File size: 11,004 Bytes
2ff5c54 5fb9ec3 2ff5c54 5fb9ec3 2ff5c54 5fb9ec3 2ff5c54 5fb9ec3 2ff5c54 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 | """
MINDI 1.5 Vision-Coder — Model Architecture
Loads Qwen/Qwen2.5-Coder-7B-Instruct with LoRA adapters.
Handles model initialization, LoRA application, save/load,
and parameter counting for the base LLM component.
"""
from __future__ import annotations
from pathlib import Path
from typing import Optional
import torch
from peft import LoraConfig, PeftModel, TaskType, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
class MINDIArchitecture:
"""Qwen2.5-Coder-7B-Instruct with LoRA for MINDI 1.5 fine-tuning."""
DEFAULT_TARGET_MODULES: list[str] = [
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",
]
def __init__(
self,
model_name: str = "Qwen/Qwen2.5-Coder-7B-Instruct",
device: Optional[str] = None,
cache_dir: Optional[Path] = None,
torch_dtype: torch.dtype = torch.bfloat16,
) -> None:
"""
Initialize the architecture wrapper.
Args:
model_name: HuggingFace model identifier.
device: Target device ('cuda', 'cpu', or None for auto).
cache_dir: Local directory for model weight cache.
torch_dtype: Data type for model weights.
"""
self.model_name = model_name
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.cache_dir = Path(cache_dir) if cache_dir else Path("./checkpoints/base")
self.cache_dir.mkdir(parents=True, exist_ok=True)
self.torch_dtype = torch_dtype
self.model: Optional[AutoModelForCausalLM] = None
self.peft_model: Optional[PeftModel] = None
self.tokenizer: Optional[AutoTokenizer] = None
self._load_model()
def _load_model(self) -> None:
"""Load the base model and tokenizer from HuggingFace or cache."""
print(f"[MINDIArchitecture] Loading {self.model_name} ...")
if self.device == "cuda":
# Clear GPU state before loading
torch.cuda.empty_cache()
torch.cuda.synchronize()
print(f"[MINDIArchitecture] GPU cleared, loading to CPU first ...")
self.model = AutoModelForCausalLM.from_pretrained(
self.model_name,
torch_dtype=self.torch_dtype,
device_map=None,
trust_remote_code=True,
low_cpu_mem_usage=True,
)
param_count = sum(p.numel() for p in self.model.parameters())
print(f"[MINDIArchitecture] CPU load done ({param_count / 1e9:.2f}B params)")
if self.device == "cuda":
print(f"[MINDIArchitecture] Moving to CUDA ...")
self.model = self.model.to("cuda")
torch.cuda.synchronize()
vram_gb = torch.cuda.memory_allocated() / (1024**3)
print(f"[MINDIArchitecture] CUDA transfer done ({vram_gb:.1f} GB VRAM)")
self.tokenizer = AutoTokenizer.from_pretrained(
self.model_name,
trust_remote_code=True,
)
print(f"[MINDIArchitecture] Loaded on {self.device} "
f"({self._fmt_params(self._total_params())} params)")
def apply_lora(
self,
r: int = 64,
lora_alpha: int = 128,
lora_dropout: float = 0.05,
target_modules: Optional[list[str]] = None,
) -> PeftModel:
"""
Apply LoRA adapters to the base model.
Args:
r: LoRA rank.
lora_alpha: LoRA scaling factor.
lora_dropout: Dropout probability for LoRA layers.
target_modules: List of module names to apply LoRA to.
Returns:
The PEFT-wrapped model.
"""
if self.model is None:
raise RuntimeError("Base model not loaded.")
if target_modules is None:
target_modules = self.DEFAULT_TARGET_MODULES
lora_config = LoraConfig(
r=r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=target_modules,
bias="none",
task_type=TaskType.CAUSAL_LM,
)
self.peft_model = get_peft_model(self.model, lora_config)
info = self.get_trainable_params()
print(f"[MINDIArchitecture] LoRA applied (r={r}, alpha={lora_alpha})")
print(f" Trainable: {info['trainable']:>14,} ({info['trainable_pct']:.2f}%)")
print(f" Frozen: {info['frozen']:>14,}")
print(f" Total: {info['total']:>14,}")
return self.peft_model
def get_trainable_params(self) -> dict:
"""
Count trainable, frozen, and total parameters.
Returns:
Dictionary with 'trainable', 'frozen', 'total', 'trainable_pct'.
"""
model = self.peft_model or self.model
if model is None:
return {"trainable": 0, "frozen": 0, "total": 0, "trainable_pct": 0.0}
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
total = sum(p.numel() for p in model.parameters())
frozen = total - trainable
pct = 100.0 * trainable / total if total > 0 else 0.0
return {
"trainable": trainable,
"frozen": frozen,
"total": total,
"trainable_pct": round(pct, 4),
}
def print_model_info(self) -> None:
"""Print detailed model architecture and parameter information."""
model = self.peft_model or self.model
if model is None:
print("[MINDIArchitecture] No model loaded.")
return
info = self.get_trainable_params()
print()
print("=" * 60)
print(" MINDI 1.5 — Model Architecture Info")
print("=" * 60)
print(f" Base model: {self.model_name}")
print(f" Device: {self.device}")
print(f" Dtype: {self.torch_dtype}")
print(f" LoRA active: {self.peft_model is not None}")
print(f" Total params: {self._fmt_params(info['total'])}")
print(f" Trainable: {self._fmt_params(info['trainable'])} "
f"({info['trainable_pct']:.2f}%)")
print(f" Frozen: {self._fmt_params(info['frozen'])}")
if self.peft_model is not None:
config = self.peft_model.peft_config.get("default")
if config is not None:
print(f" LoRA rank: {config.r}")
print(f" LoRA alpha: {config.lora_alpha}")
print(f" LoRA dropout: {config.lora_dropout}")
print(f" Target modules: {config.target_modules}")
print("=" * 60)
print()
def save_lora(self, path: Optional[Path] = None) -> Path:
"""
Save LoRA adapter weights to disk.
Args:
path: Directory to save to. Defaults to checkpoints/lora.
Returns:
Path where weights were saved.
"""
if self.peft_model is None:
raise RuntimeError("No LoRA adapter to save. Call apply_lora() first.")
save_path = Path(path) if path else Path("./checkpoints/lora")
save_path.mkdir(parents=True, exist_ok=True)
self.peft_model.save_pretrained(str(save_path))
print(f"[MINDIArchitecture] LoRA saved to {save_path}")
return save_path
def load_lora(self, path: Path) -> PeftModel:
"""
Load LoRA adapter weights from disk.
Args:
path: Directory containing saved adapter weights.
Returns:
The PEFT-wrapped model with loaded adapter.
"""
path = Path(path)
if not path.exists():
raise FileNotFoundError(f"LoRA adapter not found: {path}")
if self.model is None:
raise RuntimeError("Base model not loaded.")
self.peft_model = PeftModel.from_pretrained(
self.model, str(path)
)
print(f"[MINDIArchitecture] LoRA loaded from {path}")
return self.peft_model
def resize_embeddings(self, new_vocab_size: int) -> None:
"""Resize model embeddings for new special tokens."""
model = self.peft_model or self.model
if model is None:
raise RuntimeError("No model loaded.")
old_size = model.get_input_embeddings().weight.shape[0]
if new_vocab_size != old_size:
model.resize_token_embeddings(new_vocab_size)
print(f"[MINDIArchitecture] Resized embeddings: {old_size} → {new_vocab_size}")
def get_model(self) -> AutoModelForCausalLM | PeftModel:
"""Return the active model (PEFT if LoRA applied, else base)."""
model = self.peft_model or self.model
if model is None:
raise RuntimeError("No model loaded.")
return model
# ── helpers ───────────────────────────────────────────────────
def _total_params(self) -> int:
model = self.peft_model or self.model
if model is None:
return 0
return sum(p.numel() for p in model.parameters())
@staticmethod
def _fmt_params(n: int) -> str:
if n >= 1_000_000_000:
return f"{n / 1_000_000_000:.2f}B"
if n >= 1_000_000:
return f"{n / 1_000_000:.2f}M"
if n >= 1_000:
return f"{n / 1_000:.1f}K"
return str(n)
# ── Test block ────────────────────────────────────────────────────────
if __name__ == "__main__":
print("=" * 60)
print(" MINDI 1.5 — Architecture Test")
print("=" * 60)
print()
# 1. Load base model
arch = MINDIArchitecture(
model_name="Qwen/Qwen2.5-Coder-7B-Instruct",
)
# 2. Apply LoRA
peft_model = arch.apply_lora(
r=64,
lora_alpha=128,
lora_dropout=0.05,
)
# 3. Print full info
arch.print_model_info()
# 4. Verify trainable params
info = arch.get_trainable_params()
assert info["trainable"] > 0, "No trainable parameters!"
assert info["frozen"] > info["trainable"], "More trainable than frozen — LoRA may not be applied!"
# 5. Verify LoRA modules exist
lora_modules = [name for name, _ in peft_model.named_parameters() if "lora_" in name]
print(f" LoRA modules found: {len(lora_modules)}")
assert len(lora_modules) > 0, "No LoRA modules found!"
# 6. Quick forward pass test (small input)
print("\n Running forward pass test ...")
test_input = arch.tokenizer("Hello MINDI!", return_tensors="pt")
test_input = {k: v.to(arch.device) for k, v in test_input.items()}
with torch.no_grad():
output = peft_model(**test_input)
print(f" Output logits shape: {output.logits.shape}")
print(f" Loss: {output.loss}")
print("\n ✓ All architecture tests passed!")
print("=" * 60)
|