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Configuration for the Music Generation LLM.
Tuned for constrained hardware (<=8GB VRAM, <=16GB RAM).
"""
from dataclasses import dataclass, field
from pathlib import Path
import torch
@dataclass
class ModelConfig:
vocab_size: int = 0 # Set dynamically from tokenizer
dim: int = 256
n_layers: int = 6
n_heads: int = 8
n_kv_heads: int = 4 # Grouped Query Attention: fewer KV heads saves memory
max_seq_len: int = 1024
hidden_dim: int = 0 # Auto-calculated as 4 * dim * 2/3 rounded to multiple of 64
dropout: float = 0.1
rope_theta: float = 10000.0
def __post_init__(self):
if self.hidden_dim == 0:
# SwiGLU hidden dim: 4 * dim * 2/3 (LLaMA convention)
self.hidden_dim = int(2 * (4 * self.dim) / 3)
# Round to nearest multiple of 64 for hardware efficiency
self.hidden_dim = 64 * ((self.hidden_dim + 63) // 64)
@dataclass
class TrainConfig:
batch_size: int = 8
grad_accum_steps: int = 4 # Effective batch = 32
learning_rate: float = 3e-4
weight_decay: float = 0.1
max_epochs: int = 50
warmup_steps: int = 200
max_grad_norm: float = 1.0
use_amp: bool = True # Mixed precision to save memory
grad_checkpoint: bool = True # Gradient checkpointing for OOM prevention
eval_interval: int = 500
save_interval: int = 1000
log_interval: int = 50
patience: int = 10 # Early stopping patience (epochs)
min_delta: float = 0.001 # Minimum improvement for early stopping
num_workers: int = 2 # DataLoader workers (low for constrained RAM)
pin_memory: bool = True
prefetch_factor: int = 2
@dataclass
class DataConfig:
dataset_name: str = "drengskapur/midi-classical-music"
max_seq_len: int = 1024
train_split: float = 0.9
val_split: float = 0.1
tokenizer_params: str = "REMI" # REMI tokenization — SOTA for symbolic music
@dataclass
class GenConfig:
temperature: float = 0.85
top_k: int = 40
top_p: float = 0.92
max_tokens: int = 1024
repetition_penalty: float = 1.15
seed: int = 42
@dataclass
class PathConfig:
base_dir: Path = field(default_factory=lambda: Path(__file__).resolve().parent.parent)
data_dir: Path = field(init=False)
checkpoint_dir: Path = field(init=False)
output_dir: Path = field(init=False)
log_dir: Path = field(init=False)
tokenizer_path: Path = field(init=False)
def __post_init__(self):
self.data_dir = self.base_dir / "data"
self.checkpoint_dir = self.base_dir / "checkpoints"
self.output_dir = self.base_dir / "output"
self.log_dir = self.base_dir / "runs"
self.tokenizer_path = self.data_dir / "tokenizer.json"
for d in [self.data_dir, self.checkpoint_dir, self.output_dir, self.log_dir]:
d.mkdir(parents=True, exist_ok=True)
def get_device() -> torch.device:
if torch.cuda.is_available():
return torch.device("cuda")
return torch.device("cpu")
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