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# Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from typing import Optional
import torch
import torch.nn as nn
from mmengine.config import Config, ConfigDict
from mmengine.model import BaseModel
from peft import get_peft_model, prepare_model_for_kbit_training
from transformers import (AutoTokenizer, BitsAndBytesConfig, GenerationConfig)
from xtuner.registry import BUILDER
from xtuner.model.utils import find_all_linear_names, get_peft_model_state_dict, guess_load_checkpoint, make_inputs_require_grad
class QwenVL2(BaseModel):
def __init__(self,
model_path,
freeze_llm=False,
freeze_visual_encoder=False,
llm_lora=None,
visual_encoder_lora=None,
quantization_vit=False,
quantization_llm=False,
pretrained_pth=None,
# Extra:
special_tokens=None,
):
super().__init__()
self.freeze_llm = freeze_llm
self.freeze_visual_encoder = freeze_visual_encoder
self.use_llm_lora = llm_lora is not None
self.use_visual_encoder_lora = visual_encoder_lora is not None
self.quantization_vit = quantization_vit
self.quantization_llm = quantization_llm
if quantization_vit:
assert visual_encoder_lora is not None
if quantization_llm:
assert quantization_llm and llm_lora is not None
# config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
if quantization_vit is False and quantization_llm is False:
quantization = None
else:
llm_int8_skip_modules = ['mlp1']
if quantization_llm and not quantization_vit:
llm_int8_skip_modules.append('vision_model')
if quantization_vit and not quantization_llm:
llm_int8_skip_modules.append('model')
quantization_config = dict(
type=BitsAndBytesConfig,
llm_int8_skip_modules=llm_int8_skip_modules,
load_in_4bit=True,
load_in_8bit=False,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4')
quantization_clazz = quantization_config.pop('type')
quantization = quantization_clazz(**quantization_config)
from .qwen2_vl import Qwen2VLForConditionalGeneration
self.model = Qwen2VLForConditionalGeneration.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
quantization_config=quantization,
# config=config,
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
model_path, trust_remote_code=True)
self.tokenizer = tokenizer
if special_tokens is not None:
self._add_special_tokens(special_tokens)
if self.freeze_llm:
self.model.model.requires_grad_(False)
if self.freeze_visual_encoder:
self.model.visual.requires_grad_(False)
if hasattr(self.model.model, 'enable_input_require_grads'):
self.model.model.enable_input_require_grads()
else:
self.model.model.get_input_embeddings(
).register_forward_hook(make_inputs_require_grad)
self.gradient_checkpointing_enable()
if self.use_llm_lora:
self._prepare_llm_for_lora(llm_lora)
if self.use_visual_encoder_lora:
self._prepare_visual_encoder_for_lora(visual_encoder_lora)
if pretrained_pth is not None:
pretrained_state_dict = guess_load_checkpoint(pretrained_pth)
self.load_state_dict(pretrained_state_dict, strict=False)
print(f'Load pretrained weight from {pretrained_pth}')
self._count = 0
def _add_special_tokens(self, special_tokens):
num_new_tokens = self.tokenizer.add_tokens(special_tokens, special_tokens=True)
if num_new_tokens > 0:
# ! important
self.model.resize_token_embeddings(len(self.tokenizer))
def _post_init(self, fast_pool_size=4, fast_pool=True):
if fast_pool:
self.fast_pool = nn.AdaptiveAvgPool2d((fast_pool_size, fast_pool_size))
return
def _parse_lora_config(self, lora_config):
if isinstance(lora_config, dict) or isinstance(
lora_config, Config) or isinstance(lora_config, ConfigDict):
lora_config = BUILDER.build(lora_config)
return lora_config
def _prepare_llm_for_lora(self,
lora_config,
use_activation_checkpointing=True):
lora_config = self._parse_lora_config(lora_config)
self.model.model = prepare_model_for_kbit_training(
self.model.model, use_activation_checkpointing)
if lora_config.target_modules is None:
modules = find_all_linear_names(self.model.model)
lora_config.target_modules = modules
self.model.model = get_peft_model(self.model.model, lora_config)
def _prepare_visual_encoder_for_lora(self, lora_config):
lora_config = self._parse_lora_config(lora_config)
if lora_config.target_modules is None:
modules = find_all_linear_names(self.model.vision_model)
lora_config.target_modules = modules
self.model.vision_model = get_peft_model(self.model.vision_model,
lora_config)
def gradient_checkpointing_enable(self):
self.activation_checkpointing_enable()
def activation_checkpointing_enable(self):
self.model.model.gradient_checkpointing_enable()
def gradient_checkpointing_disable(self):
self.activation_checkpointing_disable()
def activation_checkpointing_disable(self):
self.model.model.gradient_checkpointing_disable()
def state_dict(self, *args, **kwargs):
state_dict = super().state_dict(*args, **kwargs)
to_return = OrderedDict()
# Step 1. visual_encoder
if self.use_visual_encoder_lora:
to_return.update(
get_peft_model_state_dict(
self.model.vision_model, state_dict=state_dict))
elif not self.freeze_visual_encoder:
to_return.update({
k: v
for k, v in state_dict.items() if 'model.visual.' in k
})
# Step 2. LLM
if self.use_llm_lora:
to_return.update(
get_peft_model_state_dict(
self.model.model, state_dict=state_dict))
elif not self.freeze_llm:
to_return.update({
k: v
for k, v in state_dict.items() if 'model.model.' in k
})
# Step 3. Projector
to_return.update(
{k: v
for k, v in state_dict.items() if 'model.lm_head.' in k})
return to_return
def init_weights(self):
pass
def forward(self, data, data_samples=None, mode='loss'):
has_image = data.get('image_grid_thw', None) is not None
if has_image:
pixel_values = data['pixel_values'][0]
image_grid_thw = data['image_grid_thw'][0]
else:
pixel_values = None
image_grid_thw = None
input_ids = data['input_ids']
# position_ids = data['position_ids']
position_ids = None
attention_mask = data['attention_mask']
labels = data['labels']
use_cache = False
# for lora
outputs = self.model(input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
pixel_values=pixel_values,
image_grid_thw=image_grid_thw,
labels=labels,
use_cache=use_cache,
output_hidden_states=True,
)
return outputs
@torch.no_grad()
def generate(
self,
pixel_values: Optional[torch.FloatTensor] = None,
input_ids: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
generation_config: Optional[GenerationConfig] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
position_ids = None,
image_grid_thw = None,
**generate_kwargs,
) -> torch.LongTensor:
device = self.model.device
pixel_values = pixel_values.to(device)
image_grid_thw = image_grid_thw.to(device)
attention_mask = attention_mask.to(device)
input_ids = input_ids.to(device)
outputs = self.model.generate(
input_ids=input_ids.to(device),
attention_mask=attention_mask,
position_ids=position_ids,
pixel_values=pixel_values,
image_grid_thw=image_grid_thw,
generation_config=generation_config,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
use_cache=True,
**generate_kwargs,
)
return outputs
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