Remove deprecations for 0.26 release

tests/test_dataset_formatting.py

@@ -19,7 +19,7 @@
 from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
 
 from trl.extras.dataset_formatting import get_formatting_func_from_dataset
-from trl.models.utils import ChatMlSpecialTokens, clone_chat_template, setup_chat_format
+from trl.models.utils import clone_chat_template
 
 from .testing_utils import TrlTestCase
 
@@ -118,47 +118,6 @@ def test_get_formatting_func_from_dataset_with_unknown_format(self):
         assert formatting_func is None
 
 
-@pytest.mark.filterwarnings("ignore::FutureWarning")
-class TestSetupChatFormat(TrlTestCase):
-    def setup_method(self):
-        self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
-        self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
-        # remove built-in chat_template to simulate a model having no chat_template
-        self.tokenizer.chat_template = None
-
-    def test_setup_chat_format(self):
-        modified_model, modified_tokenizer = setup_chat_format(
-            self.model, self.tokenizer, format="chatml", resize_to_multiple_of=123
-        )
-
-        _chatml = ChatMlSpecialTokens()
-        # Check if special tokens are correctly set
-        assert modified_tokenizer.eos_token == "<|im_end|>"
-        assert modified_tokenizer.pad_token == "<|im_end|>"
-        assert modified_tokenizer.bos_token == "<|im_start|>"
-        assert modified_tokenizer.eos_token == _chatml.eos_token
-        assert modified_tokenizer.pad_token == _chatml.pad_token
-        assert modified_tokenizer.bos_token == _chatml.bos_token
-        assert (modified_model.vocab_size % 123) == 0
-
-    def test_example_with_setup_model(self):
-        modified_model, modified_tokenizer = setup_chat_format(
-            self.model,
-            self.tokenizer,
-        )
-        messages = [
-            {"role": "system", "content": "You are helpful"},
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi, how can I help you?"},
-        ]
-        prompt = modified_tokenizer.apply_chat_template(messages, tokenize=False)
-
-        assert (
-            prompt
-            == "<|im_start|>system\nYou are helpful<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi, how can I help you?<|im_end|>\n"
-        )
-
-
 class TestCloneChatTemplate(TrlTestCase):
     def test_clone(self):
         # This tokenizer doesn't have a chat_template by default

trl/__init__.py

@@ -49,7 +49,6 @@
         "PreTrainedModelWrapper",
         "clone_chat_template",
         "create_reference_model",
-        "setup_chat_format",
     ],
     "trainer": [
         "AllTrueJudge",
@@ -129,7 +128,6 @@
         PreTrainedModelWrapper,
         clone_chat_template,
         create_reference_model,
-        setup_chat_format,
     )
     from .scripts import DatasetMixtureConfig, ScriptArguments, TrlParser, get_dataset, init_zero_verbose
     from .trainer import (

trl/models/__init__.py

@@ -28,7 +28,6 @@
         "prepare_fsdp",
         "prepare_model_for_kbit_training",
         "prepare_peft_model",
-        "setup_chat_format",
         "unwrap_model_for_generation",
     ],
 }
@@ -45,7 +44,6 @@
         prepare_fsdp,
         prepare_model_for_kbit_training,
         prepare_peft_model,
-        setup_chat_format,
         unwrap_model_for_generation,
     )
 else:

trl/models/utils.py

@@ -14,12 +14,11 @@
 
 import inspect
 import itertools
-import warnings
 from collections.abc import Callable
 from contextlib import contextmanager
 from copy import deepcopy
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Literal
+from typing import TYPE_CHECKING, Any
 
 import torch
 import torch.nn as nn
@@ -85,82 +84,6 @@ def chat_template(self):
 FORMAT_MAPPING = {"chatml": ChatMlSpecialTokens}
 
 
-def setup_chat_format(
-    model: PreTrainedModel,
-    tokenizer: PreTrainedTokenizer,
-    format: Literal["chatml"] | None = "chatml",
-    resize_to_multiple_of: int | None = None,
-) -> tuple[PreTrainedModel, PreTrainedTokenizer]:
-    # docstyle-ignore
-    """
-    Setup chat format by adding special tokens to the tokenizer, setting the correct format, and extending the
-    embedding layer of the model based on the new special tokens.
-
-    > [!WARNING]
-    > This function is deprecated and will be removed in version 0.26.0. Please use [`clone_chat_template`] instead.
-
-    If the model already has a chat template, this will throw an error. If you want to overwrite it, please set
-    `tokenizer.chat_template` to `None`.
-
-    Args:
-        model ([`~transformers.PreTrainedModel`]): The model to be modified.
-        tokenizer ([`~transformers.PreTrainedTokenizer`]): The tokenizer to be modified.
-        format (`Literal["chatml"] | None`): The format to be set. Defaults to "chatml".
-        resize_to_multiple_of (`int` or `None`): Number to resize the embedding layer to. Defaults to None.
-
-    Returns:
-        model ([`~transformers.PreTrainedModel`]):
-            The modified model.
-        tokenizer ([`~transformers.PreTrainedTokenizer`]):
-            The modified tokenizer.
-    """
-    warnings.warn(
-        "The `setup_chat_format` function is deprecated and will be removed in version 0.26.0. Please use "
-        "`clone_chat_template` instead.",
-        FutureWarning,
-    )
-    # check if model already had a chat template
-    if tokenizer.chat_template is not None:
-        raise ValueError(
-            "Chat template is already added to the tokenizer. If you want to overwrite it, please set it to None"
-        )
-
-    # check if format available and retrieve
-    if format not in FORMAT_MAPPING:
-        raise ValueError(f"Format {format} not available. Please use one of {FORMAT_MAPPING.keys()}")
-
-    chat_format = FORMAT_MAPPING[format]()
-
-    # set special tokens and them
-    tokenizer.eos_token = chat_format.eos_token
-    tokenizer.pad_token = chat_format.pad_token
-    tokenizer.bos_token = chat_format.bos_token
-    tokenizer.add_special_tokens({"additional_special_tokens": [chat_format.bos_token, chat_format.eos_token]})
-    # set chat format for tokenizer
-    tokenizer.chat_template = chat_format.chat_template
-
-    # resize embedding layer to a multiple of 64, https://x.com/karpathy/status/1621578354024677377
-    model.resize_token_embeddings(
-        # After studying many tokenizers, we found that len(tokenizer.vocab) is the most reliable way to get the vocab
-        # size. Avoid using tokenizer.vocab_size or tokenizer.vocab_size + len(tokenizer.added_tokens_encoder),
-        # as handling of special and added tokens varies across tokenizers.
-        new_num_tokens=len(tokenizer.vocab),
-        pad_to_multiple_of=resize_to_multiple_of if resize_to_multiple_of is not None else None,
-    )
-    # Update the model config to use the new eos & bos tokens
-    if getattr(model, "config", None) is not None:
-        model.config.pad_token_id = tokenizer.pad_token_id
-        model.config.bos_token_id = tokenizer.bos_token_id
-        model.config.eos_token_id = tokenizer.eos_token_id
-    # Update the generation config to use the new eos & bos token
-    if getattr(model, "generation_config", None) is not None:
-        model.generation_config.bos_token_id = tokenizer.bos_token_id
-        model.generation_config.eos_token_id = tokenizer.eos_token_id
-        model.generation_config.pad_token_id = tokenizer.pad_token_id
-
-    return model, tokenizer
-
-
 def clone_chat_template(
     model: PreTrainedModel,
     tokenizer: PreTrainedTokenizer,

trl/trainer/dpo_config.py

@@ -222,16 +222,6 @@ class DPOConfig(TrainingArguments):
         generate_during_eval (`bool`, *optional*, defaults to `False`):
             Whether to generate and log completions from both the model and the reference model to W&B or Comet during
             evaluation.
-
-        > Deprecated parameters
-
-        padding_value:
-
-            <Deprecated version="0.24.0">
-
-            This parameter is deprecated and will be removed in version 0.26.0. Use `pad_token` (`str`) instead.
-
-            </Deprecated>
     """
 
     _VALID_DICT_FIELDS = TrainingArguments._VALID_DICT_FIELDS + ["model_init_kwargs", "ref_model_init_kwargs"]
@@ -508,12 +498,6 @@ class DPOConfig(TrainingArguments):
         },
     )
 
-    # Deprecated arguments
-    padding_value: int | None = field(
-        default=None,
-        metadata={"help": "Deprecated, use `pad_token` (str) instead."},
-    )
-
     def __post_init__(self):
         self.bf16 = not (self.fp16) if self.bf16 is None else self.bf16
         self.f_divergence_type = FDivergenceType(self.f_divergence_type)

trl/trainer/dpo_trainer.py

@@ -15,7 +15,6 @@
 import inspect
 import random
 import textwrap
-import warnings
 from collections import defaultdict
 from collections.abc import Callable
 from contextlib import contextmanager, nullcontext
@@ -342,21 +341,14 @@ def __init__(
 
         # Get the pad token: if not provided, use the one from the processing class or the eos token
         # if the processing class does not have a pad token.
-        if args.padding_value is not None:  # deprecated, will be removed in 0.26.0.
-            warnings.warn(
-                "The `padding_value` argument is deprecated and will be removed in version 0.26.0. Please use "
-                "`pad_token` (str) instead."
+        pad_token = args.pad_token or tokenizer.pad_token or tokenizer.eos_token
+        self.pad_token_id = tokenizer.convert_tokens_to_ids(pad_token)
+        if self.pad_token_id is None:
+            raise ValueError(
+                f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given "
+                f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists "
+                "in the vocabulary before using it as a padding token."
             )
-            self.pad_token_id = args.padding_value
-        else:
-            pad_token = args.pad_token or tokenizer.pad_token or tokenizer.eos_token
-            self.pad_token_id = tokenizer.convert_tokens_to_ids(pad_token)
-            if self.pad_token_id is None:
-                raise ValueError(
-                    f"The specified `pad_token` ('{pad_token}') is not found in the vocabulary of the given "
-                    f"`processing_class` ({processing_class.__class__.__name__}). Ensure that the `pad_token` exists "
-                    "in the vocabulary before using it as a padding token."
-                )
 
         # PEFT configuration and model wrapping
         model = self._prepare_peft_model(model, ref_model, peft_config, args)
@@ -558,22 +550,6 @@ def __init__(
         if "bco_pair" in self.loss_type:
             self.running = RunningMoments(self.accelerator)
 
-    @property
-    def padding_value(self):
-        warnings.warn(
-            "The `padding_value` property is deprecated and will be removed in version 0.26.0. Please use "
-            "`pad_token_id` instead.",
-        )
-        return self.pad_token_id
-
-    @padding_value.setter
-    def padding_value(self, value):
-        warnings.warn(
-            "The `padding_value` property is deprecated and will be removed in version 0.26.0. Please use "
-            "`pad_token_id` instead.",
-        )
-        self.pad_token_id = value
-
     def _prepare_peft_model(
         self, model: PreTrainedModel, ref_model: PreTrainedModel, peft_config: Any, args: DPOConfig
     ) -> PreTrainedModel: