chore: import upstream snapshot with attribution

2026-07-13 13:24:13 +08:00
commit 1037506f2e
6050 changed files with 1731598 additions and 0 deletions
@@ -0,0 +1,110 @@
+import json
+import argparse
+import sys
+import collections
+import glob
+import os
+
+sys.set_int_max_str_digits(0)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--completion_file", type=str, required=True)
+    parser.add_argument("--reward_file", type=str, required=True)
+    parser.add_argument("--lower_bound", type=float, default=0.0)
+    parser.add_argument("--margin", type=float, default=1.0)
+    args = parser.parse_args()
+
+    solutions = json.load(open(args.completion_file))
+    if "difficulty" in solutions[0]:
+        all_diff = collections.Counter([item["difficulty"] for item in solutions])
+        all_diff_success = {k: 0 for k in all_diff}
+    else:
+        all_diff = None
+        all_diff_success = None
+
+    if os.path.exists(args.reward_file):
+        rewards = json.load(open(args.reward_file))
+    else:
+        rewards = []
+        for file in glob.glob(args.reward_file):
+            rewards += json.load(open(file))
+
+    id2reward = collections.defaultdict(dict)
+    for item in rewards:
+        index = item["index"]
+        orig_id, pred_id = list(map(int, index.split("_")))
+        if isinstance(item["reward"], list):
+            id2reward[orig_id][pred_id] = item["reward"][0]
+        else:
+            id2reward[orig_id][pred_id] = item["reward"]
+
+    outputs = []
+    success = 0
+    num_pairs = 0
+    for item in solutions:
+        problem_id = item["id"]
+
+        if not item["response"]:
+            continue
+        if not item["pred"]:
+            continue
+
+        assert isinstance(item["response"], list)
+        assert isinstance(item["pred"], list)
+        assert len(item["response"]) == len(item["pred"])
+
+        tuples = []
+        for j, (resp, pred) in enumerate(zip(item["response"], item["pred"])):
+            if pred:
+                tuples.append((j, resp, pred, id2reward[problem_id][j]))
+
+        if not tuples:
+            continue
+
+        tuples = sorted(tuples, key=lambda x: x[3], reverse=True)
+        # Construct preference pairs: collect pairs with margin greater than args.margin
+        pos = []
+        neg = []
+        for i in range(len(tuples)):
+            if tuples[i][3] < args.lower_bound:
+                break
+            for j in range(i + 1, len(tuples)):
+                if tuples[i][3] - tuples[j][3] < args.margin:
+                    continue
+                pos.append(tuples[i][1])
+                neg.append(tuples[j][1])
+        outputs.append({
+            "id": problem_id,
+            "pos": pos,
+            "neg": neg
+        })
+        num_pairs += len(pos)
+
+        _pred_id = tuples[0][0]
+        if item["res"] and item["res"][_pred_id]:
+            success += 1
+            if all_diff is not None:
+                all_diff_success[item["difficulty"]] += 1
+
+    print(f"Success rate: {success / len(solutions)}")
+    if all_diff is not None:
+        for k, v in all_diff.items():
+            print(f"Difficulty {k}: {all_diff_success[k] / v}")
+
+    json.dump(outputs, open(args.completion_file.replace(".json", f".lower-{args.lower_bound}.mar-{args.margin}.json"), "w"), ensure_ascii=False, indent=2)
+    print(f"Number of pairs: {num_pairs}")
+
+
+if __name__ == '__main__':
+    main()
+
+"""
+>>> python scripts/apps/construct_prefer_pair_rm.py --completion_file ../msranlpintern/reward_modeling/experiments/deepseek-coder-v1.5-ins.7b.apps.r2c.gpt4o.distil.A100.w8.v3.0.s42/apps/checkpoint-400/train.0shot.tem1.0.n10.v1.1.json --reward_file ../msranlpintern/reward_modeling/experiments/deepseek-coder-v1.5-ins.7b.apps.pair-rm.gpt4o-worsen.A100.w8.v1.2.s42/r2c.sft.step-400.train.n10/test-checkpoint-50/eval_predictions_rank0.json --lower_bound 0.0 --margin 1.0
+
+Success rate: 0.3566
+Number of pairs: 29584
+"""
+
+