Release 0.18.0.rc0

examples/cpt_finetuning/cpt_train_and_inference.ipynb

@@ -159,7 +159,7 @@
         "    TrainingArguments,\n",
         ")\n",
         "\n",
-        "from peft import CPTConfig, get_peft_model\n",
+        "from peft import CPTConfig, TaskType, get_peft_model\n",
         "\n",
         "\n",
         "MAX_INPUT_LENGTH = 1024\n",
@@ -559,6 +559,7 @@
         "\n",
         "# Initialize the CPT configuration\n",
         "config = CPTConfig(\n",
+        "            task_type=TaskType.CAUSAL_LM,\n",
         "            cpt_token_ids=context_ids,\n",
         "            cpt_mask=context_attention_mask,\n",
         "            cpt_tokens_type_mask=context_input_type_mask,\n",
@@ -761,7 +762,6 @@
         "    num_train_epochs=5,\n",
         "    fp16=True,\n",
         "    save_strategy='no',\n",
-        "    logging_dir=\"logs\",\n",
         "    report_to=\"none\"\n",
         ")\n",
         "\n",

setup.py

@@ -15,7 +15,7 @@
 from setuptools import find_packages, setup
 
 
-VERSION = "0.17.2.dev0"
+VERSION = "0.18.0.rc0"
 
 extras = {}
 extras["quality"] = [

src/peft/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "0.17.2.dev0"
+__version__ = "0.18.0.rc0"
 
 from .auto import (
     MODEL_TYPE_TO_PEFT_MODEL_MAPPING,

src/peft/tuners/cpt/config.py

@@ -12,7 +12,6 @@
 # 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
 
@@ -81,13 +80,7 @@ def __post_init__(self):
         self.num_transformer_submodules = 1  # Number of transformer submodules used.
         self.peft_type = PeftType.CPT  # Specifies that the PEFT type is CPT.
         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.
+            raise ValueError(f"{self.__class__.__name__} only supports task_type = {TaskType.CAUSAL_LM.value}.")
 
         if self.cpt_token_ids is None:
             self.cpt_token_ids = [0]

tests/test_config.py

@@ -26,6 +26,7 @@
     BOFTConfig,
     BoneConfig,
     C3AConfig,
+    CPTConfig,
     FourierFTConfig,
     HRAConfig,
     IA3Config,
@@ -134,6 +135,8 @@ def test_from_peft_type(self):
 
             if expected_cls == AdaLoraConfig:
                 mandatory_config_kwargs = {"total_step": 1}
+            elif expected_cls == CPTConfig:
+                mandatory_config_kwargs = {"task_type": TaskType.CAUSAL_LM}
 
             config = PeftConfig.from_peft_type(peft_type=peft_type, **mandatory_config_kwargs)
             assert type(config) is expected_cls

tests/test_cpt.py

@@ -55,6 +55,7 @@ def config_text():
         opt_projection_epsilon=0.2,
         opt_projection_format_epsilon=0.1,
         tokenizer_name_or_path=MODEL_NAME,
+        task_type=TaskType.CAUSAL_LM,
     )
     return config
 
@@ -68,6 +69,7 @@ def config_random():
         opt_projection_epsilon=0.2,
         opt_projection_format_epsilon=0.1,
         tokenizer_name_or_path=MODEL_NAME,
+        task_type=TaskType.CAUSAL_LM,
     )
     return config
 
@@ -227,12 +229,14 @@ 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_initialization_wrong_task_type_raises():
+    msg = "CPTConfig only supports task_type = CAUSAL_LM."
+    with pytest.raises(ValueError, match=msg):
+        CPTConfig(task_type=TaskType.SEQ_CLS)
+
+    msg = "CPTConfig only supports task_type = CAUSAL_LM."
+    with pytest.raises(ValueError, match=msg):
+        CPTConfig()
 
 
 def test_model_training_random(sst_data, global_tokenizer, collator, config_random):

tests/test_decoder_models.py

@@ -708,6 +708,7 @@ def process(samples):
             (
                 CPTConfig,
                 {
+                    "task_type": "CAUSAL_LM",
                     "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],

tests/test_vision_models.py

@@ -29,6 +29,7 @@
 )
 
 from peft import (
+    BOFTConfig,
     HRAConfig,
     LoHaConfig,
     LoKrConfig,
@@ -50,10 +51,12 @@
         r=1, oft_block_size=0, target_modules=["convolution"], modules_to_save=["classifier", "normalization"]
     ),
     "hra": HRAConfig(target_modules=["convolution"], modules_to_save=["classifier", "normalization"]),
-    # TODO: cannot use BOFT because some convolutional kernel dimensions are even (64) and others odd (147). There is no
-    # common denominator for the boft_block_size except 1, but using 1 results in an error in the fbd_cuda kernel:
+    # Cannot target multiple layers with BOFT because some convolutional kernel dimensions vary and there is no common
+    # denominator for the boft_block_size except 1, but using 1 results in an error in the fbd_cuda kernel:
     # > Error in forward_fast_block_diag_cuda_kernel: an illegal memory access was encountered
-    # "boft": BOFTConfig(target_modules=["convolution"], modules_to_save=["classifier", "normalization"], boft_block_size=2),
+    "boft": BOFTConfig(
+        target_modules=["0.layer.0.convolution"], modules_to_save=["classifier", "normalization"], boft_block_size=2
+    ),
 }