data.py

# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import annotations

import numpy as np

from paddlenlp.peft import LoRAModel, PrefixModelForCausalLM


def convert_multi_rounds_to_single_round(example, tokenizer):
 # 1. convert multi-rounds to single-round data format with chat_template
 example["src"] = example["src"] if isinstance(example["src"], list) else [example["src"]]
 example["tgt"] = example["tgt"] if isinstance(example["tgt"], list) else [example["tgt"]]

 src = tokenizer.chat_template.render_system()
 conversations = list(zip(example["src"], example["tgt"]))

 for index, conversation in enumerate(conversations[:-1]):
 src += "".join(tokenizer.chat_template.render_conversation(conversation, index=index))

 last_user, last_bot = tokenizer.chat_template.render_conversation(conversations[-1], index=len(conversations) - 1)

 example["src"] = [src + last_user]
 example["tgt"] = [last_bot]
 return example


def get_convert_example(model):
 if isinstance(model, LoRAModel) or isinstance(model, PrefixModelForCausalLM):
 base_model_prefix = model.model.base_model_prefix
 else:
 base_model_prefix = model.base_model_prefix

 if base_model_prefix == "chatglm":
 return convert_example_chatglm
 elif base_model_prefix in ["chatglm_v2", "llama", "bloom", "opt", "qwen"]:
 return convert_example_common
 else:
 raise ValueError(
 f"Unknown base_model_prefix: {model.base_model_prefix}. Supported base_model_prefix list: chatglm, bloom, llama."
 )


class DataFormatError(ValueError):
 pass


def tokenize_example(tokenizer, example, data_args):
 if "src" in example and "tgt" in example:
 source = example["src"][0] if isinstance(example["src"], list) else example["src"]
 target = example["tgt"][0] if isinstance(example["tgt"], list) else example["tgt"]
 else:
 raise DataFormatError(
 f"Example format is wrong, please check: {example} or rewrite tokenize_example in data.py "
 )
 tokenized_source = tokenizer(
 source,
 max_length=data_args.src_length,
 truncation=True,
 truncation_side="left",
 add_special_tokens=True,
 )

 tgt_max_length = data_args.max_length - len(tokenized_source["input_ids"])
 tokenized_target = tokenizer(
 target,
 max_length=tgt_max_length,
 truncation=True,
 truncation_side="right",
 add_special_tokens=False,
 )

 tokenized_target_input_ids = tokenized_target["input_ids"]
 # Add eos_token_id at the end of sequence if the sentence is not truncated.
 # Attention! In some cases(ex. ChatGLMv2), tokenized eos_token is not equal to eos_token_id.
 if len(tokenized_target_input_ids) < tgt_max_length:
 tokenized_target_input_ids += [tokenizer.eos_token_id]

 return tokenized_source, tokenized_target_input_ids


def tokenize_rounds_example(tokenizer, example, data_args):
 """tokenize multi-rounds examples with chat_template.json

 Args:
 tokenizer (PretrainedTokenizer): the instance of tokenizer
 example (dict[str, str | list[str]]):
 the example instance, which can be: {"src": "src-sentence", "tgt": "tgt-sentence"}
 or {"src": ["src-sentence-1", ..., "src-sentence-N"], "tgt": ["tgt-sentence-1", ..., "tgt-sentence-N"]}
 data_args (DataArgument): the data_argument instance of data processing

 Returns:
 dict[str, list[int]]: return input_ids and labels fields
 """

 # 0. prepare data
 context_data = example.get("context", {})
 example["src"] = example["src"] if isinstance(example["src"], list) else [example["src"]]
 example["tgt"] = example["tgt"] if isinstance(example["tgt"], list) else [example["tgt"]]

 assert len(example["src"]) == len(example["tgt"]), "the length of `src` and `tgt` field must be same."

 conversations = [[src, tgt] for src, tgt in zip(example["src"], example["tgt"])]

 # 1. only tokenize input_ids
 conversation_result: list[tuple[list[int], list[int]]] = tokenizer.encode_chat_inputs(
 conversations, context_data=context_data
 )
 system_ids = conversation_result.pop("system", []) or []

 # 2. truncate conversations based on conversation unit
 input_ids, labels = [], []
 conversations_ids = conversation_result.pop("conversations")

 assert (
 len(system_ids) < data_args.max_length
 ), f"the length of system_ids<{len(system_ids)}> should be smaller than max_length<{data_args.max_length}>."
 max_length = data_args.max_length - len(system_ids)

 should_break = False
 for index in range(len(conversations_ids) - 1, -1, -1):
 user_input_ids, bot_input_ids = conversations_ids[index][0], conversations_ids[index][1]

 # break when the length of current conversations is greater than max_length
 if len(input_ids) + len(user_input_ids) + len(bot_input_ids) > max_length:

 # when the length of last conversation is lager than max_length, we should not break: at least one round
 if index < len(conversations_ids) - 1:
 break

 user_input_ids = user_input_ids[: data_args.src_length - len(system_ids)]
 bot_input_ids = bot_input_ids[: max_length - len(user_input_ids)]

 should_break = True

 input_ids = user_input_ids + bot_input_ids + input_ids
 labels = len(user_input_ids) * [-100] + bot_input_ids + labels

 if should_break:
 break

 input_ids = system_ids + input_ids
 labels = [-100] * len(system_ids) + labels
 tokenized_source = {"input_ids": input_ids}
 sequence_length = len(input_ids)

 if "position_ids" in tokenizer.model_input_names:
 tokenized_source["position_ids"] = list(range(sequence_length))

 return tokenized_source, labels


def convert_example_common(example, tokenizer, data_args, is_test=True, intokens=False):
 if tokenizer.chat_template is not None:
 return convert_rounds_example_common(example, tokenizer, data_args, is_test, intokens)

 tokenized_source, tokenized_target_input_ids = tokenize_example(tokenizer, example, data_args)
 if is_test:
 return {
 **tokenized_source,
 "labels": tokenized_target_input_ids,
 }
 else:
 input_ids = tokenized_source["input_ids"] + tokenized_target_input_ids
 source_length = len(tokenized_source["input_ids"])
 labels = [-100] * source_length + input_ids[source_length:]
 # shift input_ids and labels
 input_ids, labels = input_ids[:-1], labels[1:]
 seq_length = len(input_ids)
 features = {"input_ids": input_ids, "labels": labels}
 if "position_ids" in tokenized_source:
 features["position_ids"] = list(range(seq_length))
 if intokens:
 features["attention_mask"] = np.tri(seq_length, seq_length, dtype=bool)

 return features


def convert_rounds_example_common(example, tokenizer, data_args, is_test=True, intokens=False):
 """convert multi-rounds conversation example

 Args:
 example (dict): the source of example
 tokenizer (PretrainedTokenizer): the instance of tokenizer
 data_args (DataArgument): data argument for data preprocessing
 is_test (bool, optional): whether is testing stage. Defaults to True.
 intokens (bool, optional): whether use in_tokens. Defaults to False.

 Returns:
 dict[str, np.ndarray]: the features of example
 """
 rounds_inputs, labels = tokenize_rounds_example(tokenizer, example, data_args)

 if is_test:
 return {
 **rounds_inputs,
 "labels": labels,
 }

 input_ids = rounds_inputs.pop("input_ids")
 # shift input_ids and labels
 input_ids, labels = input_ids[:-1], labels[1:]

 seq_length = len(input_ids)
 features = {"input_ids": input_ids, "labels": labels}
 if intokens:
 features["attention_mask"] = np.tri(seq_length, seq_length, dtype=bool)

 if "position_ids" in rounds_inputs:
 rounds_inputs["position_ids"] = rounds_inputs["position_ids"][:-1]

 rounds_inputs.update(features)
 return rounds_inputs


def convert_example_chatglm(example, tokenizer, data_args, is_test=True, intokens=False):
 if tokenizer.chat_template is not None:
 # chatglm only support single-round finetune
 example = convert_multi_rounds_to_single_round(example, tokenizer)

 tokenized_source, tokenized_target_input_ids = tokenize_example(tokenizer, example, data_args)

 if is_test:
 return {
 **tokenized_source,
 "labels": tokenized_target_input_ids,
 }
 else:
 input_ids = tokenized_source["input_ids"] + tokenized_target_input_ids
 bos_position = len(tokenized_source["input_ids"]) - 1
 labels = [-100] * bos_position + input_ids[bos_position:]
 # shift input_ids and labels
 input_ids, labels = input_ids[:-1], labels[1:]
 features = {
 "input_ids": input_ids,
 "labels": labels,
 }

 if intokens:
 seq_length = len(input_ids)
 # attention_mask
 attention_mask = np.tri(seq_length, seq_length, dtype=bool)
 attention_mask[:, :bos_position] = 1
 features["attention_mask"] = attention_mask
 # 2d position_ids
 position_ids = np.arange(seq_length, dtype=np.int64)
 position_ids[:bos_position] = bos_position - 1
 block_position_ids = np.concatenate(
 [
 np.zeros(bos_position, dtype=np.int64),
 np.arange(1, seq_length - bos_position + 1, dtype=np.int64),
 ]
 )
 features["position_ids"] = np.stack([position_ids, block_position_ids], axis=0)

 return features