Adding continued_pretraining task (#131)

* add continued pretraining script

* simplify config; add dataset_config option

* add ds configs in data mixer creator

* use extended sftconfig

* add option to avoid setting chat template

* fix data_configs bug

* add continued pretraining info

* add gpt2-nl recipe for continued pretraining example

* add final newline

* make style

* Update README.md

Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

* Update README.md

Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

* Update recipes/gpt2-nl/README.md

Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

* rename continued pretraining to cpt

* improve README

---------

Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

scripts/README.md

@@ -25,7 +25,7 @@ ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_con
 ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/deepspeed_zero3.yaml --num_processes={num_gpus} scripts/run_{task}.py recipes/{model_name}/{task}/config_qlora.yaml --load_in_4bit=false
 ```
 
-Here `{task}` refers to the type of training you wish to run (SFT, DPO, etc), while `{model_name}` refers to the choice of a recipe in the `recipes` directory. For example, to replicate Zephyr-7B-β you can run:
+Here `{task}` refers to the type of training you wish to run. Currently the following tasks are supported: continued pretraining `cpt`, supervised finetuning `sft`, and direct preference optimisation `dpo`. Note that `cpt` is only present in the `gpt-nl` example recipe. {model_name}` refers to the choice of a recipe in the `recipes` directory. For example, to replicate Zephyr-7B-β you can run:
 
 ```shell
 # Step 1 - train SFT policy

scripts/run_cpt.py

@@ -0,0 +1,204 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. 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.
+"""
+Continued pretraining script for decoder language models.
+"""
+
+import logging
+import random
+import sys
+
+import datasets
+import torch
+import transformers
+from transformers import set_seed
+
+from alignment import (
+    DataArguments,
+    H4ArgumentParser,
+    ModelArguments,
+    SFTConfig,
+    get_checkpoint,
+    get_datasets,
+    get_kbit_device_map,
+    get_peft_config,
+    get_quantization_config,
+    get_tokenizer,
+)
+from trl import SFTTrainer
+
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    parser = H4ArgumentParser((ModelArguments, DataArguments, SFTConfig))
+    model_args, data_args, training_args = parser.parse()
+
+    # Set seed for reproducibility
+    set_seed(training_args.seed)
+
+    ###############
+    # Setup logging
+    ###############
+    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 = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process a small summary
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Model parameters {model_args}")
+    logger.info(f"Data parameters {data_args}")
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Check for last checkpoint
+    last_checkpoint = get_checkpoint(training_args)
+    if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+        logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
+
+    ###############
+    # Load datasets
+    ###############
+    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits, configs=data_args.dataset_configs)
+
+    logger.info(
+        f"Training on the following datasets and their proportions:"
+        f" {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
+    )
+
+    train_dataset = raw_datasets["train"] if "train" in raw_datasets else None
+    eval_dataset = raw_datasets["test"] if "test" in raw_datasets else None
+
+    if train_dataset is None:
+        raise ValueError(
+            "Training set must be included (so make sure that your dataset has a split with" " 'train' in the name)."
+        )
+
+    if training_args.do_eval and eval_dataset is None:
+        raise ValueError("'--do_eval' enabled so make sure that your dataset has a split with 'test' in the name.")
+
+    ################
+    # Load tokenizer
+    ################
+    tokenizer = get_tokenizer(model_args, data_args, auto_set_chat_template=False)
+
+    with training_args.main_process_first(desc="Log a few random samples from the processed training set"):
+        for index in random.sample(range(len(raw_datasets["train"])), 3):
+            logger.info(f"Sample {index} of the processed training set:\n\n{raw_datasets['train'][index]['text']}")
+
+    #######################
+    # Load pretrained model
+    #######################
+    logger.info("*** Load pretrained model ***")
+    torch_dtype = (
+        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
+    )
+    quantization_config = get_quantization_config(model_args)
+
+    model_kwargs = dict(
+        revision=model_args.model_revision,
+        trust_remote_code=model_args.trust_remote_code,
+        use_flash_attention_2=model_args.use_flash_attention_2,
+        torch_dtype=torch_dtype,
+        use_cache=False if training_args.gradient_checkpointing else True,
+        device_map=get_kbit_device_map() if quantization_config is not None else None,
+        quantization_config=quantization_config,
+    )
+
+    ########################
+    # Initialize the Trainer
+    ########################
+    trainer = SFTTrainer(
+        model=model_args.model_name_or_path,
+        model_init_kwargs=model_kwargs,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        dataset_text_field=data_args.text_column,
+        max_seq_length=training_args.max_seq_length,
+        tokenizer=tokenizer,
+        packing=True,
+        peft_config=get_peft_config(model_args),
+        dataset_kwargs=training_args.dataset_kwargs,
+    )
+
+    ###############
+    # Training loop
+    ###############
+    logger.info("*** Train ***")
+    checkpoint = None
+    if training_args.resume_from_checkpoint is not None:
+        checkpoint = training_args.resume_from_checkpoint
+    elif last_checkpoint is not None:
+        checkpoint = last_checkpoint
+
+    train_result = trainer.train(resume_from_checkpoint=checkpoint)
+    metrics = train_result.metrics
+    metrics["train_samples"] = len(train_dataset)
+    trainer.log_metrics("train", metrics)
+    trainer.save_metrics("train", metrics)
+    trainer.save_state()
+
+    ##################################
+    # Save model and create model card
+    ##################################
+    logger.info("*** Save model ***")
+    trainer.save_model(training_args.output_dir)
+    logger.info(f"Model saved to {training_args.output_dir}")
+
+    # Save everything else on main process
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "dataset": list(data_args.dataset_mixer.keys()),
+        "dataset_tags": list(data_args.dataset_mixer.keys()),
+        "tags": ["alignment-handbook"],
+    }
+    if trainer.accelerator.is_main_process:
+        trainer.create_model_card(**kwargs)
+        # Restore k,v cache for fast inference
+        trainer.model.config.use_cache = True
+        trainer.model.config.save_pretrained(training_args.output_dir)
+
+    ##########
+    # Evaluate
+    ##########
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+        metrics["eval_samples"] = len(eval_dataset)
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    if training_args.push_to_hub is True:
+        logger.info("Pushing to hub...")
+        trainer.push_to_hub(**kwargs)
+
+    logger.info("*** Training complete ***")
+
+
+if __name__ == "__main__":
+    main()

scripts/run_dpo.py

@@ -77,7 +77,7 @@ def main():
     ###############
     # Load datasets
     ###############
-    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
+    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits, configs=data_args.dataset_configs)
     logger.info(
         f"Training on the following splits: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
     )

scripts/run_sft.py

@@ -85,7 +85,7 @@ def main():
     ###############
     # Load datasets
     ###############
-    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
+    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits, configs=data_args.dataset_configs)
     logger.info(
         f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
     )