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@ -1,12 +1,46 @@
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from typing import Callable |
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import copy |
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from dataclasses import dataclass, field |
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from typing import Callable, Dict, Sequence |
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import random |
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from torch.utils.data import Dataset |
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import torch.distributed as dist |
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from tqdm import tqdm |
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import torch |
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from .utils import is_rank_0 |
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from .utils import is_rank_0, jload |
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import transformers |
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from colossalai.logging import get_dist_logger |
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logger = get_dist_logger() |
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IGNORE_INDEX = -100 |
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PROMPT_DICT = { |
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"prompt_input": ( |
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"Below is an instruction that describes a task, paired with an input that provides further context. " |
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"Write a response that appropriately completes the request.\n\n" |
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"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" |
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), |
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"prompt_no_input": ( |
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"Below is an instruction that describes a task. " |
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"Write a response that appropriately completes the request.\n\n" |
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"### Instruction:\n{instruction}\n\n### Response:" |
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), |
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} |
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class SFTDataset(Dataset): |
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""" |
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@ -38,3 +72,87 @@ class SFTDataset(Dataset):
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def __getitem__(self, idx): |
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return self.prompts[idx] |
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def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: |
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"""Tokenize a list of strings.""" |
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tokenized_list = [ |
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tokenizer( |
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text, |
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return_tensors="pt", |
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padding="longest", |
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max_length=tokenizer.model_max_length, |
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truncation=True, |
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) |
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for text in strings |
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] |
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input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] |
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input_ids_lens = labels_lens = [ |
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tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list |
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] |
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return dict( |
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input_ids=input_ids, |
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labels=labels, |
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input_ids_lens=input_ids_lens, |
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labels_lens=labels_lens, |
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) |
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def preprocess( |
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sources: Sequence[str], |
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targets: Sequence[str], |
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tokenizer: transformers.PreTrainedTokenizer, |
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) -> Dict: |
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"""Preprocess the data by tokenizing.""" |
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examples = [s + t for s, t in zip(sources, targets)] |
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examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] |
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input_ids = examples_tokenized["input_ids"] |
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labels = copy.deepcopy(input_ids) |
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for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): |
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label[:source_len] = IGNORE_INDEX |
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return dict(input_ids=input_ids, labels=labels) |
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class AlpacaDataset(Dataset): |
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"""Dataset for supervised fine-tuning.""" |
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def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer): |
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super(AlpacaDataset, self).__init__() |
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logger.info("Loading data...") |
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list_data_dict = jload(data_path) |
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logger.info("Formatting inputs...") |
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prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] |
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sources = [ |
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prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example) |
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for example in list_data_dict |
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] |
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targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] |
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logger.info("Tokenizing inputs... This may take some time...") |
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data_dict = preprocess(sources, targets, tokenizer) |
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self.input_ids = data_dict["input_ids"] |
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self.labels = data_dict["labels"] |
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def __len__(self): |
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return len(self.input_ids) |
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def __getitem__(self, i) -> Dict[str, torch.Tensor]: |
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return dict(input_ids=self.input_ids[i], labels=self.labels[i]) |
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@dataclass |
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class AlpacaDataCollator(object): |
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"""Collate examples for supervised fine-tuning.""" |
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tokenizer: transformers.PreTrainedTokenizer |
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def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: |
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input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) |
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input_ids = torch.nn.utils.rnn.pad_sequence( |
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input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id |
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) |
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labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) |
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return dict( |
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input_ids=input_ids, |
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labels=labels, |
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attention_mask=input_ids.ne(self.tokenizer.pad_token_id), |
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) |
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Fazzie-Maqianli
2 years ago
committed by