mirror of
https://github.com/katanemo/plano.git
synced 2026-06-17 15:25:17 +02:00
fix test
This commit is contained in:
cotran
parent
d776ed0117
commit
665dbc2d4e
3 changed files with 203 additions and 61 deletions
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@ -1,8 +1,15 @@
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import torch
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import numpy as np
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from typing import List, Dict
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import math
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import app.commons.constants as const
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import random
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from typing import List, Dict, Any, Tuple
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import json
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def filter_tokens_and_probs(tokens: List[str], probs: List[float]) -> Tuple[List[], List[float]]:
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def filter_tokens_and_probs(
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tokens: List[str], probs: List[float]
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) -> Tuple[List[str], List[float]]:
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"""
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Filters out special tokens from the list of tokens and their corresponding probabilities.
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@ -14,17 +21,17 @@ def filter_tokens_and_probs(tokens: List[str], probs: List[float]) -> Tuple[List
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tuple: A tuple containing two lists - filtered tokens and their corresponding probabilities.
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"""
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# Use regex to identify tokens without special characters
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special_tokens = ['\\n', '{"', '":', ' "', '",', ' {"', '"}}\\n', ' ', '"}}\n']
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filtered_tokens = [
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token for token in tokens
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if token not in special_tokens
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]
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special_tokens = ["\\n", '{"', '":', ' "', '",', ' {"', '"}}\\n', " ", '"}}\n']
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filtered_tokens = [token for token in tokens if token not in special_tokens]
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filtered_probs = [
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prob for token, prob in zip(tokens, probs)
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if token not in special_tokens
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prob for token, prob in zip(tokens, probs) if token not in special_tokens
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]
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return filtered_tokens, filtered_probs
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def get_all_parameter_values(tokens: List[str], probs: List[float], parameter_names: Dict[str, List[str]]) -> Tuple[Dict[str, List[str]], Dict[str, List[float]]]:
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def get_all_parameter_values(
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tokens: List[str], probs: List[float], parameter_names: Dict[str, Any]
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) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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"""
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Extracts parameter values and their corresponding probabilities from the tokens.
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@ -49,7 +56,9 @@ def get_all_parameter_values(tokens: List[str], probs: List[float], parameter_na
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# Incrementally combine tokens to find a full match with any parameter name
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while i < len(tokens):
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if combined_token:
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combined_token += tokens[i] # Append next token to the current combination
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combined_token += tokens[
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i
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] # Append next token to the current combination
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else:
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combined_token = tokens[i] # Start a new combination
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@ -62,7 +71,11 @@ def get_all_parameter_values(tokens: List[str], probs: List[float], parameter_na
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i += 1 # Move past the parameter name
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# Collect tokens as values until the next parameter or end marker
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while i < len(tokens) and tokens[i] not in params and tokens[i] != '</tool_call>':
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while (
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i < len(tokens)
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and tokens[i] not in params
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and tokens[i] != "</tool_call>"
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):
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values.append(tokens[i])
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prob_values.append(probs[i])
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i += 1
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@ -83,7 +96,11 @@ def get_all_parameter_values(tokens: List[str], probs: List[float], parameter_na
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i = start + 1
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return parameter_values, probs_values
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def calculate_stats(data: Dict, function_description: Dict) -> Dict:
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def calculate_stats(
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data: Dict[str, Any], function_description: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Calculates statistical metrics for the given data.
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@ -97,19 +114,33 @@ def calculate_stats(data: Dict, function_description: Dict) -> Dict:
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stats = {}
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try:
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for key, values in data.items():
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if len(data[key])>=1:
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if len(data[key]) >= 1:
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first = values[0]
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max_value = max(values)
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min_value = min(values)
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avg_value = sum(values) / len(values)
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has_format = check_parameter_property(function_description, key, "format")
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has_default = check_parameter_property(function_description, key , "default")
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stats[key] = {'first':first, 'max': max_value, 'min': min_value, 'avg': avg_value, 'has_format': has_format, 'has_default': has_default}
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has_format = check_parameter_property(
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function_description, key, "format"
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)
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has_default = check_parameter_property(
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function_description, key, "default"
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)
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stats[key] = {
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"first": first,
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"max": max_value,
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"min": min_value,
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"avg": avg_value,
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"has_format": has_format,
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"has_default": has_default,
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}
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except Exception as e:
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print(data)
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return stats
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def check_parameter_property(api_description: Dict, parameter_name: str, property_name: str)-> bool:
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def check_parameter_property(
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api_description: Dict[str, Any], parameter_name: str, property_name: str
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) -> bool:
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"""
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Check if a parameter in an API description has a specific property.
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@ -127,8 +158,36 @@ def check_parameter_property(api_description: Dict, parameter_name: str, propert
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return property_name in parameter_info
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def calculate_entropy(log_probs):
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"""
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Calculate the entropy and variance of entropy (varentropy) from log probabilities.
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def hallucination_detect(token:str, log_probs:List[float], current_state: Dict, entropy_thd : float= 0.7, varentropy_thd :float = 4.0) -> bool:
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Args:
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log_probs (list of float): A list of log probabilities.
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Returns:
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tuple: A tuple containing:
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- log_probs (list of float): The input log probabilities as a list.
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- entropy (float): The calculated entropy.
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- varentropy (float): The calculated variance of entropy.
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"""
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log_probs = torch.tensor(log_probs)
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token_probs = torch.exp(log_probs)
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entropy = -torch.sum(log_probs * token_probs, dim=-1) / math.log(2, math.e)
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varentropy = torch.sum(
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token_probs * (log_probs / math.log(2, math.e)) + entropy.unsqueeze(-1) ** 2,
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dim=-1,
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)
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return log_probs.tolist(), entropy.item(), varentropy.item()
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def hallucination_detect(
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token: str,
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log_probs: List[float],
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current_state: Dict[str, Any],
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entropy_thd: float = 0.7,
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varentropy_thd: float = 4.0,
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) -> bool:
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"""
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Detects hallucinations in the token sequence based on entropy and varentropy thresholds.
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@ -142,12 +201,12 @@ def hallucination_detect(token:str, log_probs:List[float], current_state: Dict,
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Returns:
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bool: True if a hallucination is detected, False otherwise.
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"""
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if token:
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# check if there is content in token
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current_state["tokens"].append(token)
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current_state['content'] += token
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current_state['logprobs'].append(log_probs)
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current_state["content"] += token
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current_state["logprobs"].append(log_probs)
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# keep track of entropy and varentropy
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_, entropy, varentropy = calculate_entropy(log_probs)
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current_state["entropy"].append(entropy)
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@ -156,71 +215,148 @@ def hallucination_detect(token:str, log_probs:List[float], current_state: Dict,
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if token == "<tool_call>":
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if entropy > entropy_thd or varentropy > varentropy_thd:
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current_state["hallucination"] = True
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current_state["hallucination_message"] = f"{token} with entropy {entropy}, varentropy {varentropy} doesn't pass the threshold {entropy_thd} | {varentropy_thd}"
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current_state[
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"hallucination_message"
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] = f"{token} with entropy {entropy}, varentropy {varentropy} doesn't pass the threshold {entropy_thd} | {varentropy_thd}"
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return True
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elif token == "</tool_call>":
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current_state["state"] = "tool_call_end"
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# try to extract tool call, else raise error
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try:
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current_state['tool_call'] = extract_tool_calls(current_state["content"])[0]
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current_state['tool_call_process'] = True
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current_state[
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"tool_call"
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] = const.arch_function_hanlder.extract_tool_calls(
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current_state["content"]
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)[
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0
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]
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current_state["tool_call_process"] = True
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except:
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current_state['tool_call_process'] = False
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current_state["tool_call_process"] = False
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print(f"cant process tool")
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return True
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# check if function name is valid
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if current_state['tool_call']['function']['name'] not in current_state['parameter_names'].keys():
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if (
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current_state["tool_call"]["function"]["name"]
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not in current_state["parameter_names"].keys()
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):
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current_state["hallucination"] = True
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current_state["hallucination_message"] = f"function name {current_state['tool_call']['name']} not found"
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current_state[
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"hallucination_message"
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] = f"function name {current_state['tool_call']['name']} not found"
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return True
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# check if parameter names are from the given function tools
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current_parameter_names = current_state['tool_call']['function']['arguments'].keys()
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given_parameter_names = current_state['parameter_names'][current_state['tool_call']['function']['name']]
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current_parameter_names = current_state["tool_call"]["function"][
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"arguments"
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].keys()
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given_parameter_names = current_state["parameter_names"][
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current_state["tool_call"]["function"]["name"]
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]
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if not set(current_parameter_names).issubset(given_parameter_names):
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missing_keys = set(current_parameter_names) - set(given_parameter_names)
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current_state["hallucination"] = True
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current_state["hallucination_message"] = f"parameter names {missing_keys} not found"
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current_state[
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"hallucination_message"
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] = f"parameter names {missing_keys} not found"
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return True
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# filtered special tokens that are not needed in the hallucination check for parameter values
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current_state["filtered_tokens"], current_state["filtered_entropy"] = filter_tokens_and_probs(current_state["tokens"], current_state["entropy"])
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current_state["filtered_tokens"], current_state["filtered_varentropy"] = filter_tokens_and_probs(current_state["tokens"], current_state["varentropy"])
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parameter_values, entropy_values = get_all_parameter_values(current_state["filtered_tokens"], current_state["filtered_entropy"], current_state['parameter_names'])
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parameter_values, varentropy_values = get_all_parameter_values(current_state["filtered_tokens"], current_state["filtered_varentropy"], current_state['parameter_names'])
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(
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current_state["filtered_tokens"],
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current_state["filtered_entropy"],
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) = filter_tokens_and_probs(
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current_state["tokens"], current_state["entropy"]
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)
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(
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current_state["filtered_tokens"],
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current_state["filtered_varentropy"],
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) = filter_tokens_and_probs(
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current_state["tokens"], current_state["varentropy"]
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)
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parameter_values, entropy_values = get_all_parameter_values(
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current_state["filtered_tokens"],
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current_state["filtered_entropy"],
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current_state["parameter_names"],
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)
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parameter_values, varentropy_values = get_all_parameter_values(
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current_state["filtered_tokens"],
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current_state["filtered_varentropy"],
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current_state["parameter_names"],
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)
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current_state['parameter_values'] = parameter_values
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current_state['parameter_values_entropy'] = entropy_values
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current_state['parameter_values_varentropy'] = varentropy_values
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current_state["parameter_values"] = parameter_values
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current_state["parameter_values_entropy"] = entropy_values
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current_state["parameter_values_varentropy"] = varentropy_values
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# calculate the max, first, avg of sub tokens for parameter value
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current_state['parameter_value_entropy_stat'] = calculate_stats(current_state['parameter_values_entropy'], current_state['function_description'][0])
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current_state['parameter_value_varentropy_stat'] = calculate_stats(current_state['parameter_values_varentropy'], current_state['function_description'][0])
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current_state["parameter_value_entropy_stat"] = calculate_stats(
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current_state["parameter_values_entropy"],
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current_state["function_description"][0],
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)
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current_state["parameter_value_varentropy_stat"] = calculate_stats(
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current_state["parameter_values_varentropy"],
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current_state["function_description"][0],
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)
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# get map for debugging
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current_state['token_entropy_map'] = {x : y for x,y in zip(current_state['tokens'], current_state['entropy'])}
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current_state['token_varentropy_map'] = {x : y for x,y in zip(current_state['tokens'], current_state['varentropy'])}
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current_state["token_entropy_map"] = {
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x: y for x, y in zip(current_state["tokens"], current_state["entropy"])
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}
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current_state["token_varentropy_map"] = {
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x: y
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for x, y in zip(current_state["tokens"], current_state["varentropy"])
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}
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# checking hallucination for parameter value
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current_state['parameter_value_check'] = {x : {'hallucination': False, 'message': ''} for x in current_state['parameter_values'].keys()}
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for key in current_state['parameter_value_check'].keys():
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current_state["parameter_value_check"] = {
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x: {"hallucination": False, "message": ""}
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for x in current_state["parameter_values"].keys()
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}
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for key in current_state["parameter_value_check"].keys():
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# if parameter is given a format, check the first token
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if current_state['parameter_value_entropy_stat'][key]['has_format']:
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if current_state['parameter_value_entropy_stat'][key]['first'] > entropy_thd or current_state['parameter_value_varentropy_stat'][key]['first'] > varentropy_thd:
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current_state['parameter_value_check'][key]['hallucination'] = True
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if current_state["parameter_value_entropy_stat"][key]["has_format"]:
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if (
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current_state["parameter_value_entropy_stat"][key]["first"]
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> entropy_thd
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or current_state["parameter_value_varentropy_stat"][key][
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"first"
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]
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> varentropy_thd
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):
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current_state["parameter_value_check"][key][
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"hallucination"
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] = True
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current_state["hallucination"] = True
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current_state['parameter_value_check'][key]['message'] = f"parameter {key} with formatting doesn't pass threshold"
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current_state["parameter_value_check"][key][
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"message"
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] = f"parameter {key} with formatting doesn't pass threshold"
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# if parameter gis given a default value, we can always use default
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elif current_state['parameter_value_entropy_stat'][key]['has_default']:
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current_state['parameter_value_check'][key]['hallucination'] = False
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current_state['parameter_value_check'][key]['message'] = f"parameter {key} with default"
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elif current_state["parameter_value_entropy_stat"][key]["has_default"]:
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current_state["parameter_value_check"][key]["hallucination"] = False
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current_state["parameter_value_check"][key][
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"message"
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] = f"parameter {key} with default"
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# check if max sub token is > thresholds
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else:
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if current_state['parameter_value_entropy_stat'][key]['max'] > entropy_thd or current_state['parameter_value_varentropy_stat'][key]['max'] > varentropy_thd:
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current_state['parameter_value_check'][key]['hallucination'] = True
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current_state['parameter_value_check'][key]['message'] = f"parameter {key} with {current_state['parameter_value_entropy_stat'][key]['max']} and {current_state['parameter_value_varentropy_stat'][key]['max']} doesnt pass threshold"
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if (
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current_state["parameter_value_entropy_stat"][key]["max"]
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> entropy_thd
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or current_state["parameter_value_varentropy_stat"][key]["max"]
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> varentropy_thd
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):
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current_state["parameter_value_check"][key][
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"hallucination"
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] = True
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current_state["parameter_value_check"][key][
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"message"
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] = f"parameter {key} with {current_state['parameter_value_entropy_stat'][key]['max']} and {current_state['parameter_value_varentropy_stat'][key]['max']} doesnt pass threshold"
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current_state["hallucination"] = True
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if current_state["hallucination"] == True:
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current_state["hallucination_message"] = "\n".join([current_state['parameter_value_check'][key]['message'] for key in current_state['parameter_value_check'].keys()])
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current_state["hallucination_message"] = "\n".join(
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[
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current_state["parameter_value_check"][key]["message"]
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for key in current_state["parameter_value_check"].keys()
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]
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)
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return True
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return False
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return False
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