add tokenizer_save_jinja_files to keep legacy behavior of including chat template in tokenizer_config.json (#3093)

* add tokenizer_save_jinja_files to keep legacy behavior of including chat template in tokenizer_config.json

* fix test import

src/axolotl/cli/merge_lora.py

@@ -43,7 +43,10 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
             safe_serialization=safe_serialization,
             progressbar=True,
         )
-        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+        tokenizer.save_pretrained(
+            str(Path(cfg.output_dir) / "merged"),
+            save_jinja_files=cfg.tokenizer_save_jinja_files,
+        )
 
         if processor:
             processor.save_pretrained(str(Path(cfg.output_dir) / "merged"))

src/axolotl/cli/quantize.py

@@ -84,5 +84,6 @@ def do_quantize(
         str(Path(output_dir) / "quantized"),
         safe_serialization=False,
         progressbar=True,
+        save_jinja_files=cfg.tokenizer_save_jinja_files,
     )
     LOG.info(f"Quantized model saved to: {str(Path(output_dir) / 'quantized')}...")

src/axolotl/core/builders/causal.py

@@ -404,6 +404,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             **trainer_kwargs,
         )
         trainer = self.hook_post_create_trainer(trainer)
+        # if the trainer has the `axolotl_cfg` property, set it
+        if hasattr(trainer, "axolotl_cfg"):
+            trainer.axolotl_cfg = self.cfg
         for callback in self.get_post_trainer_create_callbacks(trainer):
             trainer.add_callback(callback)
 

src/axolotl/core/trainers/base.py

@@ -42,6 +42,7 @@ from axolotl.core.trainers.utils import (
 )
 from axolotl.utils import get_not_null
 from axolotl.utils.bench import get_gpu_memory_usage
+from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.logging import get_logger
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
@@ -63,6 +64,15 @@ class AxolotlTrainer(
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
     tag_names = ["axolotl"]
+    _axolotl_cfg: DictDefault | None = None
+
+    @property
+    def axolotl_cfg(self):
+        return self._axolotl_cfg
+
+    @axolotl_cfg.setter
+    def axolotl_cfg(self, cfg):
+        self._axolotl_cfg = cfg
 
     def __init__(
         self,
@@ -657,6 +667,11 @@ class AxolotlTrainer(
                 LOG.info(
                     "Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`"
                 )
-                self.data_collator.tokenizer.save_pretrained(output_dir)
+                save_jinja_files = True
+                if self.axolotl_cfg:
+                    save_jinja_files = self.axolotl_cfg.tokenizer_save_jinja_files
+                self.data_collator.tokenizer.save_pretrained(
+                    output_dir, save_jinja_files=save_jinja_files
+                )
             # Good practice: save your training arguments together with the trained model
             torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

src/axolotl/train.py

@@ -416,7 +416,9 @@ def save_initial_configs(
 
     # Pre-save the tokenizer and model configs
     LOG.info(f"Pre-saving tokenizer to {cfg.output_dir}...")
-    tokenizer.save_pretrained(str(output_dir))
+    tokenizer.save_pretrained(
+        str(Path(cfg.output_dir)), save_jinja_files=cfg.tokenizer_save_jinja_files
+    )
     if hasattr(model, "config"):
         LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
         model.config.save_pretrained(str(output_dir))
@@ -592,6 +594,9 @@ def train(
 
     # Save the trained model and cleanup
     save_trained_model(cfg, trainer, model, safe_serialization)
+    tokenizer.save_pretrained(
+        str(Path(cfg.output_dir)), save_jinja_files=cfg.tokenizer_save_jinja_files
+    )
     create_model_card(cfg, trainer)
     if not cfg.use_ray:
         cleanup_distributed()

src/axolotl/utils/config/__init__.py

@@ -77,7 +77,7 @@ def resolve_dtype(cfg):
     if cfg.device == "mps":
         cfg.load_in_8bit = False
         cfg.tf32 = False
-        if cfg.bf16:
+        if cfg.bf16 and cfg.fp16 is not False:
             cfg.fp16 = True
         cfg.bf16 = False
     else:

src/axolotl/utils/schemas/model.py

@@ -59,6 +59,12 @@ class ModelInputConfig(BaseModel):
     processor_type: str | None = Field(
         default=None, json_schema_extra={"description": "transformers processor class"}
     )
+    tokenizer_save_jinja_files: bool | None = Field(
+        default=True,  # match the default behavior from transformers
+        json_schema_extra={
+            "description": "Whether to save jinja files for tokenizer, transformers default is True"
+        },
+    )
     trust_remote_code: bool | None = Field(
         default=None,
         json_schema_extra={"description": "Trust remote code for untrusted source"},

tests/e2e/test_tokenizer.py

@@ -0,0 +1,63 @@
+"""
+e2e test for saving the tokenizer
+"""
+
+from unittest.mock import patch
+
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import check_model_output_exists
+
+
+def test_tokenizer_no_save_jinja_files(temp_dir):
+    # pylint: disable=duplicate-code
+    cfg = DictDefault(
+        {
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
+            "tokenizer_type": "AutoTokenizer",
+            "sequence_len": 1024,
+            "load_in_8bit": True,
+            "adapter": "lora",
+            "lora_r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.05,
+            "lora_target_linear": True,
+            "val_set_size": 0.02,
+            "special_tokens": {
+                "pad_token": "<|endoftext|>",
+            },
+            "chat_template": "chatml",
+            "datasets": [
+                {
+                    "path": "mhenrichsen/alpaca_2k_test",
+                    "type": "alpaca",
+                },
+            ],
+            "num_epochs": 1,
+            "micro_batch_size": 2,
+            "gradient_accumulation_steps": 1,
+            "output_dir": temp_dir,
+            "learning_rate": 0.00001,
+            "optimizer": "adamw_torch_fused",
+            "lr_scheduler": "cosine",
+            "max_steps": 5,
+            "save_first_step": False,
+            "fp16": False,
+            "tokenizer_save_jinja_files": False,
+        }
+    )
+
+    cfg = validate_config(cfg)
+    normalize_config(cfg)
+    dataset_meta = load_datasets(cfg=cfg)
+
+    with patch("axolotl.train.execute_training"):
+        train(cfg=cfg, dataset_meta=dataset_meta)
+
+    check_model_output_exists(temp_dir, cfg)
+    with open(f"{temp_dir}/tokenizer_config.json", "r", encoding="utf-8") as f:
+        tokenizer_config = f.read()
+        assert "chat_template" in tokenizer_config