pyarmor_runtime_000000/__init__.py

@@ -0,0 +1,2 @@
+# Pyarmor 8.5.11 (trial), 000000, 2024-10-10T13:10:41.990758
+from .pyarmor_runtime import __pyarmor__

pipeline.py

@@ -0,0 +1,214 @@
+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))

prompts.py

@@ -0,0 +1,174 @@
+RELEVANCY_PROMPT = """\
+Your goal is to determine if the provided LLM response is relevant to the user's query.\
+    You should disregard whether the response is factually accurate or not and only be concerned with relevance. 
+
+In this task, I will provide you with the following:
+- User Query: the question asked by the user
+- LLM Response: a response to the user derived from an input context
+
+
+You should return a score of 0 if the response is not relevant and a score of 1 if the response is relevant.
+
+# User Query
+{query}
+
+# LLM response
+{llm_response}
+ 
+
+Return your response if the format of "Response: score" where the score is your estimation on relevancy. Return no other text  
+"""
+
+
+HALLUCINATION_PROMPT = """\
+Your goal is to determine if the provided LLM response is hallucinating given the provided RAG context.  \
+
+In this task, I will provide you with the following:
+- RAG Context: the provided context which will act as your source of truth
+- LLM Response: a series of claims derived from the RAG context
+
+You should return a score between 0 and 1 based on how accurate you perceive the claims to be. \
+- If the response does not hallucinate at all then return a value of 1
+- If the entire response is hallucinated then return a value of 0
+- if half of the response is hallucinated then return a score of 0.5
+
+# RAG Context
+{rag_context}
+
+# LLM Response
+{llm_response}
+
+
+Return your response if the format of "Response: score" where the score is your estimation on relevancy. Return no other text
+"""
+
+HALLUCINATION_MISTAKES_PROMPT = """\
+Your goal is to extract the hallucinations from the provided respnse given the underlying RAG context if there are any.  \
+Essentially, you must compare the response to the RAG context, determine if any of the claims in the response are false, and \
+return back any false claims you identify.  
+
+In this task, I will provide you with the following:
+- RAG Context: the provided context which will act as your source of truth
+- LLM Response: a series of claims derived from the RAG context
+
+Requirements:
+- You must return false claims verbatim as they appear in the LLM response
+- You must return each false claim separated by a newline character 
+- Do not return any other text unless you consider it to be false based on the provided RAG context
+
+# RAG Context
+{rag_context}
+
+# LLM Response
+{llm_response}
+
+Do not return any other text beside the false claims separated by a newline character. 
+"""
+
+
+
+ATTRIBUTION_PROMPT = """\
+Your goal is to determine if the provided LLM response is mis-attributing action items to the wrong person given the provided RAG context.  \
+
+In this task, I will provide you with the following:
+- RAG Context: the provided context which will act as your source of truth
+- LLM Response: a series of summarized action items attributed to a participant derived from the RAG context
+
+For example, if the RAG context says that Person A must achieve Task 1, but the LLM response incorrectly says that Person B must achieve Task 1 \
+then this would be a misattribution
+
+You should return a score between 0 and 1 based on how accurate you perceive the attributions to be. \
+- If the response attributes action items with complete accuracy  then return a value of 1
+- If the entire response is misattributed then return a value of 0
+- if half of the response is misattributed then return a score of 0.5
+
+# RAG Context
+{rag_context}
+
+# LLM Response
+{llm_response}
+
+
+Return your response if the format of "Response: score" where the score is your estimation on relevancy. Return no other text
+"""
+
+ATTRIBUTION_MISTAKES_PROMPT = """\
+Your goal is to extract the misattributed action items from the provided respnse given the underlying RAG context if there are any.  \
+Essentially, you must compare the LLM response to the RAG context, determine if any of the action items in the response are misattributed, and \
+return back any misattributed action items you identify.  
+
+For example, if the RAG context says that Person A must achieve Task 1, but the LLM response incorrectly says that Person B must achieve Task 1 \
+then this would be a misattribution.  You must return this action item if it was misattributed.
+
+
+In this task, I will provide you with the following:
+- RAG Context: the provided context which will act as your source of truth
+- LLM Response: a series of summarized action items attributed to a participant derived from the RAG context
+
+Requirements:
+- You must return misattributed action items verbatim as they appear in the LLM response
+- You must return each misattributed action items separated by a newline character 
+- Do not return any other text unless you consider it to be misattributed based on the provided RAG context
+
+# RAG Context
+{rag_context}
+
+# LLM Response
+{llm_response}
+
+Do not return any other text beside the misattributed action items separated by a newline character. 
+"""
+
+
+SUMMARY_COMPLETENESS_PROMPT = """\
+Your goal is to determine if the provided LLM response is a complete summary given the provided RAG context.  \
+
+In this task, I will provide you with the following:
+- RAG Context: the provided context which will act as your source of truth
+- LLM Response: a series of claims derived from the RAG context
+
+
+For example, if the RAG context contains important information that should be summarized then this would be considered an incomplete summary.
+
+You should return a score between 0 and 1 based on how accurate you perceive the claims to be. \
+- If the response is a perfect summary of the RAG context then return a value of 1
+- If the response is missing all important information from RAG context then return a value of 0
+- if the response is missing half of the important information from the RAG context then return a score of 0.5
+
+# RAG Context
+{rag_context}
+
+# LLM Response
+{llm_response}
+
+
+Return your response if the format of "Response: score" where the score is your estimation on relevancy. Return no other text
+"""
+
+
+SUMMARY_MISTAKES_PROMPT = """\
+Your goal is to identify any important topics in the provided RAG context that is not included in the LLM response.  \
+Essentially, there is a possibility that the provided summary provided in the LLM response is missing key information, \
+and it is your job to identify this missing information and then summarize it.  You must return a summarized version of any \
+missing key information that you identified.
+
+For example, if the RAG context contains important information that should be summarized then this would be considered an incomplete summary.
+
+In this task, I will provide you with the following:
+- RAG Context: the provided context which will act as your source of truth
+- LLM Response: a series of claims derived from the RAG context
+
+Requirements:
+- You must return a summary of any key topics or important information from the RAG context that is not already present in LLM response
+- Do not summarize any information that is already included in the LLM response. This would be considered a failure. 
+- You must return each summary separated by a newline character 
+- Do not return any other text unless you consider it to be a summary of missing information from RAG context.
+
+# RAG Context
+{rag_context}
+
+# LLM Response
+{llm_response}
+
+Do not return any other text beside the summaries separated by a newline character. 
+"""