trainer_code.py

from huggingface_hub import *

# create_repo(repo_id="test-model")
import pandas as pd

from datasets import load_dataset


df_train = pd.read_csv("/home/prafull/apps_all/flan_tuning/FlanT5-train-test-idiomSimplifier.csv")
complex_sentences = df_train["Idiom sentences"].to_list()
simple_sentences = df_train["English casual"].to_list()

data_dict = {
    "dialogue": complex_sentences,
    "summary": simple_sentences
}

df_train_new = pd.DataFrame(data_dict)
# random shuffling
df_train_shuffled = df_train_new.sample(frac = 1, random_state=1)
# # Save pre-processed final data
df_train_shuffled.head(1000).to_csv("dialog_summary.csv", encoding="utf-8", index=False)

dataset = load_dataset("csv", data_files="dialog_summary.csv", split='train') 

dataset = dataset.train_test_split(test_size=0.05)
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id="google/flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)

from datasets import concatenate_datasets

# The maximum total input sequence length after tokenization.
# Sequences longer than this will be truncated, sequences shorter will be padded.
tokenized_inputs = concatenate_datasets([dataset["train"], dataset["test"]]).map(lambda x: tokenizer(x["dialogue"], truncation=True), batched=True, remove_columns=["dialogue", "summary"])
max_source_length = max([len(x) for x in tokenized_inputs["input_ids"]])
print(f"Max source length: {max_source_length}")

max_target_length = max_source_length + 10
print(f"Max Target length: {max_target_length}")


def preprocess_function(sample,padding="max_length"):
    # add prefix to the input for t5
    inputs = ["Easy to understand Sentence without idioms and jargons: " + item for item in sample["dialogue"]]

    # tokenize inputs
    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)

    # Tokenize targets with the `text_target` keyword argument
    labels = tokenizer(text_target=sample["summary"], max_length=max_target_length, padding=padding, truncation=True)

    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
    # padding in the loss.
    if padding == "max_length":
        labels["input_ids"] = [
            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
        ]

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=["dialogue", "summary"])
print(f"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}")


from transformers import AutoModelForSeq2SeqLM

# huggingface hub model id
model_id="google/flan-t5-base"

# load model from the hub
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

import evaluate
import nltk
import numpy as np
from nltk.tokenize import sent_tokenize

# Metric
metric = evaluate.load("rouge")

# helper function to postprocess text
def postprocess_text(preds, labels):
    preds = [pred.strip() for pred in preds]
    labels = [label.strip() for label in labels]

    # rougeLSum expects newline after each sentence
    preds = ["\n".join(sent_tokenize(pred)) for pred in preds]
    labels = ["\n".join(sent_tokenize(label)) for label in labels]

    return preds, labels

def compute_metrics(eval_preds):
    preds, labels = eval_preds
    if isinstance(preds, tuple):
        preds = preds[0]
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
    # Replace -100 in the labels as we can't decode them.
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    # Some simple post-processing
    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
    result = {k: round(v * 100, 4) for k, v in result.items()}
    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
    result["gen_len"] = np.mean(prediction_lens)
    return result


from transformers import DataCollatorForSeq2Seq

# we want to ignore tokenizer pad token in the loss
label_pad_token_id = -100
# Data collator
data_collator = DataCollatorForSeq2Seq(
    tokenizer,
    model=model,
    label_pad_token_id=label_pad_token_id,
    pad_to_multiple_of=8
)

import torch

torch.cuda.set_device(0)
print(torch.cuda.current_device())

from huggingface_hub import HfFolder
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments



repository_id = f"flan-tuning"


# Define training args
training_args = Seq2SeqTrainingArguments(
    overwrite_output_dir=True,
    output_dir=repository_id,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    fp16=False, # Overflows with fp16
    learning_rate=5e-5,
    num_train_epochs=1,
    # logging & evaluation strategies
    logging_dir=f"{repository_id}/logs",
    logging_strategy="steps",
    logging_steps=500,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    save_total_limit=2,
    load_best_model_at_end=True,
    # metric_for_best_model="overall_f1",
    # push to hub parameters
    report_to="tensorboard",
    push_to_hub=False,
    hub_strategy="every_save",
    hub_model_id=repository_id,
    hub_token=HfFolder.get_token(),
)

# Create Trainer instance
trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["test"],
    compute_metrics=compute_metrics,
)

trainer.train()

# trainer.model.save_pretrained("/home/prafull/apps_all/ChatGPT_Playground/Flan_models/flan-t5-LARGE-IDIOM-24k", from_pt=True)
# tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")


# PUSH TO HUB ------------

# Save our tokenizer and create model card
tokenizer.save_pretrained(repository_id)
trainer.create_model_card()
# Push the results to the hub
trainer.push_to_hub()