Spaces:
Sleeping
Sleeping
File size: 11,862 Bytes
a72a7d4 1855cc2 a72a7d4 |
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 |
from typing import Dict, Optional
import torch
import torch.distributed as dist
from torch import nn, Tensor
from transformers import PreTrainedModel, AutoModelForCausalLM, AutoConfig
from peft import LoraConfig, get_peft_model, PeftModel
from src.arguments import ModelArguments
from src.vlm_backbone.phi3_v.modeling_phi3_v import Phi3VForCausalLM
from src.vlm_backbone.llava_next import LlavaNextForConditionalGeneration
from transformers import Qwen2VLForConditionalGeneration
class MMEBModel(nn.Module):
TRANSFORMER_CLS = AutoModelForCausalLM
def __init__(self,
encoder: PreTrainedModel,
pooling: str = 'cls',
normalize: bool = False,
temperature: float = 1.0,
):
super().__init__()
self.config = encoder.config
self.encoder = encoder
self.pooling = pooling
self.normalize = normalize
self.temperature = temperature
self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')
self.is_ddp = dist.is_initialized()
if self.is_ddp:
self.process_rank = dist.get_rank()
self.world_size = dist.get_world_size()
def encode_input(self, input):
hidden_states = self.encoder(**input, return_dict=True, output_hidden_states=True)
hidden_states = hidden_states.hidden_states[-1]
pooled_output = self._pooling(hidden_states, input['attention_mask'])
return pooled_output
def _pooling(self, last_hidden_state, attention_mask):
if self.pooling == 'last' or self.pooling == 'eos':
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_state.shape[0]
reps = last_hidden_state[
torch.arange(batch_size, device=last_hidden_state.device), sequence_lengths]
else:
raise NotImplementedError
if self.normalize:
reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
return reps
@classmethod
def build(cls, model_args: ModelArguments, **hf_kwargs):
# Loading the base model
lora_target_modules = None
if model_args.model_backbone == "llava_next":
config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
config.use_cache = False
config.padding_side = "left"
base_model = LlavaNextForConditionalGeneration.from_pretrained(
model_args.model_name,
config=config,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
)
elif model_args.model_backbone == "qwen":
base_model = Qwen2VLForConditionalGeneration.from_pretrained(
model_args.model_name,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
)
base_model.padding_side = "right"
# Loading the base model
elif model_args.model_backbone == "phi35v":
config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
# config._attn_implementation = "eager"
config.attn_implementation = "flash_attention_2"
config.padding_side = "right"
config.use_cache = False
base_model = Phi3VForCausalLM.from_pretrained(
model_args.model_name,
config=config,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
)
elif model_args.model_backbone == "internvl_2_5":
# from transformers import InternVLChatConfig, InternVLChatModel
from src.vlm_backbone.intern_vl import InternVLChatConfig, InternVLChatModel
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name,
trust_remote_code=True
)
# import pdb;pdb.set_trace()
config = InternVLChatConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
# config.vision_config.image_size = data_args.force_image_size # 假设data_args包含图像尺寸
config.use_flash_attn = False
base_model = InternVLChatModel.from_pretrained(
model_args.model_name,
config=config,
tokenizer=tokenizer,
# attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16
)
lora_target_modules = base_model.get_lora_target_modules()
else:
config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
config.use_cache = False
config.padding_side = "right"
base_model = cls.TRANSFORMER_CLS.from_pretrained(
model_args.model_name, **hf_kwargs, config=config,
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16,
trust_remote_code=True)
base_model.padding_side = "right"
if model_args.lora:
if lora_target_modules is None:
lora_target_modules = model_args.lora_target_modules.split(',')
lora_config = LoraConfig(
r=model_args.lora_r,
lora_alpha=model_args.lora_alpha,
target_modules=lora_target_modules,
lora_dropout=model_args.lora_dropout,
init_lora_weights="gaussian",
use_dora=True,
inference_mode=False
)
lora_model = get_peft_model(base_model, lora_config)
model = cls(
encoder=lora_model,
pooling=model_args.pooling,
normalize=model_args.normalize,
temperature=model_args.temperature
)
else:
model = cls(
encoder=base_model,
pooling=model_args.pooling,
normalize=model_args.normalize,
temperature=model_args.temperature
)
return model
@classmethod
def load(cls, model_args: ModelArguments, **hf_kwargs):
# Loading the base model
checkpoint_path = model_args.checkpoint_path if model_args.checkpoint_path else model_args.model_name
if model_args.model_backbone == "llava_next":
config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
config.use_cache = False
base_model = LlavaNextForConditionalGeneration.from_pretrained(
model_args.model_name,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
# attn_implementation="flash_attention_2"
)
base_model.padding_side = "left"
elif model_args.model_backbone == "phi35v":
# Loading the base model
config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
config.use_cache = False
config.padding_side = "right"
base_model = Phi3VForCausalLM.from_pretrained(model_args.model_name, **hf_kwargs, config=config,
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16, trust_remote_code=True)
base_model.padding_side = "right"
elif model_args.model_backbone == "internvl_2_5":
print("loading model")
from src.vlm_backbone.intern_vl import InternVLChatConfig, InternVLChatModel
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name,
trust_remote_code=True
)
config = InternVLChatConfig.from_pretrained(model_args.model_name)
# config.vision_config.image_size = data_args.force_image_size
config.use_flash_attn = False
base_model = InternVLChatModel.from_pretrained(
model_args.model_name,
config=config,
tokenizer=tokenizer,
# attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16
)
else:
# Loading the base model
config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
config.use_cache = False
config.padding_side = "right"
base_model = cls.TRANSFORMER_CLS.from_pretrained(
checkpoint_path, **hf_kwargs, config=config,
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16,
trust_remote_code=True)
base_model.padding_side = "right"
# Building the model on top of the base
if model_args.lora:
print("loading lora parameters")
lora_config = LoraConfig.from_pretrained(checkpoint_path)
lora_model = PeftModel.from_pretrained(base_model, checkpoint_path, config=lora_config)
merged_model = lora_model.merge_and_unload()
model = cls(
encoder=merged_model,
pooling=model_args.pooling,
normalize=model_args.normalize
)
else:
model = cls(
encoder=base_model,
pooling=model_args.pooling,
normalize=model_args.normalize
)
return model
def save(self, output_dir: str):
self.encoder.save_pretrained(output_dir)
def forward(self, qry: Dict[str, Tensor] = None, tgt: Dict[str, Tensor] = None, neg: Dict[str, Tensor] = None):
qry_reps = self.encode_input(qry) if qry else None # (bsz_per_device, dim)
tgt_reps = self.encode_input(tgt) if tgt else None # (bsz_per_device, dim)
neg_reps = self.encode_input(neg) if neg else None # (bsz_per_device, dim)
if qry_reps is None or tgt_reps is None:
return {"qry_reps": qry_reps, "tgt_reps": tgt_reps}
# Gather representations if using DDP
if self.is_ddp:
all_qry_reps = self._dist_gather_tensor(qry_reps)
all_tgt_reps = self._dist_gather_tensor(tgt_reps)
all_neg_reps = self._dist_gather_tensor(neg_reps) if neg_reps is not None else None
else:
all_qry_reps = qry_reps
all_tgt_reps = tgt_reps
all_neg_reps = neg_reps
# Compute similarity scores
scores = self.compute_similarity(all_qry_reps, all_tgt_reps)
scores = scores.view(all_qry_reps.size(0), -1)
# Add negative scores if available
if all_neg_reps is not None:
qry_neg_cos = self.compute_similarity(all_qry_reps, all_neg_reps)
scores = torch.cat([scores, qry_neg_cos], dim=1)
# Compute loss
target = torch.arange(scores.size(0), device=scores.device, dtype=torch.long)
target = target * (all_qry_reps.size(0) // all_tgt_reps.size(0))
loss = self.cross_entropy(scores / self.temperature, target)
if self.is_ddp:
loss = loss * self.world_size
return loss
def _dist_gather_tensor(self, t: Tensor):
t = t.contiguous()
all_tensors = [torch.empty_like(t) for _ in range(self.world_size)]
dist.all_gather(all_tensors, t)
all_tensors[self.process_rank] = t
all_tensors = torch.cat(all_tensors, dim=0)
return all_tensors
def compute_similarity(self, q_reps, p_reps):
return torch.matmul(q_reps, p_reps.transpose(0, 1))
|