add missing peft config test

Signed-off-by: Yu Chin Fabian Lim <[email protected]>

tests/acceleration/test_acceleration_framework.py

@@ -24,7 +24,7 @@
 
 # First Party
 from tests.helpers import causal_lm_train_kwargs
-from tests.test_sft_trainer import BASE_LORA_KWARGS
+from tests.test_sft_trainer import BASE_FT_KWARGS, BASE_LORA_KWARGS
 
 # Local
 from .spying_utils import create_mock_plugin_class_and_spy
@@ -55,8 +55,10 @@
 
     if is_fms_accelerate_available(plugins="peft"):
         # Third Party
-        from fms_acceleration_peft import AutoGPTQAccelerationPlugin
-        from fms_acceleration_peft import BNBAccelerationPlugin
+        from fms_acceleration_peft import (
+            AutoGPTQAccelerationPlugin,
+            BNBAccelerationPlugin,
+        )
 
     if is_fms_accelerate_available(plugins="foak"):
         # Third Party
@@ -242,16 +244,73 @@ def test_framework_raises_if_used_with_missing_package():
                     quantized_lora_config=quantized_lora_config,
                 )
 
+
+invalid_kwargs_map = [
+    (
+        {
+            **BASE_LORA_KWARGS,
+            "model_name_or_path": "TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ",
+        },
+        AssertionError,
+        "need to run in fp16 mixed precision or load model in fp16",
+    ),
+    (
+        {
+            **BASE_FT_KWARGS,
+            "model_name_or_path": "TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ",
+            "torch_dtype": torch.float16,
+        },
+        AssertionError,
+        "need peft_config to install PEFT adapters",
+    ),
+]
+
+
+@pytest.mark.skipif(
+    not is_fms_accelerate_available(plugins="peft"),
+    reason="Only runs if fms-accelerate is installed along with accelerated-peft plugin",
+)
+@pytest.mark.parametrize(
+    "train_kwargs,exception",
+    invalid_kwargs_map,
+    ids=["triton_v2 requires fp16", "accelerated peft requires peft config"],
+)
+def test_framework_raises_due_to_invalid_arguments(
+    bad_train_kwargs, exception, exception_msg
+):
+    """Ensure that invalid arguments will be checked by the activated framework
+    plugin.
+    """
+    with tempfile.TemporaryDirectory() as tempdir:
+        TRAIN_KWARGS = {
+            **bad_train_kwargs,
+            "output_dir": tempdir,
+        }
+        model_args, data_args, training_args, tune_config = causal_lm_train_kwargs(
+            TRAIN_KWARGS
+        )
+        quantized_lora_config = QuantizedLoraConfig(auto_gptq=AutoGPTQLoraConfig())
+
+        # 1. activate the accelerated peft plugin
+        # 2. demonstrate that the invalid arguments will be checked
+        with pytest.raises(exception, match=exception_msg):
+            sft_trainer.train(
+                model_args,
+                data_args,
+                training_args,
+                tune_config,
+                quantized_lora_config=quantized_lora_config,
+            )
+
+
 if is_fms_accelerate_available(plugins="peft"):
     acceleration_configs_map = [
         (
             QuantizedLoraConfig(bnb_qlora=BNBQLoraConfig()),
             "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
             (
                 "peft.quantization.bitsandbytes",
-                create_mock_plugin_class_and_spy(
-                    "PluginMock", BNBAccelerationPlugin
-                )
+                create_mock_plugin_class_and_spy("PluginMock", BNBAccelerationPlugin),
             ),
         ),
         (
@@ -261,19 +320,20 @@ def test_framework_raises_if_used_with_missing_package():
                 "peft.quantization.auto_gptq",
                 create_mock_plugin_class_and_spy(
                     "PluginMock", AutoGPTQAccelerationPlugin
-                )
+                ),
             ),
         ),
     ]
 
+
 @pytest.mark.skipif(
     not is_fms_accelerate_available(plugins="peft"),
     reason="Only runs if fms-accelerate is installed along with accelerated-peft plugin",
 )
 @pytest.mark.parametrize(
-    "quantized_lora_config,model_name_or_path,mock_and_spy", 
+    "quantized_lora_config,model_name_or_path,mock_and_spy",
     acceleration_configs_map,
-    ids=['bitsandbytes', 'auto_gptq'],
+    ids=["bitsandbytes", "auto_gptq"],
 )
 def test_framework_intialized_properly_peft(
     quantized_lora_config, model_name_or_path, mock_and_spy
@@ -320,6 +380,7 @@ def test_framework_intialized_properly_peft(
         assert spy["augmentation_calls"] == 1
         assert spy["get_ready_for_train_calls"] == 1
 
+
 @pytest.mark.skipif(
     not is_fms_accelerate_available(plugins=["peft", "foak"]),
     reason=(

tests/helpers.py

@@ -43,5 +43,5 @@ def causal_lm_train_kwargs(train_kwargs):
         training_args,
         lora_config
         if train_kwargs.get("peft_method") == "lora"
-        else prompt_tuning_config,
+        else (None if train_kwargs.get("peft_method") == "" else prompt_tuning_config,),
     )

tuning/config/acceleration_configs/acceleration_framework_config.py

@@ -123,7 +123,7 @@ def from_dataclasses(*dataclasses: Type):
             for fi in fields(dc):
                 attr = getattr(dc, fi.name)
                 if attr is None:
-                    continue # skip the None attributes
+                    continue  # skip the None attributes
 
                 if not is_dataclass(attr):
                     raise ValueError(