update tests

Author: VipulMascarenhas

tests/unitary/with_extras/aqua/test_data/finetuning/ft_config.json

@@ -1,28 +1,13 @@
 {
   "configuration": {
-    "adapter": "lora",
-    "bf16": true,
-    "flash_attention": true,
-    "fp16": false,
-    "gradient_accumulation_steps": 1,
-    "gradient_checkpointing": true,
     "learning_rate": 0.0002,
-    "logging_steps": 1,
     "lora_alpha": 16,
     "lora_dropout": 0.05,
     "lora_r": 32,
     "lora_target_linear": true,
-    "lora_target_modules": [
-      "q_proj",
-      "k_proj"
-    ],
-    "lr_scheduler": "cosine",
     "micro_batch_size": 1,
-    "optimizer": "adamw_torch",
     "pad_to_sequence_len": true,
-    "sample_packing": true,
     "sequence_len": 2048,
-    "tf32": false,
     "val_set_size": 0.1
   },
   "shape": {

tests/unitary/with_extras/aqua/test_finetuning.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*--
 
-# Copyright (c) 2024 Oracle and/or its affiliates.
+# Copyright (c) 2024, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import os
@@ -11,7 +11,6 @@
 from unittest import TestCase
 from unittest.mock import MagicMock, PropertyMock
 from mock import patch
-from dataclasses import asdict
 from importlib import reload
 
 import ads.aqua
@@ -147,7 +146,7 @@ def test_create_fine_tuning(
 
         aqua_ft_summary = self.app.create(**create_aqua_ft_details)
 
-        assert asdict(aqua_ft_summary) == {
+        assert aqua_ft_summary.to_dict() == {
             "console_url": f"https://cloud.oracle.com/data-science/models/{ft_model.id}?region={self.app.region}",
             "experiment": {
                 "id": f"{mock_mvs_create.return_value[0]}",
@@ -267,15 +266,14 @@ def test_get_finetuning_default_params(self):
         params_dict = {
             "params": {
                 "batch_size": 1,
+                "val_set_size": 0.1,
                 "sequence_len": 2048,
-                "sample_packing": True,
                 "pad_to_sequence_len": True,
                 "learning_rate": 0.0002,
                 "lora_r": 32,
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "lora_target_modules": ["q_proj", "k_proj"],
             }
         }
         config_json = os.path.join(self.curr_dir, "test_data/finetuning/ft_config.json")
@@ -308,16 +306,24 @@ def test_get_finetuning_default_params(self):
                 },
                 True,
             ),
+            (
+                {"optimizer": "adamw_torch"},
+                False,
+            ),
             (
                 {
-                    "micro_batch_size": 1,
-                    "max_sequence_len": 2048,
-                    "flash_attention": True,
-                    "pad_to_sequence_len": True,
-                    "lr_scheduler": "cosine",
+                    "epochs": [2],
                 },
                 False,
             ),
+            (
+                {
+                    "epochs": 2,
+                    "load_best_model_at_end": True,
+                    "metric_for_best_model": "accuracy",
+                },
+                True,
+            ),
         ]
     )
     def test_validate_finetuning_params(self, params, is_valid):