improve how we setup eval/save strategies and steps (#547)

* setup save end eval strategies to be consistent with trainer logic

* add comments

* better eval handling

src/axolotl/utils/trainer.py

@@ -567,21 +567,33 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
             "sample_packing_efficiency"
         ] = cfg.sample_packing_eff_est
 
-    if cfg.val_set_size == 0:
+    if cfg.eval_steps and cfg.evaluation_strategy:
+        # assume if the user set both, they know what they're doing
+        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
+        training_arguments_kwargs["eval_steps"] = cfg.eval_steps
+    elif cfg.val_set_size == 0:
+        # no eval set, so don't eval
         training_arguments_kwargs["evaluation_strategy"] = "no"
+    elif cfg.evaluation_strategy and cfg.evaluation_strategy in ["epoch", "no"]:
+        # if explicitly set for epoch, just set, and eval steps don't matter
+        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
     elif cfg.eval_steps:
+        # steps isn't used w/ epochs
         training_arguments_kwargs["evaluation_strategy"] = "steps"
         training_arguments_kwargs["eval_steps"] = cfg.eval_steps
     else:
-        # we have an eval set, but no steps defined, use epoch
+        # we have an eval set, but no steps defined, default to use epoch
         training_arguments_kwargs["evaluation_strategy"] = "epoch"
 
-    if cfg.save_strategy:
+    if cfg.save_steps:
+        # save_steps implies save_strategy of steps
+        training_arguments_kwargs["save_strategy"] = "steps"
+        training_arguments_kwargs["save_steps"] = cfg.save_steps
+    elif cfg.save_strategy:
         training_arguments_kwargs["save_strategy"] = cfg.save_strategy
     else:
-        training_arguments_kwargs["save_strategy"] = (
-            "steps" if cfg.save_steps else "epoch"
-        )
+        # default to saving each epoch if not defined
+        training_arguments_kwargs["save_strategy"] = "epoch"
 
     if cfg.do_bench_eval:
         training_arguments_kwargs["do_bench_eval"] = cfg.do_bench_eval