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Model loader and PEFT / Quantization configuration.
Supports Qwen2.5-VL and Qwen2-VL vision-language architectures with 4-bit QLoRA.
"""
from typing import Any, Dict, Optional, Tuple
import torch
from transformers import (
AutoProcessor,
AutoModelForImageTextToText,
BitsAndBytesConfig,
Qwen2VLForConditionalGeneration,
Qwen2_5_VLForConditionalGeneration,
)
from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast
from peft import (
LoraConfig,
get_peft_model,
prepare_model_for_kbit_training,
PeftModel,
)
from ..utils.logging import setup_logger
logger = setup_logger(__name__)
def build_quantization_config(cfg: Dict[str, Any]) -> Optional[BitsAndBytesConfig]:
"""Constructs BitsAndBytesConfig for 4-bit / 8-bit QLoRA."""
q_cfg = cfg.get("quantization", {})
if not q_cfg.get("load_in_4bit", False):
return None
compute_dtype = torch.bfloat16 if q_cfg.get("bnb_4bit_compute_dtype") == "bfloat16" else torch.float16
return BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type=q_cfg.get("bnb_4bit_quant_type", "nf4"),
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=q_cfg.get("bnb_4bit_use_double_quant", True),
)
def build_peft_config(cfg: Dict[str, Any]) -> LoraConfig:
"""Constructs LoRA configuration."""
p_cfg = cfg.get("peft", {})
return LoraConfig(
r=p_cfg.get("r", 16),
lora_alpha=p_cfg.get("lora_alpha", 32),
lora_dropout=p_cfg.get("lora_dropout", 0.05),
bias=p_cfg.get("bias", "none"),
task_type=p_cfg.get("task_type", "CAUSAL_LM"),
target_modules=p_cfg.get("target_modules", ["q_proj", "v_proj"]),
)
def load_model_and_processor(
cfg: Dict[str, Any],
is_training: bool = True,
adapter_path: Optional[str] = None
) -> Tuple[Any, Any]:
"""Loads model and processor with optional LoRA / QLoRA wrapping."""
model_cfg = cfg.get("model", {})
model_name = model_cfg.get("name_or_path", "Qwen/Qwen2.5-VL-3B-Instruct")
model_type = model_cfg.get("model_type", "qwen2_5_vl")
trust_remote_code = model_cfg.get("trust_remote_code", True)
logger.info(f"Loading processor for: {model_name}")
processor_kwargs = {"trust_remote_code": trust_remote_code}
data_cfg = cfg.get("data", {})
min_pixels = data_cfg.get("min_pixels")
max_pixels = data_cfg.get("max_pixels")
try:
p_kwargs = dict(processor_kwargs)
if min_pixels is not None:
p_kwargs["min_pixels"] = min_pixels
if max_pixels is not None:
p_kwargs["max_pixels"] = max_pixels
processor = AutoProcessor.from_pretrained(model_name, **p_kwargs)
except TypeError:
processor = AutoProcessor.from_pretrained(model_name, **processor_kwargs)
load_4bit = cfg.get("quantization", {}).get("load_in_4bit", False) or (is_training and cfg.get("training", {}).get("method") == "qlora")
bnb_config = build_quantization_config(cfg) if load_4bit else None
torch_dtype = torch.bfloat16 if model_cfg.get("torch_dtype") == "bfloat16" else torch.float16
attn_impl = model_cfg.get("attn_implementation")
if attn_impl == "flash_attention_2" and not (torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8):
logger.info("FlashAttention-2 requires compute capability >= 8.0. Falling back to 'sdpa'.")
attn_impl = "sdpa"
logger.info(f"Loading base model: {model_name} (dtype={torch_dtype}, 4bit={bnb_config is not None}, attn={attn_impl})")
kwargs = {
"quantization_config": bnb_config,
"torch_dtype": torch_dtype,
"device_map": "auto",
"trust_remote_code": trust_remote_code,
}
if attn_impl:
kwargs["attn_implementation"] = attn_impl
if "qwen2_5" in model_type or "qwen2.5" in model_type or "Qwen2.5" in model_name:
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_name, **kwargs)
elif "qwen2" in model_type or "qwen" in model_type or "Qwen2" in model_name:
model = Qwen2VLForConditionalGeneration.from_pretrained(model_name, **kwargs)
else:
model = AutoModelForImageTextToText.from_pretrained(model_name, **kwargs)
if adapter_path:
logger.info(f"Loading trained LoRA adapter from: {adapter_path}")
model = PeftModel.from_pretrained(model, adapter_path)
elif is_training and cfg.get("training", {}).get("method") in ["qlora", "lora"]:
if bnb_config is not None:
model = prepare_model_for_kbit_training(
model,
use_gradient_checkpointing=cfg.get("training", {}).get("gradient_checkpointing", True)
)
peft_config = build_peft_config(cfg)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Apply selective memory-efficient forward pass to prevent OOM on large vocabulary logits
if is_training:
target_base = getattr(model, "base_model", None)
if target_base is not None and hasattr(target_base, "model"):
base_inner = target_base.model
if isinstance(base_inner, (Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration)):
base_inner.forward = _memory_efficient_qwen2_vl_forward.__get__(base_inner, type(base_inner))
logger.info("Applied selective memory-efficient forward pass to base model.")
elif isinstance(model, (Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration)):
model.forward = _memory_efficient_qwen2_vl_forward.__get__(model, type(model))
logger.info("Applied selective memory-efficient forward pass to model.")
if cfg.get("training", {}).get("gradient_checkpointing", True) and hasattr(model, "gradient_checkpointing_enable"):
model.gradient_checkpointing_enable()
return model, processor
def _memory_efficient_qwen2_vl_forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
past_key_values=None,
inputs_embeds=None,
labels=None,
use_cache=None,
pixel_values=None,
pixel_values_videos=None,
image_grid_thw=None,
video_grid_thw=None,
mm_token_type_ids=None,
logits_to_keep=0,
**kwargs,
):
"""Memory-efficient forward pass that only computes lm_head and cross-entropy on non-masked tokens."""
import torch.nn as nn
outputs = self.model(
input_ids=input_ids,
pixel_values=pixel_values,
pixel_values_videos=pixel_values_videos,
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
mm_token_type_ids=mm_token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
**kwargs,
)
hidden_states = outputs.last_hidden_state
loss = None
logits = None
if labels is not None:
shift_labels = nn.functional.pad(labels, (0, 1), value=-100)
shift_labels = shift_labels[..., 1:].contiguous()
shift_labels_flat = shift_labels.view(-1)
valid_mask = shift_labels_flat != -100
if valid_mask.any():
shift_hidden = hidden_states.view(-1, hidden_states.shape[-1])
valid_hidden = shift_hidden[valid_mask]
valid_labels = shift_labels_flat[valid_mask].to(valid_hidden.device)
token_weights = kwargs.get("token_weights", getattr(self, "token_weights", None))
if token_weights is not None:
token_weights = token_weights.to(valid_hidden.device)
cfg_obj = getattr(self, "config", None)
vocab_size = getattr(cfg_obj, "vocab_size", None) or getattr(getattr(cfg_obj, "text_config", None), "vocab_size", None) or 152064
if token_weights.shape[0] < vocab_size:
token_weights = nn.functional.pad(
token_weights,
(0, vocab_size - token_weights.shape[0]),
value=1.0,
)
elif token_weights.shape[0] > vocab_size:
token_weights = token_weights[:vocab_size]
sample_weights = token_weights[valid_labels] if token_weights is not None else None
focal_gamma = kwargs.get("focal_gamma", getattr(self, "focal_gamma", 0.0))
num_items_in_batch = kwargs.get("num_items_in_batch", None)
reduction = "sum" if num_items_in_batch is not None else "mean"
chunk_size = 64
if valid_hidden.shape[0] > chunk_size:
losses = []
w_splits = sample_weights.split(chunk_size) if sample_weights is not None else [None] * ((valid_hidden.shape[0] + chunk_size - 1) // chunk_size)
for chunk_h, chunk_y, chunk_w in zip(valid_hidden.split(chunk_size), valid_labels.split(chunk_size), w_splits):
chunk_logits = self.lm_head(chunk_h).float()
ce = nn.functional.cross_entropy(chunk_logits, chunk_y, reduction="none")
if chunk_w is not None:
ce = ce * chunk_w
if focal_gamma > 0.0:
pt = torch.exp(-ce.detach())
focal_w = (1.0 - pt) ** focal_gamma
ce = focal_w * ce
losses.append(ce.sum())
total_loss = torch.stack(losses).sum()
if reduction == "mean":
loss = total_loss / valid_hidden.shape[0]
else:
if torch.is_tensor(num_items_in_batch):
num_items_in_batch = num_items_in_batch.to(total_loss.device)
loss = total_loss / (num_items_in_batch if num_items_in_batch is not None else valid_hidden.shape[0])
else:
valid_logits = self.lm_head(valid_hidden).float()
ce = nn.functional.cross_entropy(valid_logits, valid_labels, reduction="none")
if sample_weights is not None:
ce = ce * sample_weights
if focal_gamma > 0.0:
pt = torch.exp(-ce.detach())
focal_w = (1.0 - pt) ** focal_gamma
ce = focal_w * ce
if reduction == "mean":
loss = ce.mean()
else:
total_loss = ce.sum()
if torch.is_tensor(num_items_in_batch):
num_items_in_batch = num_items_in_batch.to(total_loss.device)
loss = total_loss / (num_items_in_batch if num_items_in_batch is not None else valid_hidden.shape[0])
else:
loss = torch.tensor(0.0, device=hidden_states.device, requires_grad=True)
else:
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
return Qwen2VLCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
rope_deltas=outputs.rope_deltas,
)
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