WizardLM-1.6 / WizardCoder /src /train_wizardcoder.py
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
# 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 copy
import logging
import random
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence
import torch
import torch.distributed
import transformers
from torch.utils.data import Dataset
from transformers import Trainer
from datasets import load_dataset
import utils
IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "<|endoftext|>"
DEFAULT_BOS_TOKEN = "<|endoftext|>"
DEFAULT_UNK_TOKEN = "<|endoftext|>"
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="bigcode/starcoder")
@dataclass
class DataArguments:
data_path: str = field(default=None, metadata={"help": "Path to the training data."})
@dataclass
class TrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
)
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
"""Collects the state dict and dump to disk."""
state_dict = trainer.model.state_dict()
if trainer.args.should_save:
cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
del state_dict
trainer._save(output_dir, state_dict=cpu_state_dict) # noqa
def smart_tokenizer_and_embedding_resize(
special_tokens_dict: Dict,
tokenizer: transformers.PreTrainedTokenizer,
model: transformers.PreTrainedModel,
):
"""Resize tokenizer and embedding.
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
"""
num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = model.get_input_embeddings().weight.data
output_embeddings = model.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict:
"""Tokenize a list of strings."""
tokenized_list = [
tokenizer(
text,
return_tensors="pt",
padding="longest",
max_length=tokenizer.model_max_length,
truncation=True,
)
for text in strings
]
input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list]
input_ids_lens = labels_lens = [
tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
]
return dict(
input_ids=input_ids,
labels=labels,
input_ids_lens=input_ids_lens,
labels_lens=labels_lens,
)
def preprocess(
sources: Sequence[str],
targets: Sequence[str],
tokenizer: transformers.PreTrainedTokenizer,
) -> Dict:
"""Preprocess the data by tokenizing."""
examples = [s + t for s, t in zip(sources, targets)]
examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)]
input_ids = examples_tokenized["input_ids"]
labels = copy.deepcopy(input_ids)
for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]):
label[:source_len] = IGNORE_INDEX
return dict(input_ids=input_ids, labels=labels)
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
input_ids = [torch.tensor(x) for x in input_ids]
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
)
labels = [torch.tensor(x) for x in labels]
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
return dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
)
def train_tokenize_function(examples, tokenizer):
prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"]
if 'input' in examples:
sources = [
prompt_input.format_map(dict(instruction=instruction, input=input)) if input != "" \
else prompt_no_input.format_map(dict(instruction=instruction)) \
for instruction, input in zip(examples['instruction'], examples['input'])
]
else:
sources = [
prompt_no_input.format_map(dict(instruction=instruction)) \
for instruction in examples['instruction']
]
targets = [f"{output}{tokenizer.eos_token}" for output in examples['output']]
data_dict = preprocess(sources, targets, tokenizer)
return data_dict
def train():
parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=True,
)
if tokenizer.pad_token is None:
smart_tokenizer_and_embedding_resize(
special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
tokenizer=tokenizer,
model=model,
)
if "starcoder" in model_args.model_name_or_path:
tokenizer.add_special_tokens(
{
"eos_token": DEFAULT_EOS_TOKEN,
"bos_token": DEFAULT_BOS_TOKEN,
"unk_token": DEFAULT_UNK_TOKEN,
"pad_token": DEFAULT_PAD_TOKEN,
}
)
raw_train_datasets = load_dataset('json', data_files=data_args.data_path, split="train", cache_dir=training_args.cache_dir)
if training_args.local_rank > 0:
torch.distributed.barrier()
train_dataset = raw_train_datasets.map(
train_tokenize_function,
batched=True,
batch_size=3000,
num_proc=32,
remove_columns=raw_train_datasets.column_names,
load_from_cache_file=True, # not args.overwrite_cache
desc="Running tokenizer on train dataset",
fn_kwargs={"tokenizer": tokenizer}
)
if training_args.local_rank == 0:
torch.distributed.barrier()
if training_args.local_rank == 0:
print(len(train_dataset))
for index in random.sample(range(len(train_dataset)), 3):
print(f"Sample {index} of the training set: {train_dataset[index]}.")
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
data_module = dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator)
#Tell Trainer not to attempt DataParallel
model.is_parallelizable = True
model.model_parallel = True
trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
model.config.use_cache = False
trainer.train()
trainer.save_state()
safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)
if __name__ == "__main__":
train()