add mteb evaluation

pipelines/examples/contrastive_training/mteb_evaluation/README.md

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+# MTEB基准评估
+[MTEB](https://github.com/embeddings-benchmark/mteb)
+是一个大规模文本嵌入基准，包含了丰富的向量评估任务和数据集，涉及多语言、多领域的向量检索。
+本仓库主要面向其中的中英文的检索任务（Retrieval），并以SciFact数据集作为主要示例。
+
+## 环境准备
+通过Anaconda创建和配置环境：
+```
+conda create -n mteb_paddle python=3.8
+conda activate mteb_paddle
+```
+安装相关包：
+```
+conda install paddlepaddle-gpu==2.6.1 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
+conda install nccl -c conda-forge
+pip install paddlenlp==2.8.0
+pip install mteb==1.12.13
+```
+
+## 模型评估
+使用评估脚本`eval_mteb.py`：
+
+- `base_model_name_or_path`: 模型名称或路径
+- `output_folder`: 结果文件存储路径
+- `task_name`：任务（数据集）名称，如SciFact
+- `task_split`：测试查询集合，如test或dev
+- `query_instruction`：查询前添加的提示文本，如'query: '或None
+- `document_instruction`：文档前添加的提示文本，如'passage: '或None
+- `pooling_method`：获取表示的方式，last表示取最后token，mean表示取平均，cls表示取`[CLS]`token \
+- `max_seq_length`: 最大序列长度
+- `eval_batch_size`: 模型预测的批次大小（单个GPU）
+- `pad_token`：设置padding的token，可取unk_token、eos_token或pad_token
+- `padding_side`：设置padding的位置，可取left或right
+- `add_bos_token`：是否添加起始符，0表示不添加，1表示添加
+- `add_eos_token`：是否添加结束符，0表示不添加，1表示添加
+
+#### RepLLaMA向量检索模型
+RepLLaMA的基础模型是LLaMA-2-7B，它在MS MARCO段落数据上进行了LoRA训练，模型参数开源在 [repllama-v1-7b-lora-passage](https://huggingface.co/castorini/repllama-v1-7b-lora-passage) 。
+```
+CUDA_VISIBLE_DEVICES=0 python eval_mteb.py \
+       --base_model_name_or_path castorini/repllama-v1-7b-lora-passage \
+       --output_folder en_results/repllama-v1-7b-lora-passage \
+       --task_name SciFact \
+       --task_split test \
+       --query_instruction 'query: ' \
+       --document_instruction 'passage: ' \
+       --pooling_method last \
+       --max_seq_length 512 \
+       --eval_batch_size 2 \
+       --pad_token unk_token \
+       --padding_side right \
+       --add_bos_token 0 \
+       --add_eos_token 1
+```
+结果文件保存在`en_results/repllama-v1-7b-lora-passage/SciFact/last/no_revision_available/SciFact.json`，包含以下类似的评估结果：
+```
+'ndcg_at_1': 0.63,
+'ndcg_at_3': 0.71785,
+'ndcg_at_5': 0.73735,
+'ndcg_at_10': 0.75708,
+'ndcg_at_20': 0.7664,
+'ndcg_at_100': 0.77394,
+'ndcg_at_1000': 0.7794
+```
+
+#### BGE向量检索模型
+BGE的基础模型是类BERT模型，以 [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) 模型为例。
+```
+CUDA_VISIBLE_DEVICES=0 python eval_mteb.py \
+       --base_model_name_or_path BAAI/bge-large-en-v1.5 \
+       --output_folder en_results/bge-large-en-v1.5 \
+       --task_name SciFact \
+       --task_split test \
+       --document_instruction 'Represent this sentence for searching relevant passages: ' \
+       --pooling_method mean \
+       --max_seq_length 512 \
+       --eval_batch_size 32 \
+       --pad_token pad_token \
+       --padding_side right \
+       --add_bos_token 0 \
+       --add_eos_token 0
+```
+结果文件保存在`en_results/bge-large-en-v1.5/SciFact/mean/no_revision_available/SciFact.json`，包含以下类似的评估结果：
+```
+'ndcg_at_1': 0.64667,
+'ndcg_at_3': 0.70359,
+'ndcg_at_5': 0.7265,
+'ndcg_at_10': 0.75675,
+'ndcg_at_20': 0.76743,
+'ndcg_at_100': 0.77511,
+'ndcg_at_1000': 0.77939
+```
+
+## 参考文献
+[1] Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, Jimmy Lin: Fine-Tuning LLaMA for Multi-Stage Text Retrieval.
+
+[2] Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighof: C-Pack: Packaged Resources To Advance General Chinese Embedding.

pipelines/examples/contrastive_training/mteb_evaluation/eval_mteb.py

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+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import logging
+
+import mteb
+from models import EncodeModel
+from mteb import MTEB
+
+from paddlenlp.transformers import AutoModel, AutoTokenizer
+
+
+def get_model(peft_model_name, base_model_name):
+    if peft_model_name is not None:
+        raise NotImplementedError("PEFT model is not supported yet")
+    else:
+        base_model = AutoModel.from_pretrained(base_model_name)
+        return base_model
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base_model_name_or_path", default="bge-large-en-v1.5", type=str)
+    parser.add_argument("--peft_model_name_or_path", default=None, type=str)
+    parser.add_argument("--output_folder", default="tmp", type=str)
+
+    parser.add_argument("--task_name", default="SciFact", type=str)
+    parser.add_argument(
+        "--task_split",
+        default="test",
+        help='Note that some datasets do not have "test", they only have "dev"',
+        type=str,
+    )
+
+    parser.add_argument("--query_instruction", default=None, help="add prefix instruction before query", type=str)
+    parser.add_argument(
+        "--document_instruction", default=None, help="add prefix instruction before document", type=str
+    )
+
+    parser.add_argument("--pooling_method", default="last", help="choose in [mean, last, cls]", type=str)
+    parser.add_argument("--max_seq_length", default=512, type=int)
+    parser.add_argument("--eval_batch_size", default=1, type=int)
+
+    parser.add_argument("--pad_token", default="unk_token", help="unk_token, eos_token or pad_token", type=str)
+    parser.add_argument("--padding_side", default="left", help="right or left", type=str)
+    parser.add_argument("--add_bos_token", default=0, help="1 means add token", type=int)
+    parser.add_argument("--add_eos_token", default=1, help="1 means add token", type=int)
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    logger = logging.getLogger(__name__)
+    logging.basicConfig(level=logging.INFO)
+    logger.info("Args: {}".format(args))
+
+    model = get_model(args.peft_model_name_or_path, args.base_model_name_or_path)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.base_model_name_or_path)
+    assert hasattr(tokenizer, args.pad_token), f"Tokenizer does not have {args.pad_token} token"
+    token_dict = {"unk_token": tokenizer.unk_token, "eos_token": tokenizer.eos_token, "pad_token": tokenizer.pad_token}
+    tokenizer.pad_token = token_dict[args.pad_token]
+
+    assert args.padding_side in [
+        "right",
+        "left",
+    ], f"padding_side should be either 'right' or 'left', but got {args.padding_side}"
+    assert not (
+        args.padding_side == "left" and args.pooling_method == "cls"
+    ), "Padding 'left' is not supported for pooling method 'cls'"
+    tokenizer.padding_side = args.padding_side
+
+    assert args.add_bos_token in [0, 1], f"add_bos_token should be either 0 or 1, but got {args.add_bos_token}"
+    assert args.add_eos_token in [0, 1], f"add_eos_token should be either 0 or 1, but got {args.add_eos_token}"
+    tokenizer.add_bos_token = bool(args.add_bos_token)
+    tokenizer.add_eos_token = bool(args.add_eos_token)
+
+    encode_model = EncodeModel(
+        model=model,
+        tokenizer=tokenizer,
+        pooling_method=args.pooling_method,
+        query_instruction=args.query_instruction,
+        document_instruction=args.document_instruction,
+        eval_batch_size=args.eval_batch_size,
+        max_seq_length=args.max_seq_length,
+    )
+
+    logger.info("Ready to eval")
+    evaluation = MTEB(tasks=mteb.get_tasks(tasks=[args.task_name]))
+    # evaluation = MTEB(tasks=[args.task_name])  ## For older MTEB versions
+    evaluation.run(
+        encode_model,
+        output_folder=f"{args.output_folder}/{args.task_name}/{args.pooling_method}",
+        eval_splits=[args.task_split],
+    )

pipelines/examples/contrastive_training/mteb_evaluation/models.py

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+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Dict, List, Union
+
+import numpy as np
+import paddle
+from tqdm import tqdm
+
+
+class EncodeModel:
+    def __init__(
+        self,
+        model,
+        tokenizer,
+        pooling_method: str = "last",
+        query_instruction: str = None,
+        document_instruction: str = None,
+        eval_batch_size: int = 64,
+        max_seq_length: int = 512,
+    ):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.pooling_method = pooling_method
+        self.query_instruction = query_instruction
+        self.document_instruction = document_instruction
+        self.eval_batch_size = eval_batch_size
+        self.max_seq_length = max_seq_length
+
+        if paddle.device.is_compiled_with_cuda():
+            self.device = paddle.device.set_device("gpu")
+        else:
+            self.device = paddle.device.set_device("cpu")
+        self.model = self.model.to(self.device)
+
+        num_gpus = paddle.device.cuda.device_count()
+        if num_gpus > 1:
+            raise NotImplementedError("Multi-GPU is not supported yet.")
+
+    def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:
+        """
+        This function will be used to encode queries for retrieval task
+        if there is a instruction for queries, we will add it to the query text
+        """
+        if self.query_instruction is not None:
+            input_texts = [f"{self.query_instruction}{query}" for query in queries]
+        else:
+            input_texts = queries
+        return self.encode(input_texts)
+
+    def encode_corpus(self, corpus: List[Union[Dict[str, str], str]], **kwargs) -> np.ndarray:
+        """
+        This function will be used to encode corpus for retrieval task
+        if there is a instruction for docs, we will add it to the doc text
+        """
+        if isinstance(corpus[0], dict):
+            if self.document_instruction is not None:
+                input_texts = [
+                    "{}{} {}".format(self.document_instruction, doc.get("title", ""), doc["text"]).strip()
+                    for doc in corpus
+                ]
+            else:
+                input_texts = ["{} {}".format(doc.get("title", ""), doc["text"]).strip() for doc in corpus]
+        else:
+            if self.document_instruction is not None:
+                input_texts = [f"{self.document_instruction}{doc}" for doc in corpus]
+            else:
+                input_texts = corpus
+        return self.encode(input_texts)
+
+    @paddle.no_grad()
+    def encode(self, sentences: List[str], **kwargs) -> np.ndarray:
+        self.model.eval()
+        all_embeddings = []
+        for start_index in tqdm(range(0, len(sentences), self.eval_batch_size), desc="Batches"):
+            sentences_batch = sentences[start_index : start_index + self.eval_batch_size]
+
+            inputs = self.tokenizer(
+                sentences_batch,
+                padding=True,
+                truncation=True,
+                return_tensors="pd",
+                max_length=self.max_seq_length,
+                return_attention_mask=True,
+            )
+            outputs = self.model(
+                input_ids=inputs.input_ids,
+                attention_mask=inputs.attention_mask,
+                return_dict=True,
+                output_hidden_states=True,
+            )
+            last_hidden_state = outputs.hidden_states[-1]
+
+            if self.pooling_method == "last":
+                if self.tokenizer.padding_side == "right":
+                    sequence_lengths = inputs.attention_mask.sum(axis=1)
+                    last_token_indices = sequence_lengths - 1
+                    embeddings = last_hidden_state[paddle.arange(last_hidden_state.shape[0]), last_token_indices]
+                elif self.tokenizer.padding_side == "left":
+                    embeddings = last_hidden_state[:, -1]
+                else:
+                    raise NotImplementedError(f"Padding side {self.tokenizer.padding_side} not supported.")
+            elif self.pooling_method == "cls":
+                embeddings = last_hidden_state[:, 1]
+            elif self.pooling_method == "mean":
+                s = paddle.sum(last_hidden_state * inputs.attention_mask.unsqueeze(-1), axis=1)
+                d = inputs.attention_mask.sum(axis=1, keepdim=True)
+                embeddings = s / d
+            else:
+                raise NotImplementedError(f"Pooling method {self.pooling_method} not supported.")
+
+            embeddings = paddle.nn.functional.normalize(embeddings, p=2, axis=-1)
+
+            all_embeddings.append(embeddings.cpu().numpy().astype("float32"))
+
+        return np.concatenate(all_embeddings, axis=0)