🩳 max_seq_length to max_length (#2895)

* `max_seq_length` to `max_length`

* remove in 0.20

commands/run_sft.sh

@@ -42,7 +42,7 @@ accelerate launch $EXTRA_ACCELERATE_ARGS \
     --output_dir $OUTPUT_DIR \
     --max_steps $MAX_STEPS \
     --per_device_train_batch_size $BATCH_SIZE \
-    --max_seq_length $SEQ_LEN \
+    --max_length $SEQ_LEN \
     $EXTRA_TRAINING_ARGS
 """
 

docs/source/reducing_memory_usage.md

@@ -44,7 +44,7 @@ training_args = DPOConfig(..., max_completion_length=...)
 </hfoption>
 <hfoption id="SFT">
 
-SFT truncation is applied to the input sequence via the `max_seq_length` parameter.
+SFT truncation is applied to the input sequence via the `max_length` parameter.
 
 <div class="flex justify-center">
     <img src="https://huggingface.co/datasets/trl-lib/documentation-images/resolve/main/truncation_input_ids.png" alt="Truncation input ids" width="600"/>
@@ -55,7 +55,7 @@ To set the truncation parameter, use the following code snippet:
 ```python
 from trl import SFTConfig
 
-training_args = SFTConfig(..., max_seq_length=...)
+training_args = SFTConfig(..., max_length=...)
 ```
 
 </hfoption>
@@ -85,7 +85,7 @@ Packing eliminates padding, preserves all sequence information, and allows for f
 ```python
 from trl import SFTConfig
 
-training_args = SFTConfig(..., packing=True, max_seq_length=512)
+training_args = SFTConfig(..., packing=True, max_length=512)
 ```
 
 <Tip warning={true}>

docs/source/sft_trainer.md

@@ -19,7 +19,7 @@ from trl import SFTConfig, SFTTrainer
 dataset = load_dataset("stanfordnlp/imdb", split="train")
 
 training_args = SFTConfig(
-    max_seq_length=512,
+    max_length=512,
     output_dir="/tmp",
 )
 trainer = SFTTrainer(
@@ -29,7 +29,7 @@ trainer = SFTTrainer(
 )
 trainer.train()
 ```
-Make sure to pass the correct value for `max_seq_length` as the default value will be set to `min(tokenizer.model_max_length, 1024)`.
+Make sure to pass the correct value for `max_length` as the default value will be set to `min(tokenizer.model_max_length, 1024)`.
 
 You can also construct a model outside of the trainer and pass it as follows:
 
@@ -550,12 +550,12 @@ import torch
 from trl import SFTConfig, SFTTrainer
 from unsloth import FastLanguageModel
 
-max_seq_length = 2048 # Supports automatic RoPE Scaling, so choose any number
+max_length = 2048 # Supports automatic RoPE Scaling, so choose any number
 
 # Load model
 model, tokenizer = FastLanguageModel.from_pretrained(
     model_name="unsloth/mistral-7b",
-    max_seq_length=max_seq_length,
+    max_seq_length=max_length,
     dtype=None,  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
     load_in_4bit=True,  # Use 4bit quantization to reduce memory usage. Can be False
     # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
@@ -581,7 +581,7 @@ model = FastLanguageModel.get_peft_model(
     random_state=3407,
 )
 
-training_args = SFTConfig(output_dir="./output", max_seq_length=max_seq_length)
+training_args = SFTConfig(output_dir="./output", max_length=max_length)
 
 trainer = SFTTrainer(
     model=model,
@@ -624,7 +624,7 @@ To learn more about Liger-Kernel, visit their [official repository](https://gith
 
 Pay attention to the following best practices when training a model with that trainer:
 
-- [`SFTTrainer`] always truncates by default the sequences to the `max_seq_length` argument of the [`SFTConfig`]. If none is passed, the trainer will retrieve that value from the tokenizer. Some tokenizers do not provide a default value, so there is a check to retrieve the minimum between 1024 and that value. Make sure to check it before training.
+- [`SFTTrainer`] always truncates by default the sequences to the `max_length` argument of the [`SFTConfig`]. If none is passed, the trainer will retrieve that value from the tokenizer. Some tokenizers do not provide a default value, so there is a check to retrieve the minimum between 1024 and that value. Make sure to check it before training.
 - For training adapters in 8bit, you might need to tweak the arguments of the `prepare_model_for_kbit_training` method from PEFT, hence we advise users to use `prepare_in_int8_kwargs` field, or create the `PeftModel` outside the [`SFTTrainer`] and pass it.
 - For a more memory-efficient training using adapters, you can load the base model in 8bit, for that simply add `load_in_8bit` argument when creating the [`SFTTrainer`], or create a base model in 8bit outside the trainer and pass it.
 - If you create a model outside the trainer, make sure to not pass to the trainer any additional keyword arguments that are relative to `from_pretrained()` method.

examples/research_projects/stack_llama_2/scripts/sft_llama2.py

@@ -185,7 +185,7 @@ def create_datasets(tokenizer, args, seed=None):
     train_dataset=train_dataset,
     eval_dataset=eval_dataset,
     peft_config=peft_config,
-    max_seq_length=None,
+    max_length=None,
     formatting_func=prepare_sample_text,
     processing_class=tokenizer,
     args=training_args,

tests/slow/test_sft_slow.py

@@ -46,7 +46,7 @@ class SFTTrainerSlowTester(unittest.TestCase):
     def setUp(self):
         self.train_dataset = load_dataset("stanfordnlp/imdb", split="train[:10%]")
         self.eval_dataset = load_dataset("stanfordnlp/imdb", split="test[:10%]")
-        self.max_seq_length = 128
+        self.max_length = 128
         self.peft_config = LoraConfig(
             lora_alpha=16,
             lora_dropout=0.1,
@@ -74,7 +74,7 @@ def test_sft_trainer_str(self, model_name, packing):
                 per_device_train_batch_size=2,
                 max_steps=10,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
             )
 
             trainer = SFTTrainer(
@@ -100,7 +100,7 @@ def test_sft_trainer_transformers(self, model_name, packing):
                 per_device_train_batch_size=2,
                 max_steps=10,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
             )
 
             model = AutoModelForCausalLM.from_pretrained(model_name)
@@ -135,7 +135,7 @@ def test_sft_trainer_peft(self, model_name, packing):
                 max_steps=10,
                 fp16=True,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
             )
 
             model = AutoModelForCausalLM.from_pretrained(model_name)
@@ -172,7 +172,7 @@ def test_sft_trainer_transformers_mp(self, model_name, packing):
                 max_steps=10,
                 fp16=True,  # this is sufficient to enable amp
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
             )
 
             model = AutoModelForCausalLM.from_pretrained(model_name)
@@ -205,7 +205,7 @@ def test_sft_trainer_transformers_mp_gc(self, model_name, packing, gradient_chec
                 per_device_train_batch_size=2,
                 max_steps=10,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
                 fp16=True,  # this is sufficient to enable amp
                 gradient_checkpointing=True,
                 gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
@@ -242,7 +242,7 @@ def test_sft_trainer_transformers_mp_gc_peft(self, model_name, packing, gradient
                 per_device_train_batch_size=2,
                 max_steps=10,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
                 fp16=True,  # this is sufficient to enable amp
                 gradient_checkpointing=True,
                 gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
@@ -286,7 +286,7 @@ def test_sft_trainer_transformers_mp_gc_device_map(
                 per_device_train_batch_size=2,
                 max_steps=10,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
                 fp16=True,  # this is sufficient to enable amp
                 gradient_checkpointing=True,
                 gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
@@ -324,7 +324,7 @@ def test_sft_trainer_transformers_mp_gc_peft_qlora(self, model_name, packing, gr
                 per_device_train_batch_size=2,
                 max_steps=10,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
                 fp16=True,  # this is sufficient to enable amp
                 gradient_checkpointing=True,
                 gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
@@ -364,7 +364,7 @@ def test_sft_trainer_with_chat_format_qlora(self, model_name, packing):
 
             training_args = SFTConfig(
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
                 output_dir=tmp_dir,
                 logging_strategy="no",
                 report_to="none",
@@ -411,7 +411,7 @@ def test_sft_trainer_with_liger(self, model_name, packing):
                 per_device_train_batch_size=2,
                 max_steps=2,
                 packing=packing,
-                max_seq_length=self.max_seq_length,
+                max_length=self.max_length,
                 use_liger=True,
             )
 

tests/test_sft_trainer.py

@@ -326,7 +326,7 @@ def test_sft_trainer_uncorrect_data(self):
                 eval_steps=1,
                 save_steps=1,
                 per_device_train_batch_size=2,
-                max_seq_length=32,  # make sure there is at least 1 packed sequence
+                max_length=32,  # make sure there is at least 1 packed sequence
                 packing=True,
                 report_to="none",
             )
@@ -353,15 +353,15 @@ def test_sft_trainer_uncorrect_data(self):
                 train_dataset=self.conversational_lm_dataset["train"],
             )
 
-            # Same, but with packing with `max_seq_length`
+            # Same, but with packing with `max_length`
             training_args = SFTConfig(
                 output_dir=tmp_dir,
                 dataloader_drop_last=True,
                 max_steps=2,
                 eval_steps=1,
                 save_steps=1,
                 per_device_train_batch_size=2,
-                max_seq_length=16,  # make sure there is at least 1 packed sequence
+                max_length=16,  # make sure there is at least 1 packed sequence
                 packing=True,
                 report_to="none",
             )
@@ -396,7 +396,7 @@ def test_sft_trainer_uncorrect_data(self):
                 eval_steps=1,
                 save_steps=1,
                 per_device_train_batch_size=2,
-                max_seq_length=32,  # make sure there is at least 1 packed sequence
+                max_length=32,  # make sure there is at least 1 packed sequence
                 packing=True,
                 report_to="none",
             )
@@ -461,7 +461,7 @@ def test_sft_trainer_with_model_num_train_epochs(self):
                 save_steps=1,
                 num_train_epochs=2,
                 per_device_train_batch_size=2,
-                max_seq_length=16,
+                max_length=16,
                 packing=True,
                 report_to="none",
             )
@@ -485,7 +485,7 @@ def test_sft_trainer_with_model_num_train_epochs(self):
                 save_steps=1,
                 num_train_epochs=2,
                 per_device_train_batch_size=2,
-                max_seq_length=16,
+                max_length=16,
                 report_to="none",
             )
             trainer = SFTTrainer(
@@ -534,7 +534,7 @@ def test_sft_trainer_with_model(self):
                 max_steps=2,
                 save_steps=1,
                 per_device_train_batch_size=2,
-                max_seq_length=16,
+                max_length=16,
                 packing=True,
                 report_to="none",
             )
@@ -558,7 +558,7 @@ def test_sft_trainer_with_model(self):
                 max_steps=2,
                 save_steps=1,
                 per_device_train_batch_size=2,
-                max_seq_length=16,
+                max_length=16,
                 packing=True,
                 report_to="none",
             )
@@ -583,7 +583,7 @@ def test_sft_trainer_with_model(self):
                 max_steps=2,
                 save_steps=1,
                 per_device_train_batch_size=2,
-                max_seq_length=16,
+                max_length=16,
                 report_to="none",
             )
             trainer = SFTTrainer(
@@ -606,7 +606,7 @@ def test_sft_trainer_with_model(self):
                 max_steps=2,
                 save_steps=1,
                 per_device_train_batch_size=2,
-                max_seq_length=16,
+                max_length=16,
                 report_to="none",
             )
             trainer = SFTTrainer(
@@ -755,7 +755,7 @@ def test_sft_trainer_infinite_with_model(self):
                 save_steps=1,
                 per_device_train_batch_size=2,
                 packing=True,
-                max_seq_length=500,
+                max_length=500,
                 report_to="none",
             )
             trainer = SFTTrainer(
@@ -782,7 +782,7 @@ def test_sft_trainer_infinite_with_model_epochs(self):
                 per_device_train_batch_size=2,
                 save_strategy="epoch",
                 packing=True,
-                max_seq_length=500,
+                max_length=500,
                 report_to="none",
             )
             trainer = SFTTrainer(
@@ -1088,7 +1088,7 @@ def test_sft_trainer_only_train_packing(self):
                 per_device_train_batch_size=2,
                 gradient_checkpointing=True,
                 packing=True,
-                max_seq_length=16,  # make sure there is at least 1 packed sequence
+                max_length=16,  # make sure there is at least 1 packed sequence
                 eval_packing=False,
                 report_to="none",
             )
@@ -1114,7 +1114,7 @@ def test_sft_trainer_eval_packing(self):
                 save_steps=2,
                 per_device_train_batch_size=2,
                 gradient_checkpointing=True,
-                max_seq_length=16,  # make sure there is at least 1 packed sequence
+                max_length=16,  # make sure there is at least 1 packed sequence
                 packing=True,
                 report_to="none",
             )
@@ -1139,7 +1139,7 @@ def test_sft_trainer_no_packing(self):
                 save_steps=2,
                 per_device_train_batch_size=2,
                 gradient_checkpointing=True,
-                max_seq_length=16,  # make sure there is at least 1 packed sequence
+                max_length=16,  # make sure there is at least 1 packed sequence
                 packing=False,
                 report_to="none",
             )

tests/test_trainers_args.py

@@ -368,7 +368,7 @@ def test_sft(self):
                 tmp_dir,
                 dataset_text_field="dummy_text_field",
                 packing=True,
-                max_seq_length=256,
+                max_length=256,
                 dataset_num_proc=4,
                 dataset_batch_size=512,
                 neftune_noise_alpha=0.1,
@@ -379,7 +379,7 @@ def test_sft(self):
             trainer = SFTTrainer(model_id, args=training_args, train_dataset=dataset)
             self.assertEqual(trainer.args.dataset_text_field, "dummy_text_field")
             self.assertEqual(trainer.args.packing, True)
-            self.assertEqual(trainer.args.max_seq_length, 256)
+            self.assertEqual(trainer.args.max_length, 256)
             self.assertEqual(trainer.args.dataset_num_proc, 4)
             self.assertEqual(trainer.args.dataset_batch_size, 512)
             self.assertEqual(trainer.args.neftune_noise_alpha, 0.1)

trl/trainer/gkd_trainer.py

@@ -87,7 +87,7 @@ def __init__(
     ):
         # add remove_unused_columns=False to the dataclass args
         args.remove_unused_columns = False
-        data_collator = DataCollatorForChatML(tokenizer=processing_class, max_length=args.max_seq_length)
+        data_collator = DataCollatorForChatML(tokenizer=processing_class, max_length=args.max_length)
 
         super().__init__(
             model,