chore: import upstream snapshot with attribution

2026-07-13 13:34:58 +08:00
commit a203934033
1368 changed files with 175001 additions and 0 deletions
@@ -0,0 +1,80 @@
+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:
+        # > not >=, orm caller passes 0, which will cause error
+        _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