experimental expansion of ctx len

scripts/finetune.py

@@ -6,22 +6,20 @@ import os
 import random
 import signal
 import sys
-from functools import partial
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 import fire
 import torch
 import yaml
-from transformers import GenerationConfig
-
-from axolotl.utils.data import load_prepare_datasets
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_tokenizer
 
 # add src to the pythonpath so we don't need to pip install this
 from optimum.bettertransformer import BetterTransformer
+from transformers import GenerationConfig
 
+from axolotl.utils.data import load_prepare_datasets, load_pretraining_dataset
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.trainer import setup_trainer
 from axolotl.utils.validation import validate_config
@@ -194,9 +192,19 @@ def train(
     if check_not_in(
         ["inference", "shard", "merge_lora"], kwargs
     ):  # don't need to load dataset for these
-        train_dataset, eval_dataset = load_prepare_datasets(
+        if not cfg.pretraining_dataset:
-            tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
+            train_dataset, eval_dataset = load_prepare_datasets(
-        )
+                tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
+            )
+        else:
+            if cfg.pretraining_dataset is True:
+                pretraining_dataset = "togethercomputer/RedPajama-Data-1T"
+            else:
+                pretraining_dataset = cfg.pretraining_dataset
+            train_dataset = load_pretraining_dataset(
+                pretraining_dataset, tokenizer, max_tokens=cfg.sequence_len
+            )
+            eval_dataset = None
 
     if cfg.debug or "debug" in kwargs:
         logging.info("check_dataset_labels...")
@@ -246,7 +254,7 @@ def train(
         logging.info("check_dataset_labels...")
         check_dataset_labels(
             train_dataset.select(
-                [random.randrange(0, len(train_dataset) - 1) for i in range(5)]
+                [random.randrange(0, len(train_dataset) - 1) for i in range(5)]  # nosec
             ),
             tokenizer,
         )
@@ -255,10 +263,7 @@ def train(
         logging.info("Finished preparing dataset. Exiting...")
         return
 
-    try:
+    model.train()
-        model.train()
-    except:
-        pass
 
     trainer = setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer)
 
@@ -275,14 +280,15 @@ def train(
 
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
     if cfg.local_rank == 0:
-        def terminate_handler(signum, frame, model):
+
+        def terminate_handler(_, __, model):
             if cfg.flash_optimum:
                 model = BetterTransformer.reverse(model)
             model.save_pretrained(cfg.output_dir)
             sys.exit(0)
+
         signal.signal(
-            signal.SIGINT,
+            signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
-            lambda signum, frame: terminate_handler(signum, frame, model)
         )
 
     logging.info("Starting trainer...")
@@ -304,7 +310,9 @@ def train(
             )
 
     if cfg.flash_optimum:
-        with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
+        with torch.backends.cuda.sdp_kernel(
+            enable_flash=True, enable_math=True, enable_mem_efficient=True
+        ):
             trainer.train(resume_from_checkpoint=resume_from_checkpoint)
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)

src/axolotl/utils/data.py

@@ -5,7 +5,8 @@ from hashlib import md5
 from pathlib import Path
 from typing import List, Tuple, Union
 
-from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
+import torch
+from datasets import Dataset, DatasetDict, IterableDataset, load_dataset, load_from_disk
 from huggingface_hub import hf_hub_download
 from transformers import PreTrainedTokenizerBase
 
@@ -385,3 +386,32 @@ def load_prepare_datasets(
     eval_dataset = dataset["test"]
 
     return train_dataset, eval_dataset
+
+
+class PretrainingDatasetWrapper(IterableDataset):
+    """
+    Wrapper for pretraining dataset that avoids loading the dataset into memory
+    """
+
+    def __init__(self, tokenizer, dataset_path, max_tokens=2048):
+        self.tokenizer = tokenizer
+        self.dataset_path = dataset_path
+        self.max_tokens = max_tokens
+
+    def __iter__(self):
+        buffer = []
+        for sample in load_dataset(
+            self.dataset_path,
+            name="all",
+            split="train",
+            streaming=True,
+        ).shuffle(buffer_size=10000):
+            buffer += self.tokenizer(sample["text"])["input_ids"]
+            buffer += [self.tokenizer.eos_token_id]
+            while len(buffer) > self.max_tokens:
+                yield torch.tensor(buffer[: self.max_tokens])
+                buffer = buffer[self.max_tokens :]
+
+
+def load_pretraining_dataset(path, tokenizer, max_tokens=2048):
+    return PretrainingDatasetWrapper(tokenizer, path, max_tokens=max_tokens)