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from dataclasses import dataclass, field
from typing import Optional
import pandas as pd
import torch
from accelerate import Accelerator
from datasets import load_dataset, Dataset, load_metric
from peft import LoraConfig, get_peft_model
from tqdm import tqdm
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, VisionEncoderDecoderModel, TrOCRProcessor, Seq2SeqTrainer, Seq2SeqTrainingArguments, default_data_collator, EarlyStoppingCallback
# from trl import SFTTrainer, is_xpu_available
from data import AphaPenDataset
import evaluate
from sklearn.model_selection import train_test_split
import torchvision.transforms as transforms
# from utils import compute_metrics
from src.calibrator import EncoderDecoderCalibrator
from src.loss import MarginLoss, KLRegularization
from src.similarity import CERSimilarity
import os
tqdm.pandas()
os.environ["WANDB_PROJECT"]="Alphapen"
# Define and parse arguments.
@dataclass
class ScriptArguments:
"""
The name of the OCR model we wish to fine with Seq2SeqTrainer
"""
model_name: Optional[str] = field(default="microsoft/trocr-base-handwritten", metadata={"help": "the model name"})
dataset_name: Optional[str] = field(
default="Anthropic/hh-rlhf", metadata={"help": "the dataset name"}
)
log_with: Optional[str] = field(default="none", metadata={"help": "use 'wandb' to log with wandb"})
learning_rate: Optional[float] = field(default=1.41e-5, metadata={"help": "the learning rate"})
batch_size: Optional[int] = field(default=8, metadata={"help": "the batch size"})
seq_length: Optional[int] = field(default=512, metadata={"help": "Input sequence length"})
gradient_accumulation_steps: Optional[int] = field(
default=16, metadata={"help": "the number of gradient accumulation steps"}
)
load_in_8bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 8 bits precision"})
load_in_4bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 4 bits precision"})
use_peft: Optional[bool] = field(default=False, metadata={"help": "Wether to use PEFT or not to train adapters"})
trust_remote_code: Optional[bool] = field(default=False, metadata={"help": "Enable `trust_remote_code`"})
output_dir: Optional[str] = field(default="output", metadata={"help": "the output directory"})
peft_lora_r: Optional[int] = field(default=64, metadata={"help": "the r parameter of the LoRA adapters"})
peft_lora_alpha: Optional[int] = field(default=16, metadata={"help": "the alpha parameter of the LoRA adapters"})
logging_steps: Optional[int] = field(default=1, metadata={"help": "the number of logging steps"})
use_auth_token: Optional[bool] = field(default=True, metadata={"help": "Use HF auth token to access the model"})
num_train_epochs: Optional[int] = field(default=3, metadata={"help": "the number of training epochs"})
max_steps: Optional[int] = field(default=-1, metadata={"help": "the number of training steps"})
max_length: Optional[int] = field(default=10, metadata={"help": "the maximum length"})
no_repeat_ngram_size: Optional[int] = field(default=3, metadata={"help": "the number of repeat"})
length_penalty: Optional[float] = field(default=2.0, metadata={"help": "the length of penalty"})
num_beams: Optional[int] = field(default=3, metadata={"help": "the number of beam search"})
early_stopping: Optional[bool] = field(default=True, metadata={"help": "Early stopping"})
save_steps: Optional[int] = field(
default=1000, metadata={"help": "Number of updates steps before two checkpoint saves"}
)
save_total_limit: Optional[int] = field(default=10, metadata={"help": "Limits total number of checkpoints."})
push_to_hub: Optional[bool] = field(default=False, metadata={"help": "Push the model to HF Hub"})
gradient_checkpointing: Optional[bool] = field(
default=False, metadata={"help": "Whether to use gradient checkpointing or no"}
)
gradient_checkpointing_kwargs: Optional[dict] = field(
default=None,
metadata={
"help": "key word arguments to be passed along `torch.utils.checkpoint.checkpoint` method - e.g. `use_reentrant=False`"
},
)
hub_model_id: Optional[str] = field(default=None, metadata={"help": "The name of the model on HF Hub"})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
# # Step 1: Load the dataset
df_path = "/mnt/data1/Datasets/AlphaPen/" + "training_data.csv"
df = pd.read_csv(df_path)
df.dropna(inplace=True)
train_df, test_df = train_test_split(df, test_size=0.15, random_state=0)
# we reset the indices to start from zero
train_df.reset_index(drop=True, inplace=True)
test_df.reset_index(drop=True, inplace=True)
root_dir = "/mnt/data1/Datasets/OCR/Alphapen/clean_data/final_cropped_rotated_"
processor = TrOCRProcessor.from_pretrained(script_args.model_name)
train_dataset = AphaPenDataset(root_dir=root_dir, df=train_df, processor=processor)
eval_dataset = AphaPenDataset(root_dir=root_dir, df=test_df.iloc[:10,:], processor=processor)
# Step 2: Load the model
# if script_args.load_in_8bit and script_args.load_in_4bit:
# raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
# elif script_args.load_in_8bit or script_args.load_in_4bit:
# quantization_config = BitsAndBytesConfig(
# load_in_8bit=script_args.load_in_8bit, load_in_4bit=script_args.load_in_4bit
# )
# # Copy the model to each device
# device_map = (
# {"": f"xpu:{Accelerator().local_process_index}"}
# if is_xpu_available()
# else {"": Accelerator().local_process_index}
# )
# torch_dtype = torch.bfloat16
# else:
# device_map = None
# quantization_config = None
# torch_dtype = None
model = VisionEncoderDecoderModel.from_pretrained(
script_args.model_name,
#quantization_config=quantization_config,
device_map="cuda",
trust_remote_code=script_args.trust_remote_code,
torch_dtype=torch.bfloat16,
token=script_args.use_auth_token,
)
# set special tokens used for creating the decoder_input_ids from the labels
model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
model.config.pad_token_id = processor.tokenizer.pad_token_id
# make sure vocab size is set correctly
model.config.vocab_size = model.config.decoder.vocab_size
# set beam search parameters
model.config.eos_token_id = processor.tokenizer.sep_token_id
model.config.max_length = script_args.max_length
model.config.early_stopping = script_args.early_stopping
model.config.no_repeat_ngram_size = script_args.no_repeat_ngram_size
model.config.length_penalty = script_args.length_penalty
model.config.num_beams = script_args.num_beams
# LoRa
lora_config = LoraConfig(
r=script_args.peft_lora_r,
lora_alpha=script_args.peft_lora_alpha,
lora_dropout=0.1,
target_modules=[
'query',
'key',
'value',
'intermediate.dense',
'output.dense',
#'wte',
#'wpe',
#'c_attn',
#'c_proj',
#'q_attn',
#'c_fc'
],
)
model = get_peft_model(model, lora_config)
tokenizer = processor.tokenizer
sim = CERSimilarity(tokenizer)
loss = MarginLoss(sim, beta=0.1, num_samples=60)
reg = KLRegularization(model)
calibrator = EncoderDecoderCalibrator(model, loss, reg, 15, 15)
# # Step 3: Define the training arguments
training_args = Seq2SeqTrainingArguments(
predict_with_generate=True,
evaluation_strategy="steps",
per_device_train_batch_size=script_args.batch_size,
per_device_eval_batch_size=script_args.batch_size,
fp16=True,
output_dir=script_args.output_dir,
logging_steps=script_args.logging_steps,
save_steps=script_args.save_steps,
eval_steps=100,
save_total_limit=script_args.save_total_limit,
# load_best_model_at_end = True,
report_to=script_args.log_with,
num_train_epochs=script_args.num_train_epochs,
push_to_hub=script_args.push_to_hub,
hub_model_id=script_args.hub_model_id,
gradient_checkpointing=script_args.gradient_checkpointing,
# metric_for_best_model="eval/cer"
# TODO: uncomment that on the next release
# gradient_checkpointing_kwargs=script_args.gradient_checkpointing_kwargs,
)
# Step 4: Define a metric
# subclass trainer
class CustomTrainer(Seq2SeqTrainer):
def compute_loss(self, model, inputs, return_outputs=False):
tokenizer = processor.tokenizer
sim = CERSimilarity(tokenizer)
marginloss = MarginLoss(sim, beta=0.1, num_samples=60)
labels = inputs.pop("labels")
labels[labels == -100] = processor.tokenizer.pad_token_id
outputs = model.generate(**inputs, num_beams=4, do_sample=True, num_return_sequences=1, return_dict_in_generate=True, output_scores=True, output_logits=True)
# pred_str = processor.batch_decode(outputs, skip_special_tokens=True)
print(model.config)
print(outputs)
print(labels.shape)
# pred_str = processor.batch_decode(outputs, skip_special_tokens=True)
# print(pred_str)
loss = marginloss(outputs, labels)
# logits = outputs.logits
# loss = nll_loss(logits, labels)
return (loss, outputs) if return_outputs else loss
def compute_metrics(pred):
# accuracy_metric = evaluate.load("precision")
cer_metric = evaluate.load("cer")
labels_ids = pred.label_ids
pred_ids = pred.predictions
pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)
labels_ids[labels_ids == -100] = processor.tokenizer.pad_token_id
label_str = processor.batch_decode(labels_ids, skip_special_tokens=True)
cer = cer_metric.compute(predictions=pred_str, references=label_str)
# accuracy = accuracy_metric.compute(predictions=pred_ids.tolist(), references=labels_ids.tolist())
return {"cer": cer}
early_stop = EarlyStoppingCallback(10, .001)
# # Step 5: Define the Trainer
trainer = CustomTrainer(
model=model,
tokenizer=processor.feature_extractor,
args=training_args,
# compute_metrics=compute_metrics,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
data_collator=default_data_collator,
# callbacks = [early_stop]
)
trainer.train()
# # Step 6: Save the model
# trainer.save_model(script_args.output_dir)
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