File size: 3,280 Bytes
d5049a2 | 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 | from __future__ import annotations
from typing import List
import torch
from peft import LoraConfig, PeftModel, TaskType, get_peft_model
from transformers import AutoModelForMultimodalLM, AutoProcessor
VISION_MARKERS = ("visual", "vision", "image", "merger")
LORA_LEAF_NAMES = {
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
"in_proj_qkv",
"in_proj_z",
"in_proj_a",
"in_proj_b",
"out_proj",
}
def load_processor(model_path: str, local_files_only: bool = True):
return AutoProcessor.from_pretrained(
model_path,
trust_remote_code=True,
local_files_only=local_files_only,
)
def load_base_model(
model_path: str,
*,
attn_implementation: str = "sdpa",
local_files_only: bool = True,
):
return AutoModelForMultimodalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
attn_implementation=attn_implementation,
trust_remote_code=True,
local_files_only=local_files_only,
low_cpu_mem_usage=True,
)
def freeze_vision_parameters(model) -> int:
count = 0
for name, parameter in model.named_parameters():
if any(marker in name.lower() for marker in VISION_MARKERS):
parameter.requires_grad_(False)
count += parameter.numel()
return count
def discover_lora_targets(model) -> List[str]:
"""Return exact linear-module paths, excluding the vision tower and lm_head."""
targets: List[str] = []
for name, module in model.named_modules():
if not isinstance(module, torch.nn.Linear):
continue
lower = name.lower()
if any(marker in lower for marker in VISION_MARKERS) or lower.endswith("lm_head"):
continue
if name.rsplit(".", 1)[-1] in LORA_LEAF_NAMES:
targets.append(name)
if not targets:
raise RuntimeError(
"No supported LoRA targets were found. Print model.named_modules() and "
"update LORA_LEAF_NAMES for this local model revision."
)
return sorted(set(targets))
def prepare_trainable_model(
model,
*,
adapter_path: str | None,
lora_r: int,
lora_alpha: int,
lora_dropout: float,
freeze_vision: bool,
):
if freeze_vision:
freeze_vision_parameters(model)
model.config.use_cache = False
if hasattr(model, "gradient_checkpointing_enable"):
model.gradient_checkpointing_enable(
gradient_checkpointing_kwargs={"use_reentrant": False}
)
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
if adapter_path:
return PeftModel.from_pretrained(model, adapter_path, is_trainable=True)
targets = discover_lora_targets(model)
config = LoraConfig(
r=lora_r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
bias="none",
task_type=TaskType.CAUSAL_LM,
target_modules=targets,
)
return get_peft_model(model, config)
def trainable_parameter_summary(model) -> tuple[int, int]:
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
total = sum(p.numel() for p in model.parameters())
return trainable, total
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