import torch
from datasets import load_dataset
from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments

"""
Please note that A100 or later generation GPUs are required to finetune Phi-3 models
1. Install accelerate: 
    conda install -c conda-forge accelerate
2. Setup accelerate config:
    accelerate config
to simply use all the GPUs available:
    python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='bf16')"
check accelerate config:
    accelerate env
3. Run the code:
    accelerate launch phi3-mini-sample-ft.py
"""

###################
# Hyper-parameters
###################


args = {
    "bf16": True,
    "do_eval": False,
    "evaluation_strategy": "no",
    "eval_steps": 100,
    "learning_rate": 5.0e-06,
    "log_level": "info",
    "logging_steps": 20,
    "logging_strategy": "steps",
    "lr_scheduler_type": "cosine",
    "num_train_epochs": 1,
    "max_steps": -1,
    "output_dir": ".",
    "overwrite_output_dir": True,
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 8,
    "remove_unused_columns": True,
    "save_steps": 100,
    "save_total_limit": 1,
    "seed": 0,
    "gradient_checkpointing": True,
    "gradient_accumulation_steps": 1,
    "warmup_ratio": 0.1,
    }
    
training_args = TrainingArguments(**args)


################
# Modle Loading
################

checkpoint_path = "microsoft/Phi-3-mini-128k-instruct"
model_kwargs = dict(
    trust_remote_code=True,
    attn_implementation="flash_attention_2",  # load the model with flash-attenstion support
    torch_dtype=torch.bfloat16,
    device_map="cuda",
)
model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, trust_remote_code=True)

################
# Data Loading
################

dataset = load_dataset("imdb")
train_dataset = dataset["train"]
eval_dataset = dataset["test"]


################
# Training
################

trainer = SFTTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    max_seq_length=2048,
    dataset_text_field="text",
    tokenizer=tokenizer,
)
train_result = trainer.train()
metrics = train_result.metrics
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()