Make RewardBench pip installable + runable!

README.md

@@ -24,8 +24,36 @@ The two primary scripts to generate results (more in `scripts/`):
 2. `scripts/run_dpo.py`: Run evaluations for direct preference optimization (DPO) models (and other models using implicit rewards, such as KTO).
 3. `scripts/train_rm.py`: A basic RM training script built on [TRL](https://github.com/huggingface/trl).
 
-## Installation
-Please install `torch` on your system, and then install the following requirements.
+## Quick Usage
+RewardBench let's you quickly evaluate any reward model on any preference set. 
+To install for quick usage, install with pip as:
+```
+pip install reward bench
+```
+Then, run a following:
+```
+rewardbench --model={yourmodel} --dataset={yourdataset} --batch_size=8
+```
+For a DPO model, pass --ref_model={} and the script will automatically route there.
+Automatically uses Tokenizers chat templates, but can also use fastchat conv templates.
+
+To run the core Reward Bench evaluation set, run:
+```
+rewardbench --model={yourmodel}
+```
+
+Examples:
+1. Normal operation
+```
+rewardbench --model=OpenAssistant/reward-model-deberta-v3-large-v2 --dataset=allenai/ultrafeedback_binarized_cleaned --split=test_gen --chat_template=raw
+```
+2. DPO model from local dataset (note `--load_json`)
+```
+rewardbench --model=Qwen/Qwen1.5-0.5B-Chat --ref_model=Qwen/Qwen1.5-0.5B --dataset=/net/nfs.cirrascale/allennlp/jacobm/herm/data/berkeley-nectar-binarized-preferences-random-rejected.jsonl --load_json
+```
+
+## Full Installation
+To install from source, please install `torch` on your system, and then install the following requirements.
 ```
 pip install -e .
 ```

rewardbench/__init__.py

@@ -12,14 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "0.1.0.dev"
+__version__ = "0.1.1"
 from .chattemplates import *  # noqa
 from .dpo import DPOInference
 from .models import DPO_MODEL_CONFIG, REWARD_MODEL_CONFIG
 from .utils import (
     check_tokenizer_chat_template,
     load_bon_dataset,
     load_eval_dataset,
+    load_preference_dataset,
     prepare_dialogue,
     prepare_dialogue_from_tokenizer,
     save_to_hub,
@@ -31,6 +32,7 @@
     DPO_MODEL_CONFIG,
     load_bon_dataset,
     load_eval_dataset,
+    load_preference_dataset,
     prepare_dialogue,
     prepare_dialogue_from_tokenizer,
     REWARD_MODEL_CONFIG,

rewardbench/__main__.py

@@ -0,0 +1,329 @@
+# Copyright 2023 AllenAI. 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.
+
+# Run RewardBench (evaluate any reward model on any dataet)
+import argparse
+import json
+import logging
+import os
+import sys
+
+import torch
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from tqdm import tqdm
+from transformers import AutoTokenizer
+
+from rewardbench import (
+    DPO_MODEL_CONFIG,
+    REWARD_MODEL_CONFIG,
+    check_tokenizer_chat_template,
+    load_preference_dataset,
+)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Evaluate a reward model.")
+
+    # core args
+    parser.add_argument("--dataset", type=str, required=True, help="The dataset to evaluate on.")
+    parser.add_argument("--split", type=str, default=None, help="The split to evaluate on.")
+    parser.add_argument("--model", type=str, required=True, help="The model to evaluate.")
+    parser.add_argument("--ref_model", type=str, default=None, help="The reference model to compare against.")
+    parser.add_argument("--tokenizer", type=str, default=None, help="The tokenizer to use (defaults to model).")
+    parser.add_argument(
+        "--chat_template",
+        type=str,
+        default=None,
+        help="The chat template to use (defaults to from tokenizer, from chattemplate).",
+    )
+
+    # inference args
+    parser.add_argument("--batch_size", type=int, default=8, help="The batch size to use.")
+    parser.add_argument("--max_length", type=int, default=512, help="The max length to use.")
+
+    # system args
+    parser.add_argument("--load_json", action="store_true", default=False, help="Load dataset as json.")
+    parser.add_argument("--trust_remote_code", action="store_true", default=False, help="Trust remote code.")
+    parser.add_argument("--debug", action="store_true", default=False, help="Debug mode.")
+    parser.add_argument("--output_dir", type=str, default="results/", help="The output directory to save results.")
+    parser.add_argument("--save_all", action="store_true", default=False, help="Save all results.")
+    args = parser.parse_args()
+
+    ###############
+    # Setup logging
+    ###############
+    accelerator = Accelerator()
+    current_device = accelerator.process_index
+
+    logger = get_logger(__name__)
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = logging.INFO
+    logger.setLevel(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    logger.info(f"Running reward model on {args.model} with chat template {args.chat_template}")
+    if args.trust_remote_code:
+        logger.info("Loading model with Trust Remote Code")
+
+    # basic checks from config
+    if args.ref_model:
+        is_dpo = True
+        MODEL_CONFIGS = DPO_MODEL_CONFIG
+        assert args.model != args.ref_model, "policy and reference model should be different"
+        from trl.trainer.utils import DPODataCollatorWithPadding
+
+        from rewardbench import DPOInference
+    else:
+        is_dpo = False
+        MODEL_CONFIGS = REWARD_MODEL_CONFIG
+
+    if args.chat_template:
+        from fastchat.conversation import get_conv_template
+
+        conv = get_conv_template(args.chat_template)
+    else:
+        conv = None
+
+    if args.model in MODEL_CONFIGS:
+        config = MODEL_CONFIGS[args.model]
+    else:
+        config = MODEL_CONFIGS["default"]
+    logger.info(f"Using reward model config: {config}")
+
+    # Default entries
+    # "model_builder": AutoModelForSequenceClassification.from_pretrained,
+    # "pipeline_builder": pipeline,
+    # "quantized": True,
+    # "custom_dialogue": False,
+    # "model_type": "Seq. Classifier"
+
+    if not is_dpo:
+        quantized = config["quantized"]  # only Starling isn't quantized for now
+        custom_dialogue = config["custom_dialogue"]
+        pipeline_builder = config["pipeline_builder"]
+        _ = config["model_type"]
+        if custom_dialogue:
+            raise NotImplementedError("Custom dialogue not implemented yet for simpler data formatting.")
+
+    model_builder = config["model_builder"]
+
+    #########################
+    # load dataset
+    #########################
+    logger.info("*** Load dataset ***")
+    tokenizer_path = args.tokenizer if args.tokenizer else args.model
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=args.trust_remote_code)
+    dataset = load_preference_dataset(
+        args.dataset, split=args.split, json=args.load_json, tokenizer=tokenizer, conv=conv
+    )
+
+    if args.debug:
+        dataset = dataset.select(range(10))
+
+    logger.info("*** Load reward model ***")
+
+    ############################
+    # Load DPO model pipeline
+    ############################
+    if is_dpo:
+        tokenizer.pad_token = tokenizer.eos_token
+        # if no BOS token, set as pad token, e.g. QWEN models
+        if tokenizer.bos_token is None:
+            tokenizer.bos_token_id = tokenizer.eos_token_id
+            tokenizer.pad_token_id = tokenizer.eos_token_id
+
+        model_kwargs = {
+            "load_in_8bit": True,
+            "device_map": "auto",
+            "torch_dtype": torch.float16 if torch.cuda.is_available() else None,
+        }
+        model = model_builder(
+            args.model,
+            trust_remote_code=args.trust_remote_code,
+            **model_kwargs,
+        )
+        ref_model = model_builder(
+            args.ref_model,
+            trust_remote_code=args.trust_remote_code,
+            **model_kwargs,
+        )
+
+        # use internal inference functions in DPO trainer
+        dpo = DPOInference(
+            model,
+            ref_model,
+            tokenizer=tokenizer,
+            accelerator=accelerator,
+            # norm is norm, avg is average, sum is sum
+        )
+
+        # tokenize dataset
+        column_names = list(dataset.features)
+
+        tokenized_dataset = dataset.map(dpo.tokenize_row, remove_columns=column_names)
+        dataloader = torch.utils.data.DataLoader(
+            tokenized_dataset,
+            batch_size=args.batch_size,
+            collate_fn=DPODataCollatorWithPadding(
+                pad_token_id=tokenizer.pad_token_id,
+                label_pad_token_id=dpo.label_pad_token_id,
+                is_encoder_decoder=dpo.is_encoder_decoder,
+            ),
+            # collate_fn = lambda x: x, # fix weird batching error
+            shuffle=False,
+            drop_last=False,
+        )
+
+    ############################
+    # Load classifier model pipeline
+    ############################
+    else:
+        reward_pipeline_kwargs = {
+            "batch_size": args.batch_size,  # eval_args.inference_batch_size,
+            "truncation": True,
+            "padding": True,
+            "max_length": args.max_length,
+            "function_to_apply": "none",  # Compute raw logits
+            "return_token_type_ids": False,
+        }
+        if quantized:
+            model_kwargs = {
+                "load_in_8bit": True,
+                "device_map": {"": current_device},
+                "torch_dtype": torch.float16 if torch.cuda.is_available() else None,
+            }
+        else:
+            model_kwargs = {"device_map": {"": current_device}}
+
+        model = model_builder(args.model, **model_kwargs, trust_remote_code=args.trust_remote_code)
+        reward_pipe = pipeline_builder(
+            "text-classification",  # often not used
+            model=model,
+            tokenizer=tokenizer,
+        )
+
+        # set pad token to eos token if not set
+        if reward_pipe.tokenizer.pad_token_id is None:
+            reward_pipe.model.config.pad_token_id = reward_pipe.tokenizer.eos_token_id
+            reward_pipe.tokenizer.pad_token_id = reward_pipe.tokenizer.eos_token_id
+        # For models whose config did not contains `pad_token_id`
+        if reward_pipe.model.config.pad_token_id is None:
+            reward_pipe.model.config.pad_token_id = reward_pipe.tokenizer.pad_token_id
+
+        # if using fastchat template (no template in tokenizer), make the RM tokenizer output an EOS token
+        if not check_tokenizer_chat_template(tokenizer):
+            reward_pipe.tokenizer.add_eos_token = True
+
+        dataloader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=args.batch_size,
+            shuffle=False,
+            drop_last=False,
+        )
+
+        dataloader, model = accelerator.prepare(dataloader, reward_pipe.model)
+        reward_pipe.model = model
+
+    ############################
+    # Run inference
+    ############################
+
+    results = []
+    scores_chosen = []
+    scores_rejected = []
+    for step, batch in enumerate(tqdm(dataloader, desc="RM batch steps")):
+        logger.info(f"RM inference step {step}/{len(dataloader)}")
+
+        if is_dpo:
+            rewards_chosen, rewards_rejected = dpo.inference_step(batch)
+        else:
+            rewards_chosen = reward_pipe(batch["text_chosen"], **reward_pipeline_kwargs)
+            rewards_rejected = reward_pipe(batch["text_rejected"], **reward_pipeline_kwargs)
+
+        # for each item in batch, record 1 if chosen > rejected
+        # extra score from dict within batched results (e.g. logits)
+        # [{'label': 'LABEL_1', 'score': 0.6826171875},... ]
+        if isinstance(rewards_chosen[0], dict):
+            score_chosen_batch = [result["score"] for result in rewards_chosen]
+            score_rejected_batch = [result["score"] for result in rewards_rejected]
+        # for classes that directly output scores (custom code)
+        else:
+            score_chosen_batch = rewards_chosen.cpu().numpy().tolist()
+            score_rejected_batch = rewards_rejected.cpu().numpy().tolist()
+
+        # log results
+        [
+            results.append(1) if chosen > rejected else results.append(0)
+            for chosen, rejected in zip(score_chosen_batch, score_rejected_batch)
+        ]
+        scores_chosen.extend(score_chosen_batch)
+        scores_rejected.extend(score_rejected_batch)
+
+    ############################
+    # compile scores
+    ############################
+    # calculate accuracy
+    accuracy = sum(results) / len(results)
+    logger.info(f"Results: {accuracy}, on {len(results)} prompts")
+
+    ############################
+    # compile scores
+    ############################
+    # save score in json to args.output_dir + args.model + ".json"
+    output_path = args.output_dir + args.model + ".json"
+    dirname = os.path.dirname(output_path)
+    os.makedirs(dirname, exist_ok=True)
+
+    # remove old data
+    if os.path.exists(output_path):
+        os.remove(output_path)
+
+    with open(output_path, "w") as f:
+        json.dump(
+            {
+                "accuracy": accuracy,
+                "num_prompts": len(results),
+                "model": args.model,
+                "ref_model": args.ref_model,
+                "tokenizer": tokenizer_path,
+                "chat_template": args.chat_template,
+            },
+            f,
+        )
+
+    # if save_all is passed, save a large jsonl with all scores_chosen, scores_rejected
+    if args.save_all:
+        output_path = args.output_dir + args.model + "_all.jsonl"
+        dirname = os.path.dirname(output_path)
+        os.makedirs(dirname, exist_ok=True)
+
+        # remove old data
+        if os.path.exists(output_path):
+            os.remove(output_path)
+
+        with open(output_path, "w") as f:
+            for chosen, rejected in zip(scores_chosen, scores_rejected):
+                f.write(json.dumps({"chosen": scores_chosen, "rejected": scores_rejected}) + "\n")
+
+
+if __name__ == "__main__":
+    main()