init src 0.9.2

vllm/entrypoints/score_utils.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Union
+
+from torch.nn import CosineSimilarity
+
+from vllm.outputs import PoolingRequestOutput
+from vllm.transformers_utils.tokenizer import (PreTrainedTokenizer,
+                                               PreTrainedTokenizerFast)
+
+
+def _cosine_similarity(
+    tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
+    embed_1: list[PoolingRequestOutput],
+    embed_2: list[PoolingRequestOutput],
+) -> list[PoolingRequestOutput]:
+
+    scorer = CosineSimilarity(0)
+    scores: Union[list[PoolingRequestOutput]] = []
+
+    for emb_1, emb_2 in zip(embed_1, embed_2):
+        pair_score = scorer(emb_1.outputs.data, emb_2.outputs.data)
+
+        padding = []
+        if (pad_token_id := getattr(tokenizer, "pad_token_id",
+                                    None)) is not None:
+            padding = [pad_token_id]
+
+        tokens = emb_1.prompt_token_ids + padding + emb_2.prompt_token_ids
+
+        scores.append(
+            PoolingRequestOutput(
+                request_id=f"{emb_1.request_id}_{emb_2.request_id}",
+                outputs=pair_score,
+                prompt_token_ids=tokens,
+                finished=True))
+
+    return scores
+
+
+def _validate_score_input_lens(
+    texts_1: Union[list[str], list[dict]],
+    texts_2: Union[list[str], list[dict]],
+):
+    if len(texts_1) > 1 and len(texts_1) != len(texts_2):
+        raise ValueError("Input lengths must be either 1:1, 1:N or N:N")
+    if len(texts_1) == 0:
+        raise ValueError("At least one text element must be given")
+    if len(texts_2) == 0:
+        raise ValueError("At least one text_pair element must be given")