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 +1,59 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+from typing import Dict, List, Optional, Tuple
+
+from swift.template import split_str_parts_by
+
+
+def calculate_loss_scale(query: str,
+                         response: str,
+                         response_loss_scale_map: Dict[str, list],
+                         query_loss_scale_map: Optional[Dict[str, list]] = None) -> Tuple[List[str], List[float]]:
+    """Calculate the loss scale by splitting the agent response.
+
+    This algorithm comes from paper: https://arxiv.org/pdf/2309.00986.pdf
+
+    Agent response format:
+
+    ```text
+        Thought: you should always think about what to do
+        Action: the action to take, should be one of the above tools[fire_recognition,
+            fire_alert, call_police, call_fireman]
+        Action Input: the input to the action
+        Observation: the result of the action
+        ... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
+        Thought: I now know the final answer
+        Final Answer: the final answer to the original input question
+    ```
+    Returns:
+        A tuple of agent response parts and their weights.
+    """
+    # query loss scale map
+    if query_loss_scale_map is not None:
+        for key in query_loss_scale_map.keys():
+            if key in query:
+                if isinstance(query_loss_scale_map[key], (float, int)):
+                    query_loss_scale_map[key] = [query_loss_scale_map[key]]
+                loss_scale_value = query_loss_scale_map[key][0]
+                return [response], [float(loss_scale_value)]
+    delimiters = [k for k, v in response_loss_scale_map.items() if len(v) == 2]
+    if delimiters:
+        agent_parts = split_str_parts_by(response, delimiters)
+    else:
+        regex_delimiters = [k for k, v in response_loss_scale_map.items() if len(v) == 1]
+        agent_parts = split_str_parts_by(response, regex_delimiters, regex_mode=True)
+    weights = []
+    agent_content = []
+    for c in agent_parts:
+        if c['key'] in response_loss_scale_map:
+            loss_scale = response_loss_scale_map[c['key']]
+            assert len(loss_scale) in {1, 2}, f'loss_scale: {loss_scale}'
+            if len(loss_scale) == 1:
+                weights += loss_scale
+                agent_content.append(c['content'])
+            else:
+                weights += loss_scale
+                agent_content += [c['key'], c['content']]
+        else:
+            weights.append(1.)
+            agent_content.append(c['content'])
+    return agent_content, weights