add support for gradient accumulation steps

scripts/finetune.py

@@ -149,8 +149,10 @@ def train(
             else:
                 cfg[k] = kwargs[k]
 
+    validate_config(cfg)
+
     # setup some derived config / hyperparams
-    cfg.gradient_accumulation_steps = cfg.batch_size // cfg.micro_batch_size
+    cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (cfg.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))
     choose_device(cfg)
@@ -168,8 +170,6 @@ def train(
             cfg.fp16 = True
         cfg.bf16 = False
 
-    validate_config(cfg)
-
     # load the tokenizer first
     logging.info("loading tokenizer...")
     tokenizer = load_tokenizer(cfg.base_model_config, cfg.tokenizer_type, cfg)

src/axolotl/utils/validation.py

@@ -4,6 +4,10 @@ import logging
 
 
 def validate_config(cfg):
+    if cfg.gradient_accumulation_steps and cfg.batch_size:
+        raise ValueError(
+            "please set only one of gradient_accumulation_steps or batch_size"
+        )
     if cfg.load_4bit:
         raise ValueError(
             "cfg.load_4bit parameter has been deprecated and replaced by cfg.gptq"

tests/test_validation.py

@@ -117,3 +117,30 @@ class ValidationTest(unittest.TestCase):
             }
         )
         validate_config(cfg)
+
+    def test_gradient_accumulations_or_batch_size(self):
+        cfg = DictDefault(
+            {
+                "gradient_accumulation_steps": 1,
+                "batch_size": 1,
+            }
+        )
+
+        with pytest.raises(ValueError, match=r".*gradient_accumulation_steps or batch_size.*"):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "batch_size": 1,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "gradient_accumulation_steps": 1,
+            }
+        )
+
+        validate_config(cfg)