add unit tests

Signed-off-by: Sukriti-Sharma4 <[email protected]>

caikit_nlp/modules/text_generation/peft_config.py

@@ -268,5 +268,6 @@ def get_lora_config(tuning_type, tuning_config, base_model) -> LoraConfig:
     log.info("<NLP61012781I>", f"Parameters used: {config_kwargs}")
     config_params = _filter_params_for_prompt_config(tuning_config, config_kwargs)
     output_model_types = _get_output_types(tuning_config, base_model)
+    del config_params["output_model_types"]
     lora_config = LoraConfig(task_type=task_type, **config_params)
     return task_type, output_model_types, lora_config, tuning_type

tests/modules/text_generation/test_peft_config.py

@@ -2,14 +2,15 @@
 from unittest.mock import Mock
 
 # Third Party
-from peft import PromptTuningConfig
+from peft import LoraConfig, PromptTuningConfig
 import pytest
 
 # Local
-from caikit_nlp.data_model import TuningConfig
+from caikit_nlp.data_model import LoraTuningConfig, TuningConfig
 from caikit_nlp.modules.text_generation import TextGeneration
 from caikit_nlp.modules.text_generation.peft_config import (
     TuningType,
+    get_lora_config,
     get_peft_config,
     resolve_base_model,
 )
@@ -74,6 +75,46 @@ def test_get_peft_config(train_kwargs, dummy_model, request):
     assert peft_config.prompt_tuning_init_text == tuning_config.prompt_tuning_init_text
 
 
+@pytest.mark.parametrize(
+    "train_kwargs,dummy_model",
+    [
+        (
+            "seq2seq_lm_train_kwargs",
+            "seq2seq_lm_dummy_model",
+        ),
+        ("causal_lm_train_kwargs", "causal_lm_dummy_model"),
+    ],
+)
+def test_get_lora_config(train_kwargs, dummy_model, request):
+    # Fixtures can't be called directly or passed to mark parametrize;
+    # Currently, passing the fixture by name and retrieving it through
+    # the request is the 'right' way to do this.
+    train_kwargs = request.getfixturevalue(train_kwargs)
+    dummy_model = request.getfixturevalue(dummy_model)
+
+    # Define some sample values for testing
+    tuning_type = TuningType.LORA
+    tuning_config = LoraTuningConfig(r=8, lora_alpha=8, lora_dropout=0.0)
+    dummy_resource = train_kwargs["base_model"]
+
+    # Call the function being tested
+    task_type, output_model_types, lora_config, tuning_type = get_lora_config(
+        tuning_type, tuning_config, dummy_resource
+    )
+
+    # Add assertions to validate the behavior of the function
+    assert task_type == dummy_resource.TASK_TYPE
+    assert output_model_types == dummy_resource.PROMPT_OUTPUT_TYPES
+    assert tuning_type == TuningType.LORA
+
+    # Validation for type & important fields in the peft config
+    assert isinstance(lora_config, LoraConfig)
+    assert lora_config.task_type == dummy_resource.TASK_TYPE
+    assert lora_config.r == tuning_config.r
+    assert lora_config.lora_alpha == tuning_config.lora_alpha
+    assert lora_config.lora_dropout == tuning_config.lora_dropout
+
+
 def test_resolve_model_with_invalid_path_raises():
     """Test passing invalid path to resolve_model function raises"""