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| Original file line number | Diff line number | Diff line change |
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| # PECOS XMR Reranker | ||
|
|
||
| This is a reranker for the PECOS XMR model. It is based on huggingface's transformers library. The reranker can be run in both | ||
| single process and distributed mode. It is based on the paper [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://arxiv.org/abs/2310.08319). | ||
|
|
||
| ## How to run | ||
| ### Single process | ||
| To run the reranker in single process mode, you can use the following command: | ||
|
|
||
| ```bash | ||
| python -m pecos.xmr.reranker.train --config_json_path <path_to_config_file> | ||
| ``` | ||
|
|
||
| ### Distributed mode | ||
| To run the reranker in distributed mode, you can use the following command to initialize the distributed configuration: | ||
| ```bash | ||
| accelerate config | ||
| ``` | ||
|
|
||
| Then you can run the reranker using the following command: | ||
| ```bash | ||
| accelerate launch -m pecos.xmr.reranker.train --config_json_path <path_to_config_file> | ||
| ``` | ||
|
|
||
| ### Predictions | ||
| To run the reranker in prediction mode, you can use the following command: | ||
| ```bash | ||
| python -m pecos.xmr.reranker.predict --config_json_path <path_to_config_file> | ||
| ``` | ||
|
|
||
| ## Configuration file | ||
|
|
||
| ### Training | ||
| Here is an example of the configuration file for training: | ||
| ```json | ||
| { | ||
| "train_params": { | ||
| "__meta__": { | ||
| "class_fullname": "pecos.xmr.reranker.model###RankingModel.TrainParams" | ||
| }, | ||
| "target_data_folder": "/home/ec2-user/docker_disk/datasets/ms_marco_partitioned/target", | ||
| "input_data_folder": "/home/ec2-user/docker_disk/datasets/ms_marco_partitioned/input", | ||
| "label_data_folder": "/home/ec2-user/docker_disk/datasets/ms_marco_partitioned/label", | ||
| "training_args": { | ||
| "__meta__": { | ||
| "class_fullname": "pecos.xmr.reranker.trainer###RankLlamaTrainer.TrainingArgs" | ||
| }, | ||
| "learning_rate": 1e-4, | ||
| "output_dir": "./ds_model", | ||
| "per_device_train_batch_size": 8, | ||
| "gradient_accumulation_steps": 8, | ||
| "max_steps": -1, | ||
| "logging_strategy": "steps", | ||
| "logging_first_step": false, | ||
| "logging_steps": 10, | ||
| "save_strategy": "steps", | ||
| "save_steps": 50, | ||
| "save_total_limit": 5, | ||
| "seed": 42, | ||
| "data_seed": 42, | ||
| "bf16": true, | ||
| "dataloader_num_workers": 2, | ||
| "dataloader_prefetch_factor": 10, | ||
| "gradient_checkpointing": true, | ||
| "train_group_size": 16 | ||
| } | ||
| }, | ||
| "model_params": { | ||
| "__meta__": { | ||
| "class_fullname": "pecos.xmr.reranker.model###RankingModel.ModelParams" | ||
| }, | ||
| "encoder_args": { | ||
| "__meta__": { | ||
| "class_fullname": "pecos.xmr.reranker.model###CrossEncoder.Config" | ||
| }, | ||
| "model_shortcut": "meta-llama/Llama-2-7b-hf", | ||
| "model_init_kwargs": {}, | ||
| "model_modifier": { | ||
| "modifier_type": "peft", | ||
| "config_type": "LoraConfig" , | ||
| "config": { | ||
| "r": 8, | ||
| "lora_alpha": 64, | ||
| "target_modules": ["q_proj", "v_proj"], | ||
| "modules_to_save": ["score", "classifier"], | ||
| "lora_dropout": 0.1 | ||
| } | ||
| } | ||
| }, | ||
| "positive_passage_no_shuffle": false, | ||
| "negative_passage_no_shuffle": false, | ||
| "rerank_max_len": 196, | ||
| "query_prefix": "query: ", | ||
| "passage_prefix": "document: ", | ||
| "inp_id_col": "inp_id", | ||
| "lbl_idxs_col": "ret_idxs", | ||
| "score_col": "rel", | ||
| "keyword_col_name": "keywords", | ||
| "content_col_names": ["title", "contents"], | ||
| "append_eos_token": false, | ||
| "pad_to_multiple_of": 16 | ||
| } | ||
| } | ||
| ``` | ||
|
|
||
| ### Prediction | ||
| Following is the example of the configuration file for prediction: | ||
| ```json | ||
| { | ||
| "model_name_or_path": "/tmp/pecosdev/ds_model", | ||
| "target_data_folder": "/home/ec2-user/docker_disk/datasets/msmarcoeval/target", | ||
| "input_data_folder": "/home/ec2-user/docker_disk/datasets/msmarcoeval/input", | ||
| "label_data_folder": "/home/ec2-user/docker_disk/datasets/msmarcoeval/label", | ||
| "output_dir": "/tmp/xmrout", | ||
| "per_device_eval_batch_size": 512, | ||
| "dataloader_num_workers": 1, | ||
| "dataloader_prefetch_factor": 10, | ||
| "rerank_max_len": 196, | ||
| "query_prefix": "query: ", | ||
| "passage_prefix": "document: ", | ||
| "inp_id_col": "inp_id", | ||
| "lbl_id_col": "lbl_id", | ||
| "keyword_col_name": "keywords", | ||
| "content_col_names": ["title", "contents"], | ||
| "append_eos_token": false, | ||
| "pad_to_multiple_of": 8, | ||
| "device": "cuda", | ||
| "model_init_kwargs": { | ||
| "device_map": "auto" | ||
| } | ||
| } | ||
| ``` | ||
|
|
||
| ## Data Schema | ||
| The column names for the data schema are configurable through the json configuration file. Following | ||
| are the various schemas that are supported by the reranker: | ||
|
|
||
| (1) Learning Target Schema | ||
| ``` | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | Column Name | Data Type | Description | | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | inp_id | int32 | input id | | ||
| # | lbl_id | array<int32> | an array of label_id | | ||
| # | score | array<float> | an array of rel_score | | ||
| # +-----------------+---------------+-----------------------+ | ||
| ``` | ||
|
|
||
| (2) Input Feature Store Schema | ||
| ``` | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | Column Name | Data Type | Description | | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | inp_id | int32 | input id | | ||
| # | keywords | string | keyword string | | ||
| # +-----------------+---------------+-----------------------+ | ||
| ``` | ||
|
|
||
| (3) Label Feature Store Schema | ||
|
|
||
| The label feature store supports variable number of columns. The column names | ||
| can be provided in the configuration file. | ||
| ``` | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | Column Name | Data Type | Description | | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | lbl_id | int32 | input id | | ||
| # | title | string | title text | | ||
| # | content | string | content string | | ||
| # | ... | string | content string | | ||
| # +-----------------+---------------+-----------------------+ | ||
| ``` | ||
|
|
||
| (4) Evaluation Schema | ||
| ``` | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | Column Name | Data Type | Description | | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | inp_id | int32 | input id | | ||
| # | lbl_id | int32 | label_id | | ||
| # +-----------------+---------------+-----------------------+ | ||
| ``` | ||
|
|
||
| (5) Evaluation Input Feature Store Schema | ||
| ``` | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | Column Name | Data Type | Description | | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | inp_id | int32 | input id | | ||
| # | keywords | string | keyword string | | ||
| # +-----------------+---------------+-----------------------+ | ||
| ``` | ||
|
|
||
| (6) Evaluation Label Feature Store Schema | ||
| ``` | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | Column Name | Data Type | Description | | ||
| # +-----------------+---------------+-----------------------+ | ||
| # | lbl_id | int32 | input id | | ||
| # | title | string | title text | | ||
| # | content | string | content string | | ||
| # | ... | string | content string | | ||
| # +-----------------+---------------+-----------------------+ | ||
| ``` |
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