Remove post-collation truncation from SFT

trl/trainer/sft_trainer.py

@@ -51,7 +51,6 @@
     maybe_convert_to_chatml,
     pack_dataset,
     prepare_multimodal_messages,
-    truncate_dataset,
 )
 from ..models import get_act_offloading_ctx_manager
 from .base_trainer import _BaseTrainer
@@ -572,6 +571,8 @@ class SFTTrainer(_BaseTrainer):
             Function to use to form a batch from a list of elements of the processed `train_dataset` or `eval_dataset`.
             Will default to [`~trainer.sft_trainer.DataCollatorForLanguageModeling`] if the model is a language model
             and [`~trainer.sft_trainer.DataCollatorForVisionLanguageModeling`] if the model is a vision-language model.
+            Custom collators must truncate sequences before padding; the trainer does not apply post-collation
+            truncation.
         train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
             Dataset to use for training. This trainer supports both [language modeling](#language-modeling) type and
             [prompt-completion](#prompt-completion) type. The format of the samples can be either:
@@ -923,8 +924,7 @@ def __init__(
         if skip_prepare_dataset and self.padding_free and args.max_length is not None and not self._is_vision_dataset:
             raise ValueError(
                 "When `padding_free=True`, `max_length` must be enforced during dataset preparation or packing, not in "
-                "the collator. Disable `skip_prepare_dataset`, provide already packed/truncated inputs, or set "
-                "`max_length=None`."
+                "the collator. Provide already packed/truncated inputs, or set `max_length=None`."
             )
         if not skip_prepare_dataset:
             if self.completion_only_loss and formatting_func:
@@ -1188,7 +1188,7 @@ def tokenize_fn(example, processing_class, dataset_text_field, assistant_only_lo
                     **map_kwargs,
                 )
 
-            # Pack or truncate
+            # Pack
             if packing:
                 if args.max_length is None:
                     raise ValueError("When packing is enabled, `max_length` can't be `None`.")
@@ -1210,12 +1210,6 @@ def tokenize_fn(example, processing_class, dataset_text_field, assistant_only_lo
 
                 # Packing adds new column "seq_lengths" needed for document aware FlashAttention
                 dataset = pack_dataset(dataset, args.max_length, args.packing_strategy, map_kwargs)
-            elif args.max_length is not None:
-                if isinstance(dataset, Dataset):  # `IterableDataset.map` does not support `desc`
-                    map_kwargs["desc"] = f"Truncating {dataset_name} dataset"
-                dataset = truncate_dataset(
-                    dataset, args.max_length, truncation_mode=args.truncation_mode, map_kwargs=map_kwargs
-                )
             # For Liger kernel, ensure only the essential columns
             if args.use_liger_kernel:
                 collator_expected_keys = {"input_ids", "seq_lengths", "completion_mask", "assistant_masks"}