llm_toolkit/tune_logical_reasoning.py

import os
import sys
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from trl import SFTTrainer
from transformers import TrainingArguments

from dotenv import find_dotenv, load_dotenv

found_dotenv = find_dotenv(".env")

if len(found_dotenv) == 0:
    found_dotenv = find_dotenv(".env.example")
print(f"loading env vars from: {found_dotenv}")
load_dotenv(found_dotenv, override=False)

path = os.path.dirname(found_dotenv)
print(f"Adding {path} to sys.path")
sys.path.append(path)

from llm_toolkit.logical_reasoning_utils import *
from llm_toolkit.llm_utils import *

model_name = os.getenv("MODEL_NAME")
token = os.getenv("HF_TOKEN") or None
load_in_4bit = os.getenv("LOAD_IN_4BIT") == "true"
local_model = os.getenv("LOCAL_MODEL") or "gemma-2-9b-it-lora"
hub_model = os.getenv("HUB_MODEL") or "inflaton-ai/gemma-2-9b-it-lora"
num_train_epochs = int(os.getenv("NUM_TRAIN_EPOCHS") or 0)
data_path = os.getenv("LOGICAL_REASONING_DATA_PATH")
results_path = os.getenv("LOGICAL_REASONING_RESULTS_PATH")

print(model_name, load_in_4bit, data_path, results_path)

max_seq_length = 4096  # Choose any! We auto support RoPE Scaling internally!
dtype = (
    None  # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
)

gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(1) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
)

gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(2) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")

model = FastLanguageModel.get_peft_model(
    model,
    r=16,  # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
    target_modules=[
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj",
        "gate_proj",
        "up_proj",
        "down_proj",
    ],
    lora_alpha=16,
    lora_dropout=0,  # Supports any, but = 0 is optimized
    bias="none",  # Supports any, but = "none" is optimized
    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
    use_gradient_checkpointing="unsloth",  # True or "unsloth" for very long context
    random_state=3407,
    use_rslora=False,  # We support rank stabilized LoRA
    loftq_config=None,  # And LoftQ
)

gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(3) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")

dataset = load_logical_reasoning_dataset(data_path, tokenizer=tokenizer, using_p1=False)
print_row_details(dataset["train"].to_pandas())

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset["train"],
    dataset_text_field="train_text",
    max_seq_length=max_seq_length,
    dataset_num_proc=2,
    packing=False,  # Can make training 5x faster for short sequences.
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        warmup_steps=5,
        max_steps=20000,
        learning_rate=2e-4,
        fp16=not is_bfloat16_supported(),
        bf16=is_bfloat16_supported(),
        logging_steps=100,
        optim="adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="linear",
        seed=3407,
        output_dir="outputs",
    ),
)

gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(4) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")

trainer_stats = trainer.train()

# @title Show final memory and time stats
used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
used_memory_for_lora = round(used_memory - start_gpu_memory, 3)
used_percentage = round(used_memory / max_memory * 100, 3)
lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)
print(f"(5) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
print(
    f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training."
)
print(f"Peak reserved memory = {used_memory} GB.")
print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
print(f"Peak reserved memory % of max memory = {used_percentage} %.")
print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")

model.save_pretrained(local_model)  # Local saving
tokenizer.save_pretrained(local_model)

print("Evaluating fine-tuned model: " + model_name)
FastLanguageModel.for_inference(model)  # Enable native 2x faster inference
predictions = eval_model(model, tokenizer, dataset["test"])

gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(6) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")

save_results(
    model_name + "(unsloth_finetuned)",
    results_path,
    dataset["test"],
    predictions,
    debug=True,
)

metrics = calc_metrics(dataset["test"]["label"], predictions, debug=True)
print(metrics)

model.push_to_hub(hub_model, token=token)  # Online saving
tokenizer.push_to_hub(hub_model, token=token)  # Online saving