Update cal_lr.py

tests/cal_lr.py

@@ -22,8 +22,9 @@ BASE_BS = 4_000_000
 def calculate_lr(
     model_name_or_path: str,
     dataset: str,
-    cutoff_len: int,
-    batch_size: int
+    cutoff_len: int, # i.e. maximum input length during training
+    batch_size: int, # total batch size, namely (batch size * gradient accumulation * world size)
+    is_mistral: bool # mistral model uses a smaller learning rate
 ):
     model_args, data_args, training_args, finetuning_args, _ = get_train_args(dict(
         stage="sft",
@@ -54,6 +55,7 @@ def calculate_lr(
     valid_ratio = valid_tokens / total_tokens
     batch_valid_len = batch_max_len * valid_ratio
     lr = BASE_LR * math.sqrt(batch_valid_len / BASE_BS)
+    lr = lr / 6.0 if is_mistral else lr
     print("Optimal learning rate is {:.2e} for valid ratio% {:.2f} and effective batch size {:.2f}".format(
         lr, valid_ratio * 100, batch_valid_len
     ))