# Copyright (c) 2022 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.

import math

from dataclasses import dataclass, field

from functools import partial

from itertools import chain

from typing import Optional

import paddle

import paddle.nn as nn

from datasets import load_dataset

from paddlenlp.data import DataCollatorWithPadding

from paddlenlp.trainer import PdArgumentParser, Trainer, TrainingArguments, set_seed

from paddlenlp.transformers import CodeGenForCausalLM, CodeGenTokenizer

from paddlenlp.utils.log import logger

@dataclass

class ModelArguments:

model_name_or_path: Optional[str] = field(

default="Salesforce/codegen-350M-mono",

metadata={"help": ("Path to pre-trained model.")},

)

overwrite_cache: Optional[bool] = field(

default=False,

metadata={"help": ("Whether to overwrite cache for dataset.")},

)

@dataclass

class DataArguments:

train_file: Optional[str] = field(

default=None,

metadata={"help": "The input training data file."},

)

validation_file: Optional[str] = field(

default=None,

metadata={"help": "The input validation data file."},

)

block_size: Optional[int] = field(

default=None,

metadata={"help": ("The training dataset will be truncated in block of this size for training. ")},

)

def compute_metrics(eval_preds):

labels = paddle.to_tensor(eval_preds.label_ids, dtype="int64")

logits = paddle.to_tensor(eval_preds.predictions)

loss_fct = nn.CrossEntropyLoss()

eval_loss = loss_fct(logits[:, :-1, :], labels[:, 1:])

perplexity = math.exp(eval_loss)

return {"perplexity": perplexity}

def convert_example(examples, tokenizer):

"""convert examples into necessary features"""

# Convert raw text to feature

tokenized_examples = tokenizer(

examples["code"], return_attention_mask=True, return_position_ids=False, return_token_type_ids=False

)

return tokenized_examples

def group_texts(examples, block_size):

concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}

total_length = len(concatenated_examples[list(examples.keys())[0]])

if total_length >= block_size:

total_length = (total_length // block_size) * block_size

result = {

k: [t[i : i + block_size] for i in range(0, total_length, block_size)]

for k, t in concatenated_examples.items()

}

result["labels"] = result["input_ids"].copy()

return result

def process_ds(dataset, tokenizer, overwrite_cache, block_size):

trans_func = partial(convert_example, tokenizer=tokenizer)

dataset = dataset.map(

trans_func, batched=True, remove_columns=dataset.column_names, load_from_cache_file=overwrite_cache

)

trans_func = partial(group_texts, block_size=block_size)

dataset = dataset.map(trans_func, batched=True, load_from_cache_file=overwrite_cache)

return dataset

def do_train():

parser = PdArgumentParser((ModelArguments, DataArguments, TrainingArguments))

model_args, data_args, training_args = parser.parse_args_into_dataclasses()

paddle.set_device(training_args.device)

if paddle.distributed.get_world_size() > 1:

paddle.distributed.init_parallel_env()

set_seed(training_args.seed)

model = CodeGenForCausalLM.from_pretrained(model_args.model_name_or_path)

tokenizer = CodeGenTokenizer.from_pretrained(model_args.model_name_or_path)

train_set = load_dataset("json", data_files=data_args.train_file, split="train")

dev_set = load_dataset("json", data_files=data_args.validation_file, split="train")

if data_args.block_size is None:

block_size = tokenizer.model_max_length

if block_size > 1024:

logger.warning(

f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "

"Picking 1024 instead. You can change that default value by passing --block_size xxx."

)

block_size = 1024

else:

if data_args.block_size > tokenizer.model_max_length:

logger.warning(

f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"

f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."

)

block_size = min(data_args.block_size, tokenizer.model_max_length)

train_set = process_ds(train_set, tokenizer, model_args.overwrite_cache, block_size)

dev_set = process_ds(dev_set, tokenizer, model_args.overwrite_cache, block_size)

batchify_fn = DataCollatorWithPadding(tokenizer, return_attention_mask=True)

trainer = Trainer(

model=model,

args=training_args,

train_dataset=train_set if training_args.do_train else None,

eval_dataset=dev_set if training_args.do_eval else None,

tokenizer=tokenizer,

data_collator=batchify_fn,

compute_metrics=compute_metrics,

)

if training_args.do_train:

train_results = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)

metrics = train_results.metrics

trainer.save_model()

trainer.log_metrics("train", metrics)

trainer.save_metrics("train", metrics)

trainer.save_state()

if training_args.do_eval:

eval_metrics = trainer.evaluate()

trainer.log_metrics("eval", eval_metrics)

if __name__ == "__main__":

do_train()