adding finetuning script

train.py

@@ -0,0 +1,297 @@
+"""
+Fine-Tune SantaCoder on code/text dataset
+"""
+# copied from https://github.com/loubnabnl/santacoder-finetuning
+# removed all parts related to FIM
+# set --subset to default to None instead of "data" to avoid issues with my own datasets.
+# added --resume_from_checkpoint to resume training from a checkpoint (untested)
+
+
+import argparse
+import os
+import random
+import sys
+
+import numpy as np
+import torch
+from datasets import load_dataset
+from torch.utils.data import IterableDataset
+from torch.utils.data.dataloader import DataLoader
+from tqdm import tqdm
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    Trainer,
+    TrainingArguments,
+    logging,
+    set_seed,
+)
+
+# import fim
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--resume_from_checkpoint", type=str, default=None) #can pass a checkpoint dir to resume training
+    parser.add_argument("--model_path", type=str, default="bigcode/santacoder")
+    parser.add_argument("--dataset_name", type=str, default="bigcode/the-stack-dedup")
+    parser.add_argument("--subset", type=str, default=None) #None a bodge but not the solution
+    parser.add_argument("--split", type=str, default="train")
+    parser.add_argument("--size_valid_set", type=int, default=4000)
+    parser.add_argument("--streaming", action="store_true")
+    parser.add_argument("--shuffle_buffer", type=int, default=5000)
+    parser.add_argument("--data_column", type=str, default="content")
+
+    parser.add_argument("--seq_length", type=int, default=1024)
+    parser.add_argument("--max_steps", type=int, default=10000)
+    parser.add_argument("--batch_size", type=int, default=2)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=8)
+    parser.add_argument("--eos_token_id", type=int, default=49152)
+
+    parser.add_argument("--learning_rate", type=float, default=5e-5)
+    parser.add_argument("--lr_scheduler_type", type=str, default="cosine")
+    parser.add_argument("--num_warmup_steps", type=int, default=100)
+    parser.add_argument("--weight_decay", type=float, default=0.05)
+
+    parser.add_argument("--local_rank", type=int, default=0)
+    parser.add_argument("--no_fp16", action="store_false")
+    parser.add_argument("--bf16", action="store_true")
+    parser.add_argument("--no_gradient_checkpointing", action="store_false")
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--num_workers", type=int, default=None)
+    parser.add_argument("--output_dir", type=str, default="./checkpoints")
+    parser.add_argument("--log_freq", default=1, type=int)
+    parser.add_argument("--eval_freq", default=1000, type=int)
+    parser.add_argument("--save_freq", default=1000, type=int)
+
+    # parser.add_argument("--fim_rate", type=float, default=0)
+    # parser.add_argument("--fim_spm_rate", type=float, default=0)
+    return parser.parse_args()
+
+
+def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):
+    """
+    Estimate the average number of characters per token in the dataset.
+    """
+    total_characters, total_tokens = 0, 0
+    for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
+        total_characters += len(example[data_column])
+        total_tokens += len(tokenizer(example[data_column]).tokens())
+
+    return total_characters / total_tokens
+
+
+class ConstantLengthDataset(IterableDataset):
+    """
+    Iterable dataset that returns constant length chunks of tokens from stream of text files.
+        Args:
+            tokenizer (Tokenizer): The processor used for proccessing the data.
+            dataset (dataset.Dataset): Dataset with text files.
+            infinite (bool): If True the iterator is reset after dataset reaches end else stops.
+            seq_length (int): Length of token sequences to return.
+            num_of_sequences (int): Number of token sequences to keep in buffer.
+            chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
+            # fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.
+            # fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.
+            seed (int): Seed for random number generator.
+    """
+
+    def __init__(
+        self,
+        tokenizer,
+        dataset,
+        infinite=False,
+        seq_length=1024,
+        num_of_sequences=1024,
+        chars_per_token=3.6,
+        content_field="content",
+        # fim_rate=0.5,
+        # fim_spm_rate=0.5,
+        seed=0,
+    ):
+        self.tokenizer = tokenizer
+        self.concat_token_id = (
+            tokenizer.eos_token_id if tokenizer.eos_token_id else args.eos_token_id
+        )
+        self.dataset = dataset
+        self.seq_length = seq_length
+        self.infinite = infinite
+        self.current_size = 0
+        self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
+        self.content_field = content_field
+        # self.fim_rate = fim_rate
+        # self.fim_spm_rate = fim_spm_rate
+        self.seed = seed
+
+        # (
+        #     self.suffix_tok_id,
+        #     self.prefix_tok_id,
+        #     self.middle_tok_id,
+        #     self.pad_tok_id,
+        # ) = fim.get_fim_token_ids(self.tokenizer)
+        # if not self.suffix_tok_id and self.fim_rate > 0:
+        #     print("FIM is not supported by tokenizer, disabling FIM")
+        #     self.fim_rate = 0
+
+    def __iter__(self):
+        iterator = iter(self.dataset)
+        more_examples = True
+        while more_examples:
+            buffer, buffer_len = [], 0
+            while True:
+                if buffer_len >= self.max_buffer_size:
+                    break
+                try:
+                    buffer.append(next(iterator)[self.content_field])
+                    buffer_len += len(buffer[-1])
+                except StopIteration:
+                    if self.infinite:
+                        iterator = iter(self.dataset)
+                    else:
+                        more_examples = False
+                        break
+            tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
+            all_token_ids = []
+
+            np_rng = np.random.RandomState(seed=self.seed)
+            for tokenized_input in tokenized_inputs:
+                # optionally do FIM permutations
+                # if self.fim_rate > 0:
+                #     tokenized_input, np_rng = fim.permute(
+                #         tokenized_input,
+                #         np_rng,
+                #         self.suffix_tok_id,
+                #         self.prefix_tok_id,
+                #         self.middle_tok_id,
+                #         self.pad_tok_id,
+                #         fim_rate=self.fim_rate,
+                #         fim_spm_rate=self.fim_spm_rate,
+                #         truncate_or_pad=False,
+                #     )
+
+                all_token_ids.extend(tokenized_input + [self.concat_token_id])
+            examples = []
+            for i in range(0, len(all_token_ids), self.seq_length):
+                input_ids = all_token_ids[i : i + self.seq_length]
+                if len(input_ids) == self.seq_length:
+                    examples.append(input_ids)
+            random.shuffle(examples)
+            for example in examples:
+                self.current_size += 1
+                yield {
+                        "input_ids": torch.LongTensor(example),
+                        "labels": torch.LongTensor(example),
+                    }
+
+def create_datasets(tokenizer, args):
+    dataset = load_dataset(
+        args.dataset_name,
+        data_dir=args.subset,
+        split=args.split,
+        use_auth_token=True,
+        num_proc=args.num_workers if not args.streaming else None,
+        streaming=args.streaming,
+    )
+    if args.streaming:
+        print("Loading the dataset in streaming mode")
+        valid_data = dataset.take(args.size_valid_set)
+        train_data = dataset.skip(args.size_valid_set)
+        train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
+    else:
+        dataset = dataset.train_test_split(test_size=0.005, seed=args.seed)
+        train_data = dataset["train"]
+        valid_data = dataset["test"]
+        print(
+            f"Size of the train set: {len(train_data)}. Size of the validation set: {len(valid_data)}"
+        )
+    chars_per_token = chars_token_ratio(train_data, tokenizer, args.data_column)
+    print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
+    train_dataset = ConstantLengthDataset(
+        tokenizer,
+        train_data,
+        infinite=True,
+        seq_length=args.seq_length,
+        chars_per_token=chars_per_token,
+        content_field=args.data_column,
+        # fim_rate=args.fim_rate,
+        # fim_spm_rate=args.fim_spm_rate,
+        seed=args.seed,
+    )
+    valid_dataset = ConstantLengthDataset(
+        tokenizer,
+        valid_data,
+        infinite=False,
+        seq_length=args.seq_length,
+        chars_per_token=chars_per_token,
+        content_field=args.data_column,
+        # fim_rate=args.fim_rate,
+        # fim_spm_rate=args.fim_spm_rate,
+        seed=args.seed,
+    )
+
+    return train_dataset, valid_dataset
+
+
+def run_training(args, train_data, val_data):
+    print("Loading the model")
+    # disable caching mechanism when using gradient checkpointing
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model_path,
+        trust_remote_code=True,
+        use_cache=not args.no_gradient_checkpointing,
+    )
+    train_data.start_iteration = 0
+
+    print(f"Starting main loop")
+
+    training_args = TrainingArguments(
+        output_dir=args.output_dir,
+        dataloader_drop_last=True,
+        evaluation_strategy="steps",
+        max_steps=args.max_steps,
+        eval_steps=args.eval_freq,
+        save_steps=args.save_freq,
+        logging_steps=args.log_freq,
+        per_device_train_batch_size=args.batch_size,
+        per_device_eval_batch_size=args.batch_size,
+        learning_rate=args.learning_rate,
+        lr_scheduler_type=args.lr_scheduler_type,
+        warmup_steps=args.num_warmup_steps,
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        gradient_checkpointing=args.no_gradient_checkpointing,
+        fp16=args.no_fp16,
+        bf16=args.bf16,
+        weight_decay=args.weight_decay,
+        run_name=f"santacoder-{args.subset}",
+        # report_to="wandb", #I am not using that, so I just comment it out to avoid errors?
+    )
+
+    trainer = Trainer(
+        model=model, args=training_args, train_dataset=train_data, eval_dataset=val_data
+    )
+
+    print("Training...")
+    trainer.train(args.resume_from_checkpoint) #can resume here
+
+    print("Saving last checkpoint of the model")
+    model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
+
+
+def main(args):
+    tokenizer = AutoTokenizer.from_pretrained(args.model_path, use_auth_token=True)
+
+    train_dataset, eval_dataset = create_datasets(tokenizer, args)
+
+    run_training(args, train_dataset, eval_dataset)
+
+
+if __name__ == "__main__":
+    print(sys.argv) #to abort early
+    args = get_args()
+    print(args) #see if the file actually red?
+    set_seed(args.seed)
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    logging.set_verbosity_info() #lower verbosity
+
+    main(args)