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| import logging |
| from typing import Callable, Literal, Optional, Union |
|
|
| from datasets import Dataset, Value |
| from transformers import AutoTokenizer |
|
|
| from ..trainer.utils import ConstantLengthDataset |
|
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|
|
| FORMAT_MAPPING = { |
| "chatml": [{"content": Value(dtype="string", id=None), "role": Value(dtype="string", id=None)}], |
| "instruction": {"completion": Value(dtype="string", id=None), "prompt": Value(dtype="string", id=None)}, |
| } |
|
|
|
|
| def conversations_formatting_function(tokenizer: AutoTokenizer, messages_field: Literal["messages", "conversations"]): |
| r""" |
| return a callable function that takes in a "messages" dataset and returns a formatted dataset, based on the tokenizer |
| apply chat template to the dataset |
| """ |
|
|
| def format_dataset(examples): |
| if isinstance(examples[messages_field][0], list): |
| output_texts = [] |
| for i in range(len(examples[messages_field])): |
| output_texts.append(tokenizer.apply_chat_template(examples[messages_field][i], tokenize=False)) |
| return output_texts |
| else: |
| return tokenizer.apply_chat_template(examples[messages_field], tokenize=False) |
|
|
| return format_dataset |
|
|
|
|
| def instructions_formatting_function(tokenizer: AutoTokenizer): |
| r""" |
| return a callable function that takes in an "instructions" dataset and returns a formatted dataset, based on the tokenizer |
| apply chat template to the dataset |
| """ |
|
|
| def format_dataset(examples): |
| if isinstance(examples["prompt"], list): |
| output_texts = [] |
| for i in range(len(examples["prompt"])): |
| converted_sample = [ |
| {"role": "user", "content": examples["prompt"][i]}, |
| {"role": "assistant", "content": examples["completion"][i]}, |
| ] |
| output_texts.append(tokenizer.apply_chat_template(converted_sample, tokenize=False)) |
| return output_texts |
| else: |
| converted_sample = [ |
| {"role": "user", "content": examples["prompt"]}, |
| {"role": "assistant", "content": examples["completion"]}, |
| ] |
| return tokenizer.apply_chat_template(converted_sample, tokenize=False) |
|
|
| return format_dataset |
|
|
|
|
| def get_formatting_func_from_dataset( |
| dataset: Union[Dataset, ConstantLengthDataset], tokenizer: AutoTokenizer |
| ) -> Optional[Callable]: |
| r""" |
| Finds the correct formatting function based on the dataset structure. Currently supported datasets are: |
| - `ChatML` with [{"role": str, "content": str}] |
| - `instruction` with [{"prompt": str, "completion": str}] |
| |
| Args: |
| dataset (Dataset): User dataset |
| tokenizer (AutoTokenizer): Tokenizer used for formatting |
| |
| Returns: |
| Callable: Formatting function if the dataset format is supported else None |
| """ |
| if isinstance(dataset, Dataset): |
| if "messages" in dataset.features: |
| if dataset.features["messages"] == FORMAT_MAPPING["chatml"]: |
| logging.info("Formatting dataset with chatml format") |
| return conversations_formatting_function(tokenizer, "messages") |
| if "conversations" in dataset.features: |
| if dataset.features["conversations"] == FORMAT_MAPPING["chatml"]: |
| logging.info("Formatting dataset with chatml format") |
| return conversations_formatting_function(tokenizer, "conversations") |
| elif dataset.features == FORMAT_MAPPING["instruction"]: |
| logging.info("Formatting dataset with instruction format") |
| return instructions_formatting_function(tokenizer) |
|
|
| return None |
|
|
