| import hashlib |
| import inspect |
| import json |
| import numpy as np |
| from copy import copy |
| from typing import Any, Dict, List, Optional |
|
|
| from swift.infer_engine import ChatCompletionResponse, InferEngine, InferRequest, RequestConfig |
| from swift.utils import get_logger |
|
|
| logger = get_logger() |
|
|
|
|
| def get_messages_md5(row: Dict[str, Any]): |
| row = copy(row) |
| row.pop('choices', None) |
| serialized = json.dumps(row, sort_keys=True) |
| return hashlib.md5(serialized.encode('utf-8')).hexdigest() |
|
|
|
|
| def get_reward(model: Any, |
| infer_requests: List[InferRequest], |
| request_config: RequestConfig = None, |
| ground_truths: List[str] = None, |
| threshold: Optional[float] = None): |
| """Get reward from an RM model. |
| |
| Args: |
| model: The model instance or an RM evaluator |
| infer_requests: Infer requests sent to the model |
| request_config: Infer config |
| ground_truths: The ground truth list |
| threshold: An optional threshold to generate the mask |
| |
| Returns: |
| Tuple |
| Index 0: The min-max normalized scores matched the infer_requests |
| Index 1: The mask filtered by the threshold |
| """ |
| infer_func = model.infer if isinstance(model, InferEngine) else model.__call__ |
| parameters = inspect.signature(infer_func).parameters |
| gt_param = {} |
| if 'ground_truths' in parameters: |
| gt_param = {'ground_truths': ground_truths} |
| if isinstance(infer_requests[0], dict): |
| infer_requests = [InferRequest(messages=req['messages']) for req in infer_requests] |
| rewards = infer_func(infer_requests, request_config=request_config, **gt_param) |
| if isinstance(rewards[0], ChatCompletionResponse): |
| print('reward:', rewards[0].choices[0].message.content) |
| if isinstance(rewards[0].choices[0].message.content, str): |
| rewards = [float(r.choices[0].message.content.strip('[]')) for r in rewards] |
| elif isinstance(rewards[0].choices[0].message.content, list): |
| rewards = [float(min(r.choices[0].message.content)) for r in rewards] |
| else: |
| rewards = [float(r.choices[0].message.content) for r in rewards] |
| arr = [] |
| for reward in rewards: |
| if isinstance(reward, (list, tuple)): |
| arr.append(min(reward)) |
| else: |
| arr.append(float(reward)) |
|
|
| _mask = np.array([True] * len(arr)) |
| if threshold is not None: |
| |
| _mask = np.array([a > threshold for a in arr]) |
|
|
| def normalize(arr): |
| min_val = np.min(arr) |
| max_val = np.max(arr) |
| if min_val == max_val: |
| if min_val == 0: |
| constant_value = 0.0 |
| else: |
| constant_value = min(1.0, min_val) |
| return np.full_like(arr, fill_value=constant_value, dtype=np.float64) |
| normalized = (arr - min_val) / (max_val - min_val + 1e-5) |
| return normalized |
|
|
| return normalize(arr), _mask |
|
|
