update get template

Former-commit-id: dabad5570bf4a6b1044c963d8f27717030f373ef

scripts/cal_lr.py

@@ -25,7 +25,7 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
 
-from llamafactory.data import get_dataset
+from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
 from llamafactory.extras.constants import IGNORE_INDEX
 from llamafactory.hparams import get_train_args
 from llamafactory.model import load_tokenizer
@@ -66,7 +66,8 @@ def calculate_lr(
     )
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    trainset = get_dataset(template, model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
     if stage == "pt":
         data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
     elif stage == "sft":

scripts/cal_ppl.py

@@ -23,7 +23,7 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
 
-from llamafactory.data import get_dataset
+from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
 from llamafactory.extras.constants import IGNORE_INDEX
 from llamafactory.hparams import get_train_args
 from llamafactory.model import load_model, load_tokenizer
@@ -88,7 +88,8 @@ def cal_ppl(
     )
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    trainset = get_dataset(template, model_args, data_args, training_args, stage, **tokenizer_module)["train_dataset"]
     model = load_model(tokenizer, model_args, finetuning_args, is_trainable=False)
     if stage == "pt":
         data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

scripts/length_cdf.py

@@ -18,7 +18,7 @@ from collections import defaultdict
 import fire
 from tqdm import tqdm
 
-from llamafactory.data import get_dataset
+from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
 from llamafactory.hparams import get_train_args
 from llamafactory.model import load_tokenizer
 
@@ -48,7 +48,8 @@ def length_cdf(
         )
     )
     tokenizer_module = load_tokenizer(model_args)
-    trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)["train_dataset"]
+    template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
+    trainset = get_dataset(template, model_args, data_args, training_args, "sft", **tokenizer_module)["train_dataset"]
     total_num = len(trainset)
     length_dict = defaultdict(int)
     for sample in tqdm(trainset["input_ids"]):

src/llamafactory/chat/hf_engine.py

@@ -54,7 +54,7 @@ class HuggingfaceEngine(BaseEngine):
         self.tokenizer = tokenizer_module["tokenizer"]
         self.processor = tokenizer_module["processor"]
         self.tokenizer.padding_side = "left" if self.can_generate else "right"
-        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args)
         self.model = load_model(
             self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
         )  # must after fixing tokenizer to resize vocab

src/llamafactory/chat/vllm_engine.py

@@ -68,7 +68,7 @@ class VllmEngine(BaseEngine):
         self.tokenizer = tokenizer_module["tokenizer"]
         self.processor = tokenizer_module["processor"]
         self.tokenizer.padding_side = "left"
-        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args)
         self.generating_args = generating_args.to_dict()
 
         engine_args = {

src/llamafactory/data/loader.py

@@ -14,7 +14,7 @@
 
 import os
 import sys
-from typing import TYPE_CHECKING, Dict, Literal, Optional, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Dict, Literal, Optional, Sequence, Union
 
 import numpy as np
 from datasets import DatasetDict, load_dataset, load_from_disk
@@ -27,7 +27,6 @@ from .aligner import align_dataset
 from .data_utils import merge_dataset, split_dataset
 from .parser import get_dataset_list
 from .preprocess import get_preprocess_and_print_func
-from .template import get_template_and_fix_tokenizer
 
 
 if TYPE_CHECKING:
@@ -179,9 +178,6 @@ def _get_preprocessed_dataset(
             load_from_cache_file=(not data_args.overwrite_cache) or (training_args.local_process_index != 0),
             desc="Running tokenizer on dataset",
         )
-        if data_args.dataset_map_batch_size:
-            # Set the batch size conditionally without considering the default variable of the batch size in the map function
-            kwargs.update(batch_size=data_args.dataset_map_batch_size)
 
     dataset = dataset.map(
         preprocess_func,
@@ -205,17 +201,14 @@ def _get_preprocessed_dataset(
 
 
 def get_dataset(
+    template: "Template",
     model_args: "ModelArguments",
     data_args: "DataArguments",
     training_args: "Seq2SeqTrainingArguments",
     stage: Literal["pt", "sft", "rm", "ppo", "kto"],
     tokenizer: "PreTrainedTokenizer",
     processor: Optional["ProcessorMixin"] = None,
-) -> Tuple["DatasetModule", "Template"]:
+) -> "DatasetModule":
-    template = get_template_and_fix_tokenizer(tokenizer, data_args.template, data_args.tool_format)
-    if data_args.train_on_prompt and template.efficient_eos:
-        raise ValueError("Current template does not support `train_on_prompt`.")
-
     # Load tokenized dataset
     if data_args.tokenized_path is not None:
         if has_tokenized_data(data_args.tokenized_path):
@@ -233,7 +226,7 @@ def get_dataset(
             if data_args.streaming:
                 dataset_module = {k: v.to_iterable_dataset() for k, v in dataset_module.items()}
 
-            return dataset_module, template
+            return dataset_module
 
         if data_args.streaming:
             raise ValueError("Turn off `streaming` when saving dataset to disk.")
@@ -280,7 +273,8 @@ def get_dataset(
         dataset_module = {}
         if "train" in dataset_dict:
             dataset_module["train_dataset"] = dataset_dict["train"]
+
         if "validation" in dataset_dict:
             dataset_module["eval_dataset"] = dataset_dict["validation"]
 
-        return dataset_module, template
+        return dataset_module

src/llamafactory/data/template.py

@@ -27,6 +27,7 @@ from .mm_plugin import get_mm_plugin
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
 
+    from ..hparams import DataArguments
     from .formatter import SLOTS, Formatter
     from .mm_plugin import BasePlugin
 
@@ -344,28 +345,27 @@ def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer")
     return jinja_template
 
 
-def get_template_and_fix_tokenizer(
+def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args: "DataArguments") -> "Template":
-    tokenizer: "PreTrainedTokenizer",
+    if data_args.template in ["llava", "paligemma", "qwen2_vl"]:
-    name: Optional[str] = None,
-    tool_format: Optional[str] = None,
-) -> Template:
-    if name in ["llava", "paligemma", "qwen2_vl"]:
         require_version(
             "transformers>=4.45.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git"
         )
 
-    if name is None:
+    if data_args.template is None:
         template = TEMPLATES["empty"]  # placeholder
     else:
-        template = TEMPLATES.get(name, None)
+        template = TEMPLATES.get(data_args.template, None)
         if template is None:
-            raise ValueError("Template {} does not exist.".format(name))
+            raise ValueError("Template {} does not exist.".format(data_args.template))
 
-    if tool_format is not None:
+    if data_args.train_on_prompt and template.efficient_eos:
-        logger.info("Using tool format: {}.".format(tool_format))
+        raise ValueError("Current template does not support `train_on_prompt`.")
+
+    if data_args.tool_format is not None:
+        logger.info("Using tool format: {}.".format(data_args.tool_format))
         eos_slots = [] if template.efficient_eos else [{"eos_token"}]
-        template.format_function = FunctionFormatter(slots=eos_slots, tool_format=tool_format)
+        template.format_function = FunctionFormatter(slots=eos_slots, tool_format=data_args.tool_format)
-        template.format_tools = ToolFormatter(tool_format=tool_format)
+        template.format_tools = ToolFormatter(tool_format=data_args.tool_format)
 
     stop_words = template.stop_words
     if template.replace_eos:

src/llamafactory/eval/evaluator.py

@@ -59,7 +59,7 @@ class Evaluator:
         self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
         self.tokenizer = load_tokenizer(self.model_args)["tokenizer"]
         self.tokenizer.padding_side = "right"  # avoid overflow issue in batched inference for llama2
-        self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args.template)
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args)
         self.model = load_model(self.tokenizer, self.model_args, finetuning_args)
         self.eval_template = get_eval_template(self.eval_args.lang)
         self.choice_inputs = [self.tokenizer.encode(ch, add_special_tokens=False)[-1] for ch in CHOICES]

src/llamafactory/hparams/data_args.py

@@ -113,10 +113,6 @@ class DataArguments:
         default=None,
         metadata={"help": "Path to save or load the tokenized datasets."},
     )
-    dataset_map_batch_size: Optional[int] = field(
-        default=None,
-        metadata={"help": "Batch size for dataset mapping."},
-    )
 
     def __post_init__(self):
         def split_arg(arg):

src/llamafactory/train/dpo/workflow.py

@@ -17,7 +17,7 @@
 
 from typing import TYPE_CHECKING, List, Optional
 
-from ...data import PairwiseDataCollatorWithPadding, get_dataset
+from ...data import PairwiseDataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
 from ...extras.constants import IGNORE_INDEX
 from ...extras.ploting import plot_loss
 from ...hparams import ModelArguments
@@ -41,7 +41,8 @@ def run_dpo(
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    dataset_module, template = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="rm", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
     data_collator = PairwiseDataCollatorWithPadding(

src/llamafactory/train/kto/workflow.py

@@ -17,7 +17,7 @@
 
 from typing import TYPE_CHECKING, List, Optional
 
-from ...data import KTODataCollatorWithPadding, get_dataset
+from ...data import KTODataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
 from ...extras.constants import IGNORE_INDEX
 from ...extras.ploting import plot_loss
 from ...hparams import ModelArguments
@@ -41,7 +41,8 @@ def run_kto(
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    dataset_module, template = get_dataset(model_args, data_args, training_args, stage="kto", **tokenizer_module)
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="kto", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
     data_collator = KTODataCollatorWithPadding(

src/llamafactory/train/ppo/workflow.py

@@ -17,7 +17,7 @@
 
 from typing import TYPE_CHECKING, List, Optional
 
-from ...data import MultiModalDataCollatorForSeq2Seq, get_dataset
+from ...data import MultiModalDataCollatorForSeq2Seq, get_dataset, get_template_and_fix_tokenizer
 from ...extras.ploting import plot_loss
 from ...model import load_model, load_tokenizer
 from ..callbacks import fix_valuehead_checkpoint
@@ -41,7 +41,8 @@ def run_ppo(
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    dataset_module, template = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="ppo", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
 
     tokenizer.padding_side = "left"  # use left-padding in generation while using right-padding in training

src/llamafactory/train/pt/workflow.py

@@ -20,7 +20,7 @@ from typing import TYPE_CHECKING, List, Optional
 
 from transformers import DataCollatorForLanguageModeling
 
-from ...data import get_dataset
+from ...data import get_dataset, get_template_and_fix_tokenizer
 from ...extras.ploting import plot_loss
 from ...model import load_model, load_tokenizer
 from ..trainer_utils import create_modelcard_and_push
@@ -42,7 +42,8 @@ def run_pt(
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    dataset_module, _ = get_dataset(model_args, data_args, training_args, stage="pt", **tokenizer_module)
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="pt", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
     data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
 

src/llamafactory/train/rm/workflow.py

@@ -17,7 +17,7 @@
 
 from typing import TYPE_CHECKING, List, Optional
 
-from ...data import PairwiseDataCollatorWithPadding, get_dataset
+from ...data import PairwiseDataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer
 from ...extras.ploting import plot_loss
 from ...model import load_model, load_tokenizer
 from ..callbacks import fix_valuehead_checkpoint
@@ -41,7 +41,8 @@ def run_rm(
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    dataset_module, template = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="rm", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
     data_collator = PairwiseDataCollatorWithPadding(template=template, pad_to_multiple_of=8, **tokenizer_module)
 

src/llamafactory/train/sft/workflow.py

@@ -17,7 +17,7 @@
 
 from typing import TYPE_CHECKING, List, Optional
 
-from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset
+from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
 from ...extras.constants import IGNORE_INDEX
 from ...extras.misc import get_logits_processor
 from ...extras.ploting import plot_loss
@@ -43,7 +43,8 @@ def run_sft(
 ):
     tokenizer_module = load_tokenizer(model_args)
     tokenizer = tokenizer_module["tokenizer"]
-    dataset_module, template = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
+    template = get_template_and_fix_tokenizer(tokenizer, data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
     if getattr(model, "is_quantized", False) and not training_args.do_train:
@@ -62,7 +63,7 @@ def run_sft(
     # Override the decoding parameters of Seq2SeqTrainer
     training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
     training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
-    training_args.remove_unused_columns = False  # important for multimodal and pairwise dataset
+    training_args.remove_unused_columns = False  # important for multimodal dataset
 
     # Metric utils
     metric_module = {}

src/llamafactory/train/test_utils.py

@@ -19,7 +19,7 @@ from peft import PeftModel
 from transformers import AutoModelForCausalLM
 from trl import AutoModelForCausalLMWithValueHead
 
-from ..data import get_dataset
+from ..data import get_dataset, get_template_and_fix_tokenizer
 from ..extras.misc import get_current_device
 from ..hparams import get_infer_args, get_train_args
 from ..model import load_model, load_tokenizer
@@ -105,7 +105,8 @@ def load_reference_model(
 def load_train_dataset(**kwargs) -> "Dataset":
     model_args, data_args, training_args, _, _ = get_train_args(kwargs)
     tokenizer_module = load_tokenizer(model_args)
-    dataset_module, _ = get_dataset(model_args, data_args, training_args, stage=kwargs["stage"], **tokenizer_module)
+    template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
+    dataset_module = get_dataset(template, model_args, data_args, training_args, kwargs["stage"], **tokenizer_module)
     return dataset_module["train_dataset"]
 
 

tests/data/test_template.py

@@ -19,6 +19,7 @@ import pytest
 from transformers import AutoTokenizer
 
 from llamafactory.data import get_template_and_fix_tokenizer
+from llamafactory.hparams import DataArguments
 
 
 if TYPE_CHECKING:
@@ -51,7 +52,7 @@ def _check_single_template(
     tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
     content_str = tokenizer.apply_chat_template(MESSAGES, tokenize=False)
     content_ids = tokenizer.apply_chat_template(MESSAGES, tokenize=True)
-    template = get_template_and_fix_tokenizer(tokenizer, name=template_name)
+    template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
     prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
     assert content_str == prompt_str + answer_str + extra_str
     assert content_ids == prompt_ids + answer_ids + tokenizer.encode(extra_str, add_special_tokens=False)
@@ -78,7 +79,7 @@ def _check_template(model_id: str, template_name: str, prompt_str: str, answer_s
 @pytest.mark.parametrize("use_fast", [True, False])
 def test_encode_oneturn(use_fast: bool):
     tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
-    template = get_template_and_fix_tokenizer(tokenizer, name="llama3")
+    template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
     prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
     prompt_str = (
         "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
@@ -93,7 +94,7 @@ def test_encode_oneturn(use_fast: bool):
 @pytest.mark.parametrize("use_fast", [True, False])
 def test_encode_multiturn(use_fast: bool):
     tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
-    template = get_template_and_fix_tokenizer(tokenizer, name="llama3")
+    template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
     encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
     prompt_str_1 = (
         "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"