train.py

"""
Parametric Floorplan Generation Model Training
Based on: DStruct2Design (arXiv:2407.15723) approach
Dataset: Custom synthetic dataset generated to match user's ProjectCreate schema
"""
import os
import json
import torch
from datasets import load_dataset, load_from_disk, DatasetDict
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, TaskType
from trl import SFTTrainer, SFTConfig

# -----------------------------------------------------------------------------
# Configuration
# -----------------------------------------------------------------------------
MODEL_ID = "Qwen/Qwen2.5-1.5B-Instruct"
OUTPUT_DIR = "/app/floorplan-model"
HUB_MODEL_ID = os.environ.get("HF_TRAINER_HUB_MODEL_ID", "Karthik8nitt/parametric-floorplan-generator")
DATASET_PATH = os.environ.get("DATASET_PATH", "/app/floorplan_synthetic_dataset")

# -----------------------------------------------------------------------------
# Load data
# -----------------------------------------------------------------------------
print("Loading dataset...")
if os.path.exists(DATASET_PATH):
    dataset = load_from_disk(DATASET_PATH)
else:
    # Fallback: load from HF if pre-uploaded
    dataset = load_dataset("Karthik8nitt/floorplan-synthetic-dataset")

print(f"Train: {len(dataset['train'])}, Val: {len(dataset['validation'])}, Test: {len(dataset['test'])}")

# -----------------------------------------------------------------------------
# Load tokenizer & model
# -----------------------------------------------------------------------------
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

# -----------------------------------------------------------------------------
# LoRA config
# -----------------------------------------------------------------------------
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)

# -----------------------------------------------------------------------------
# Training arguments
# -----------------------------------------------------------------------------
training_args = SFTConfig(
    output_dir=OUTPUT_DIR,
    num_train_epochs=5,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=1e-4,
    lr_scheduler_type="cosine",
    warmup_ratio=0.1,
    logging_steps=10,
    eval_strategy="steps",
    eval_steps=100,
    save_strategy="steps",
    save_steps=100,
    save_total_limit=3,
    max_seq_length=4096,
    bf16=True,
    gradient_checkpointing=True,
    report_to="trackio",
    run_name="floorplan-qwen1.5b-lora",
    project="parametric-floorplan",
    hub_model_id=HUB_MODEL_ID,
    push_to_hub=True,
    completion_only_loss=True,
    disable_tqdm=True,
    logging_first_step=True,
    seed=42,
)

# -----------------------------------------------------------------------------
# Trainer
# -----------------------------------------------------------------------------
trainer = SFTTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
    peft_config=peft_config,
    processing_class=tokenizer,
)

# -----------------------------------------------------------------------------
# Train
# -----------------------------------------------------------------------------
print("Starting training...")
trainer.train()

print("Saving and pushing model...")
trainer.save_model(os.path.join(OUTPUT_DIR, "final"))
trainer.push_to_hub()
print(f"Done! Model at https://huggingface.co/{HUB_MODEL_ID}")