MobiLlama / fastchat /train /train_baichuan.py
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# This code is based on tatsu-lab/stanford_alpaca. Below is the original copyright:
#
# 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.
from dataclasses import dataclass, field
import json
import math
import jsonlines
import pathlib
from multiprocessing import Pool
from typing import Dict, Optional, Sequence
import numpy as np
import torch
from torch.utils.data import Dataset
import transformers
from transformers import Trainer
from transformers.trainer_pt_utils import LabelSmoother
from fastchat.conversation import SeparatorStyle
from fastchat.model.model_adapter import get_conversation_template
IGNORE_TOKEN_ID = LabelSmoother.ignore_index
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
@dataclass
class DataArguments:
data_path: str = field(
default=None, metadata={"help": "Path to the training data."}
)
lazy_preprocess: bool = False
@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)."
},
)
local_rank = None
def rank0_print(*args):
if local_rank == 0:
print(*args)
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 apply_prompt_template(sources, systems=None):
conv = get_conversation_template("vicuna")
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
conversations = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
conv.append_message(role, sentence["value"])
if systems and systems[i]:
conv.set_system_message(systems[i])
prompt = conv.get_prompt()
conversations.append(prompt)
return conversations, conv
def tokenize_conversations(conversations, tokenizer):
input_ids = tokenizer(
conversations,
return_tensors="pt",
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets = input_ids.clone()
return input_ids, targets
def mask_targets(conversations, targets, tokenizer, conv):
sep = conv.sep + conv.roles[1] + ": "
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
turns = conversation.split(conv.sep2)
cur_len = 0
target[:cur_len] = IGNORE_TOKEN_ID
for i, turn in enumerate(turns):
if turn == "":
break
turn_len = len(tokenizer(turn + conv.sep2).input_ids)
parts = turn.split(sep)
if len(parts) != 2:
break
parts[0] += sep
instruction_len = len(tokenizer(parts[0]).input_ids) - 1
target[cur_len : cur_len + instruction_len] = IGNORE_TOKEN_ID
cur_len += turn_len
target[cur_len:] = IGNORE_TOKEN_ID
if False: # Inspect and check the correctness of masking
z = target.clone()
z = torch.where(z == IGNORE_TOKEN_ID, tokenizer.unk_token_id, z)
rank0_print(tokenizer.decode(z))
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_TOKEN_ID
rank0_print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
return targets
def preprocess(sources, tokenizer: transformers.PreTrainedTokenizer, **kwargs) -> Dict:
systems = None if not kwargs else kwargs.get("systems", None)
# If the data volume is small, process it directly in the main thread
if len(sources) <= 1000:
conversations, conv = apply_prompt_template(sources, systems)
input_ids, targets = tokenize_conversations(conversations, tokenizer)
targets = mask_targets(conversations, targets, tokenizer, conv)
else: # If the data volume is large, use multithreading for processing
with Pool() as p:
conversations, conv = p.apply_async(
apply_prompt_template, (sources, systems)
).get()
input_ids, targets = p.apply_async(
tokenize_conversations, (conversations, tokenizer)
).get()
targets = p.apply_async(
mask_targets, (conversations, targets, tokenizer, conv)
).get()
p.close()
p.join()
return dict(
input_ids=input_ids,
labels=targets,
attention_mask=input_ids.ne(tokenizer.pad_token_id),
)
class SupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer):
super(SupervisedDataset, self).__init__()
rank0_print("Formatting inputs...")
systems = [example.get("system", "") for example in raw_data]
sources = [example["conversations"] for example in raw_data]
data_dict = preprocess(sources, tokenizer, systems=systems)
self.input_ids = data_dict["input_ids"]
self.labels = data_dict["labels"]
self.attention_mask = data_dict["attention_mask"]
def __len__(self):
return len(self.input_ids)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
return dict(
input_ids=self.input_ids[i],
labels=self.labels[i],
attention_mask=self.attention_mask[i],
)
class LazySupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer):
super(LazySupervisedDataset, self).__init__()
self.tokenizer = tokenizer
rank0_print("Formatting inputs...Skip in lazy mode")
self.raw_data = raw_data
self.cached_data_dict = {}
def __len__(self):
return len(self.raw_data)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
if i in self.cached_data_dict:
return self.cached_data_dict[i]
ret = preprocess(
[self.raw_data[i]["conversations"]],
self.tokenizer,
systems=[self.raw_data[i].get("system", "")],
)
ret = dict(
input_ids=ret["input_ids"][0],
labels=ret["labels"][0],
attention_mask=ret["attention_mask"][0],
)
self.cached_data_dict[i] = ret
return ret
def make_supervised_data_module(
tokenizer: transformers.PreTrainedTokenizer, data_args, train_ratio=0.98
) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
train_ratio = min(train_ratio, 1.0)
dataset_cls = (
LazySupervisedDataset if data_args.lazy_preprocess else SupervisedDataset
)
rank0_print("Loading data...")
data_path = data_args.data_path
if data_path.endswith(".json"):
raw_data = json.load(open(data_path, "r"))
elif data_path.endswith(".jsonl"):
with jsonlines.open(data_path, mode="r") as reader:
raw_data = [item for item in reader]
# Split train/test
np.random.seed(0)
perm = np.random.permutation(len(raw_data))
split = int(len(perm) * train_ratio)
train_indices = perm[:split]
if train_ratio < 1:
eval_indices = perm[split:]
else:
# if train_ratio==1, we use 5% of data as eval data, make sure trainer will not throw error when eval data is empty
eval_indices = perm[-int(len(perm) * 0.05) :]
train_raw_data = [raw_data[i] for i in train_indices]
eval_raw_data = [raw_data[i] for i in eval_indices]
rank0_print(f"#train {len(train_raw_data)}, #eval {len(eval_raw_data)}")
train_dataset = dataset_cls(train_raw_data, tokenizer=tokenizer)
eval_dataset = dataset_cls(eval_raw_data, tokenizer=tokenizer)
return dict(train_dataset=train_dataset, eval_dataset=eval_dataset)
def train():
global local_rank
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments)
)
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
local_rank = training_args.local_rank
config = transformers.AutoConfig.from_pretrained(
model_args.model_name_or_path,
trust_remote_code=True,
cache_dir=training_args.cache_dir,
)
# Set RoPE scaling factor
orig_ctx_len = getattr(config, "max_position_embeddings", None)
if orig_ctx_len and training_args.model_max_length > orig_ctx_len:
scaling_factor = float(math.ceil(training_args.model_max_length / orig_ctx_len))
config.rope_scaling = {"type": "linear", "factor": scaling_factor}
config.use_cache = False
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=config,
trust_remote_code=True,
cache_dir=training_args.cache_dir,
)
# Tie the weights
model.tie_weights()
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
config=config,
trust_remote_code=True,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=False,
)
# NOTE: if the token_id exceed the vocab_size will cause failing in training process! we need add special config and resize the embedding size!
tokenizer.pad_token = tokenizer.unk_token
print(f"tokens len: {len(tokenizer)}")
model.resize_token_embeddings(len(tokenizer))
data_module = make_supervised_data_module(
tokenizer=tokenizer, train_ratio=0.98, data_args=data_args
)
trainer = Trainer(
model=model, tokenizer=tokenizer, args=training_args, **data_module
)
if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
trainer.train(resume_from_checkpoint=True)
else:
trainer.train()
trainer.save_state()
safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)
if __name__ == "__main__":
train()