# Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ SFT dataset - We assume user pass a single parquet file. - We load all the data into the memory. Each parquet file contains """ from typing import List, Union import pandas as pd import torch from torch.utils.data import Dataset from transformers import AutoTokenizer, PreTrainedTokenizer from verl.utils.fs import copy_to_local from verl.utils.model import compute_position_id_with_mask from verl.utils import hf_tokenizer class SFTDataset(Dataset): """ This is an in-memory SFTDataset Arguments: config (OmegaConf): the data config """ def __init__(self, parquet_files: Union[str, List[str]], tokenizer, config): prompt_key = config.get('prompt_key', 'prompt') prompt_dict_keys = config.get('prompt_dict_keys', None) response_key = config.get('response_key', 'response') response_dict_keys = config.get('response_dict_keys', None) max_length = config.get('max_length', 1024) truncation = config.get('truncation', 'error') assert truncation in ['error', 'left', 'right'] self.truncation = truncation if not isinstance(parquet_files, List): parquet_files = [parquet_files] self.parquet_files = parquet_files if isinstance(tokenizer, str): tokenizer = hf_tokenizer(tokenizer) self.tokenizer: PreTrainedTokenizer = tokenizer self.prompt_key = prompt_key if isinstance(prompt_key, (tuple, list)) else [prompt_key] self.response_key = response_key if isinstance(response_key, (tuple, list)) else [response_key] self.prompt_dict_keys = [] if not prompt_dict_keys else prompt_dict_keys self.response_dict_keys = [] if not response_dict_keys else response_dict_keys self.max_length = max_length self._download() self._read_files_and_tokenize() def _download(self): for i, parquet_file in enumerate(self.parquet_files): self.parquet_files[i] = copy_to_local(parquet_file, verbose=True) def _read_files_and_tokenize(self): def series_to_item(ls): import pandas, numpy while isinstance(ls, (pandas.core.series.Series, numpy.ndarray)) and len(ls) == 1: ls = ls[0] return ls dataframes = [] for parquet_file in self.parquet_files: # read parquet files and cache dataframe = pd.read_parquet(parquet_file) dataframes.append(dataframe) self.dataframe = pd.concat(dataframes) self.prompts = self.dataframe[self.prompt_key] for key in self.prompt_dict_keys: # type(x): pandas.core.series.Series # type(x[0]): numpy.ndarray # type(x[0][0]): dict try: self.prompts = self.prompts.apply(lambda x: series_to_item(x), axis=1) except Exception: print(f'self.prompts={self.prompts}') raise self.prompts = self.prompts.tolist() self.responses = self.dataframe[self.response_key] for key in self.response_dict_keys: try: self.responses = self.responses.apply(lambda x: series_to_item(x), axis=1) except Exception: print(f'self.responses={self.responses}') raise self.responses = self.responses.tolist() def __len__(self): return len(self.prompts) def __getitem__(self, item): tokenizer = self.tokenizer prompt = self.prompts[item] response = self.responses[item] # apply chat template prompt_chat = [{'role': 'user', 'content': prompt}] # string prompt_chat_str = tokenizer.apply_chat_template(prompt_chat, add_generation_prompt=True, tokenize=False) response_chat_str = response + tokenizer.eos_token # tokenize prompt_ids_output = tokenizer(prompt_chat_str, return_tensors='pt', add_special_tokens=False) prompt_ids = prompt_ids_output['input_ids'][0] prompt_attention_mask = prompt_ids_output['attention_mask'][0] response_ids_output = tokenizer(response_chat_str, return_tensors='pt', add_special_tokens=False) response_ids = response_ids_output['input_ids'][0] response_attention_mask = response_ids_output['attention_mask'][0] prompt_length = prompt_ids.shape[0] response_length = response_ids.shape[0] input_ids = torch.cat((prompt_ids, response_ids), dim=-1) attention_mask = torch.cat((prompt_attention_mask, response_attention_mask), dim=-1) # padding to max length sequence_length = input_ids.shape[0] if sequence_length < self.max_length: padded_input_ids = torch.ones(size=(self.max_length - sequence_length,), dtype=input_ids.dtype) * self.tokenizer.pad_token_id padded_attention_mask = torch.zeros(size=(self.max_length - sequence_length,), dtype=attention_mask.dtype) input_ids = torch.cat((input_ids, padded_input_ids)) attention_mask = torch.cat((attention_mask, padded_attention_mask)) elif sequence_length > self.max_length: if self.truncation == 'left': # actually, left truncation may not be reasonable input_ids = input_ids[-self.max_length:] attention_mask = attention_mask[-self.max_length:] elif self.truncation == 'right': input_ids = input_ids[:self.max_length] attention_mask = attention_mask[:self.max_length] elif self.truncation == 'error': raise NotImplementedError(f'{sequence_length=} is larger than {self.max_length=}') else: raise NotImplementedError(f'Unknown truncation method {self.truncation}') position_ids = compute_position_id_with_mask(attention_mask) loss_mask = attention_mask.clone() if prompt_length > 1: # mask out prompt for SFT. loss_mask[:min(prompt_length, loss_mask.size(0)) - 1] = 0 # mask out the last token in response loss_mask[min(prompt_length + response_length, loss_mask.size(0)) - 1] = 0 return { 'input_ids': input_ids, 'attention_mask': attention_mask, 'position_ids': position_ids, 'loss_mask': loss_mask }