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 +1,78 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import json
+
+import torch
+from examples.simultaneous_translation.utils.latency import LatencyInference
+
+
+LATENCY_METRICS = [
+    "differentiable_average_lagging",
+    "average_lagging",
+    "average_proportion",
+]
+
+
+class LatencyScorer:
+    def __init__(self, start_from_zero=True):
+        self.recorder = []
+        self.scores = {}
+        self.scorer = LatencyInference()
+        self.start_from_zero = start_from_zero
+
+    def update_reorder(self, list_of_dict):
+        self.recorder = []
+        for info in list_of_dict:
+            delays = [int(x) - int(not self.start_from_zero) for x in info["delays"]]
+            delays = torch.LongTensor(delays).unsqueeze(0)
+            src_len = torch.LongTensor([info["src_len"]]).unsqueeze(0)
+
+            self.recorder.append(self.scorer(delays, src_len))
+
+    def cal_latency(self):
+        self.scores = {}
+        for metric in LATENCY_METRICS:
+            self.scores[metric] = sum(
+                [x[metric][0, 0].item() for x in self.recorder]
+            ) / len(self.recorder)
+        return self.scores
+
+    @classmethod
+    def score(cls, list_of_dict, start_from_zero=True):
+        scorer_to_return = cls(start_from_zero)
+        scorer_to_return.update_reorder(list_of_dict)
+        scorer_to_return.cal_latency()
+        return scorer_to_return.scores
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--start-from-zero", action="store_true")
+    args = parser.parse_args()
+
+    scorer = LatencyInference()
+    recorder = []
+    with open(args.input, "r") as f:
+        for line in f:
+            info = json.loads(line)
+
+            delays = [int(x) - int(not args.start_from_zero) for x in info["delays"]]
+
+            delays = torch.LongTensor(delays).unsqueeze(0)
+
+            src_len = torch.LongTensor([info["src_len"]]).unsqueeze(0)
+
+            recorder.append(scorer(delays, src_len))
+
+    average_results = {}
+
+    for metric in LATENCY_METRICS:
+        average_results[metric] = sum([x[metric][0, 0].item() for x in recorder]) / len(
+            recorder
+        )
+        print(f"{metric}: {average_results[metric]}")