FIX: CPT should not be tested with sequence classification (#2507)

PR #2481 added sequence classification tests to PEFT. The test matrix
included CPT. However, CPT only supports the task type CAUSAL_LM. These
tests still passed but now started failing with:

> AttributeError: object has no attribute 'prepare_inputs_for_generation'

This is probably a change in transformers but the since causal LM was
never meant to work, the actual fix is to remove CPT from the seq cls
test matrix.

Since CPT automatically changes the task type to CAUSAL_LM, this mistake
can be hard to spot. Therefore, this PR also adds a warning if users
pass the wrong task type. In the future, this will raise an error.

Author: BenjaminBossan

src/peft/tuners/cpt/config.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
 from dataclasses import dataclass, field
 from typing import Literal, Optional
 
 from peft.config import PromptLearningConfig
-from peft.utils import PeftType
+from peft.utils import PeftType, TaskType
 
 
 @dataclass
@@ -79,7 +80,14 @@ def __post_init__(self):
         self.num_attention_heads = None  # Number of attention heads (if applicable).
         self.num_transformer_submodules = 1  # Number of transformer submodules used.
         self.peft_type = PeftType.CPT  # Specifies that the PEFT type is CPT.
-        self.task_type = "CAUSAL_LM"  # Ensures task type is causal language modeling.
+        if self.task_type != TaskType.CAUSAL_LM:
+            # TODO: adjust this to raise an error with PEFT v0.18.0
+            warnings.warn(
+                f"{self.__class__.__name__} only supports task_type = {TaskType.CAUSAL_LM.value}, "
+                "setting it automatically. This will raise an error starting from PEFT v0.18.0.",
+                FutureWarning,
+            )
+            self.task_type = TaskType.CAUSAL_LM  # Ensures task type is causal language modeling.
 
         if self.cpt_token_ids is None:
             self.cpt_token_ids = [0]

tests/test_cpt.py

@@ -27,7 +27,7 @@
     TrainingArguments,
 )
 
-from peft import CPTConfig, get_peft_model
+from peft import CPTConfig, TaskType, get_peft_model
 
 
 TEMPLATE = {"input": "input: {}", "intra_seperator": " ", "output": "output: {}", "inter_seperator": "\n"}
@@ -227,6 +227,14 @@ def test_model_initialization_random(global_tokenizer, config_random):
     assert model is not None, "PEFT model initialization failed"
 
 
+def test_model_initialization_wrong_task_type_warns():
+    # TODO: adjust this test to check for an error with PEFT v0.18.0
+    msg = "CPTConfig only supports task_type = CAUSAL_LM, setting it automatically"
+    with pytest.warns(FutureWarning, match=msg):
+        config = CPTConfig(task_type=TaskType.SEQ_CLS)
+    assert config.task_type == TaskType.CAUSAL_LM
+
+
 def test_model_training_random(sst_data, global_tokenizer, collator, config_random):
     """Perform a short training run to verify the model and data integration."""
 

tests/test_seq_classifier.py

@@ -19,7 +19,6 @@
     AdaLoraConfig,
     BOFTConfig,
     BoneConfig,
-    CPTConfig,
     FourierFTConfig,
     HRAConfig,
     IA3Config,
@@ -67,15 +66,6 @@
             "r": 2,
         },
     ),
-    (
-        CPTConfig,
-        {
-            "task_type": "SEQ_CLS",
-            "cpt_token_ids": [0, 1, 2, 3, 4, 5, 6, 7],  # Example token IDs for testing
-            "cpt_mask": [1, 1, 1, 1, 1, 1, 1, 1],
-            "cpt_tokens_type_mask": [1, 2, 2, 2, 3, 3, 4, 4],
-        },
-    ),
     (
         FourierFTConfig,
         {