refactor to set eval_batch_size earlier if unset, so we can warn if mismatched

README.md

@@ -571,7 +571,7 @@ torch_compile_backend:  # Optional[str]
 # training hyperparameters
 gradient_accumulation_steps: 1
 micro_batch_size: 2
-eval_batch_size: 2
+eval_batch_size:
 num_epochs: 3
 warmup_steps: 100
 learning_rate: 0.00003

src/axolotl/utils/config.py

@@ -49,6 +49,8 @@ def normalize_config(cfg):
     cfg.batch_size = (
         cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
     )
+    if cfg.eval_batch_size is None:
+        cfg.eval_batch_size = cfg.micro_batch_size
     cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
     cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
     cfg.eval_table_size = cfg.eval_table_size or 0
@@ -157,6 +159,11 @@ def validate_config(cfg):
             "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
             "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
         )
+    if cfg.eval_batch_size != cfg.micro_batch_size:
+        LOG.warning(
+            "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
+        )
+
     if cfg.load_4bit:
         raise ValueError("cfg.load_4bit parameter has been deprecated")
 

src/axolotl/utils/trainer.py

@@ -668,9 +668,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         max_steps=total_num_steps if cfg.max_steps else -1,
         max_seq_length=cfg.sequence_len,
         per_device_train_batch_size=cfg.micro_batch_size,
-        per_device_eval_batch_size=cfg.eval_batch_size
-        if cfg.eval_batch_size is not None
-        else cfg.micro_batch_size,
+        per_device_eval_batch_size=cfg.eval_batch_size,
         gradient_accumulation_steps=cfg.gradient_accumulation_steps,
         eval_accumulation_steps=cfg.gradient_accumulation_steps,
         num_train_epochs=cfg.num_epochs,