Merge pull request #67 from OpenAccess-AI-Collective/refactor-tokenizer-load

load the tokenizer seperately from the model

scripts/finetune.py

@@ -5,7 +5,7 @@ import random
 import signal
 import sys
 from pathlib import Path
-from typing import Optional
+from typing import Optional, List, Dict, Any, Union
 
 import fire
 import torch
@@ -21,7 +21,7 @@ src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)
 
 from axolotl.utils.data import load_prepare_datasets
-from axolotl.utils.models import load_model
+from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.trainer import setup_trainer
 from axolotl.utils.wandb import setup_wandb_env_vars
 
@@ -117,6 +117,10 @@ def choose_config(path: Path):
     return chosen_file
 
 
+def check_not_in(list1: List[str], list2: Union[Dict[str, Any], List[str]]) -> bool:
+    return not any(el in list2 for el in list1)
+
+
 def train(
     config: Path = Path("configs/"),
     prepare_ds_only: bool = False,
@@ -161,13 +165,30 @@ def train(
 
     validate_config(cfg)
 
+    # load the tokenizer first
+    logging.info("loading tokenizer...")
+    tokenizer = load_tokenizer(
+        cfg.base_model_config,
+        cfg.tokenizer_type,
+        cfg
+    )
+
+    if check_not_in(["inference", "shard", "merge_lora"], kwargs):  # don't need to load dataset for these
+        train_dataset, eval_dataset = load_prepare_datasets(
+            tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
+        )
+
+    if prepare_ds_only:
+        logging.info("Finished preparing dataset. Exiting...")
+        return
+
     # Load the model and tokenizer
-    logging.info("loading model, tokenizer, and peft_config...")
-    model, tokenizer, peft_config = load_model(
+    logging.info("loading model and peft_config...")
+    model, peft_config = load_model(
         cfg.base_model,
         cfg.base_model_config,
         cfg.model_type,
-        cfg.tokenizer_type,
+        tokenizer,
         cfg,
         adapter=cfg.adapter,
         inference=("inference" in kwargs),
@@ -192,10 +213,6 @@ def train(
         model.save_pretrained(cfg.output_dir)
         return
 
-    train_dataset, eval_dataset = load_prepare_datasets(
-        tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
-    )
-
     if cfg.debug:
         logging.info("check_dataset_labels...")
         check_dataset_labels(
@@ -205,10 +222,6 @@ def train(
             tokenizer,
         )
 
-    if prepare_ds_only:
-        logging.info("Finished preparing dataset. Exiting...")
-        return
-
     trainer = setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer)
 
     model.config.use_cache = False

src/axolotl/utils/models.py

@@ -7,7 +7,6 @@ from typing import Optional, Tuple, TYPE_CHECKING
 import bitsandbytes as bnb
 import torch
 import transformers
-from torch import nn
 from transformers import (
     AutoModelForCausalLM,
     AutoTokenizer,
@@ -34,20 +33,56 @@ if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
 
 
+def load_tokenizer(
+    base_model_config,
+    tokenizer_type,
+    cfg,
+):
+    if tokenizer_type:
+        tokenizer = getattr(transformers, tokenizer_type).from_pretrained(
+            base_model_config,
+            trust_remote_code=cfg.trust_remote_code or False,
+        )
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(
+            base_model_config,
+            trust_remote_code=cfg.trust_remote_code or False,
+        )
+
+    logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
+    logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
+    logging.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
+    logging.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
+
+    if tokenizer.__class__.__name__ in ["LlamaTokenizer", "LlamaTokenizerFast"]:
+        tokenizer.pad_token = LLAMA_DEFAULT_PAD_TOKEN
+
+    if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
+        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+    if cfg.special_tokens:
+        for k, v in cfg.special_tokens.items():
+            tokenizer.add_special_tokens({k: v})
+    if cfg.tokens:
+        tokenizer.add_tokens(list(cfg.tokens))
+
+    return tokenizer
+
+
 def load_model(
     base_model,
     base_model_config,
     model_type,
-    tokenizer_type,
+    tokenizer,
     cfg,
     adapter="lora",
     inference=False,
 ):
-    # type: (str, str, str, str, AttrDefault, Optional[str], bool) -> Tuple[PreTrainedModel, PreTrainedTokenizer, Optional[PeftConfig]]
+    # type: (str, str, str, str, AttrDefault, Optional[str], bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
 
     # TODO refactor as a kwarg
     load_in_8bit = cfg.load_in_8bit
-    tokenizer = None
     is_llama_derived_model = "llama" in base_model or (
         cfg.model_type and "llama" in cfg.model_type.lower()
     )
@@ -122,7 +157,7 @@ def load_model(
                     model_path = str(cache_model_path)
             except:
                 model_path = cfg.base_model
-            model, tokenizer = load_llama_model_4bit_low_ram(
+            model, _ = load_llama_model_4bit_low_ram(
                 base_model_config if base_model_config else base_model,
                 model_path,
                 device_map=cfg.device_map,
@@ -207,42 +242,6 @@ def load_model(
             **model_kwargs,
         )
 
-    if not tokenizer:
-        try:
-            if is_llama_derived_model and "LlamaTokenizer" in globals():
-                tokenizer = LlamaTokenizer.from_pretrained(
-                    base_model_config,
-                    trust_remote_code=True if cfg.trust_remote_code is True else False,
-                )
-            else:
-                tokenizer = getattr(transformers, tokenizer_type).from_pretrained(
-                    base_model_config,
-                    trust_remote_code=True if cfg.trust_remote_code is True else False,
-                )
-        except:
-            tokenizer = AutoTokenizer.from_pretrained(
-                base_model_config,
-                trust_remote_code=True if cfg.trust_remote_code is True else False,
-            )
-
-    logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
-    logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
-    logging.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
-    logging.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
-
-    if tokenizer.__class__.__name__ in ["LlamaTokenizer", "LlamaTokenizerFast"]:
-        tokenizer.pad_token = LLAMA_DEFAULT_PAD_TOKEN
-
-    if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
-        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
-        os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-    if cfg.special_tokens:
-        for k, v in cfg.special_tokens.items():
-            tokenizer.add_special_tokens({k: v})
-    if cfg.tokens:
-        tokenizer.add_tokens(list(cfg.tokens))
-
     embeddings_len = math.ceil(len(tokenizer) / 32) * 32
     model.resize_token_embeddings(embeddings_len)
 
@@ -291,7 +290,7 @@ def load_model(
     model.config.use_cache = False
 
     # TODO resume_from_checkpoint handling
-    return model, tokenizer, lora_config
+    return model, lora_config
 
 
 def load_adapter(model, cfg, adapter):