# Copyright 2023 Haotian Liu # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import warnings import shutil from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig import torch from llava.model import * from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN def map_keys(model, pretrained_ckpt_loc): ckpt = torch.load(pretrained_ckpt_loc, map_location='cpu') print(ckpt.keys()) print(ckpt['proj'].size()) i = 0 for name, param in model.named_parameters(): # print(ckpt.keys()) i+=1 print(name, param.size()) # if param.requires_grad: print(i) exit() with torch.no_grad(): for i in range(4): for p in range(2): self.downsample_layers[i][p].weight.copy_(ckpt[f'downsample_layers.{i}.{p}.weight']) self.downsample_layers[i][p].bias.copy_(ckpt[f'downsample_layers.{i}.{p}.bias']) for j in range(4): for k in range(stt[j]): self.stages[j][k].gamma.copy_(ckpt[f'stages.{j}.{k}.gamma']) self.stages[j][k].dwconv.weight.copy_(ckpt[f'stages.{j}.{k}.dwconv.weight']) self.stages[j][k].dwconv.bias.copy_(ckpt[f'stages.{j}.{k}.dwconv.bias']) self.stages[j][k].norm.weight.copy_(ckpt[f'stages.{j}.{k}.norm.weight']) self.stages[j][k].norm.bias.copy_(ckpt[f'stages.{j}.{k}.norm.bias']) self.stages[j][k].pwconv1.weight.copy_(ckpt[f'stages.{j}.{k}.pwconv1.weight']) self.stages[j][k].pwconv1.bias.copy_(ckpt[f'stages.{j}.{k}.pwconv1.bias']) self.stages[j][k].pwconv2.weight.copy_(ckpt[f'stages.{j}.{k}.pwconv2.weight']) self.stages[j][k].pwconv2.bias.copy_(ckpt[f'stages.{j}.{k}.pwconv2.bias']) class ClipVisionModel(torch.nn.Module): def __init__(self, model, normalize, all_tokens=False, proj=True): super().__init__() self.model = model self.normalize = normalize self.proj = model.proj if all_tokens: self.model.output_tokens = True if not proj: self.model.proj = None def forward(self, vision_, output_normalize): embedding = self.model(self.normalize(vision_)) if output_normalize: embedding = F.normalize(embedding, dim=-1) if self.model.output_tokens: # flatten and concatenate all tokens return torch.hstack([embedding[0].flatten(1), embedding[1].flatten(1)]) else: return embedding def load_pretrained_model(model_path, model_base, model_name, pretrained_rob_path=None, dtype=None, device_map="auto", device="cuda"): kwargs = {"device_map": device_map} load_8bit=False load_4bit=False if load_8bit: kwargs['load_in_8bit'] = True elif load_4bit: kwargs['load_in_4bit'] = True kwargs['quantization_config'] = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4' ) else: assert dtype is not None if dtype == 'float16': kwargs['torch_dtype'] = torch.float16 elif dtype == 'float32': kwargs['torch_dtype'] = torch.float32 else: raise ValueError(f"Unknown dtype {dtype}, must be float16 or float32") if 'llava' in model_name.lower(): # Load LLaVA model if 'lora' in model_name.lower() and model_base is None: warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.') if 'lora' in model_name.lower() and model_base is not None: lora_cfg_pretrained = AutoConfig.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) print('Loading LLaVA from base model...') model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs) token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features if model.lm_head.weight.shape[0] != token_num: model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype)) model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype)) print('Loading additional LLaVA weights...') if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')): non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu') else: # this is probably from HF Hub from huggingface_hub import hf_hub_download def load_from_hf(repo_id, filename, subfolder=None): cache_file = hf_hub_download( repo_id=repo_id, filename=filename, subfolder=subfolder) return torch.load(cache_file, map_location='cpu') non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin') non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()} if any(k.startswith('model.model.') for k in non_lora_trainables): non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()} model.load_state_dict(non_lora_trainables, strict=False) from peft import PeftModel print('Loading LoRA weights...') model = PeftModel.from_pretrained(model, model_path) print('Merging LoRA weights...') model = model.merge_and_unload() print('Model is loaded...') elif model_base is not None: # this may be mm projector only print('Loading LLaVA from base model...') if 'mpt' in model_name.lower(): if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')): shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py')) tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True) cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True) model = LlavaMPTForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) else: tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) cfg_pretrained = AutoConfig.from_pretrained(model_path) model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs) mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu') mm_projector_weights = {k: v.to(kwargs["torch_dtype"]) for k, v in mm_projector_weights.items()} model.load_state_dict(mm_projector_weights, strict=False) else: if 'mpt' in model_name.lower(): tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) model = LlavaMPTForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) else: tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) else: # Load language model if model_base is not None: # PEFT model from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=kwargs["torch_dtype"], low_cpu_mem_usage=True, device_map="auto") print(f"Loading LoRA weights from {model_path}") model = PeftModel.from_pretrained(model, model_path) print(f"Merging weights") model = model.merge_and_unload() if kwargs["torch_dtype"] == torch.float16: print('Convert to FP16...') model.to(torch.float16) else: use_fast = False if 'mpt' in model_name.lower(): tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs) else: tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) image_processor = None if 'llava' in model_name.lower(): mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True) if mm_use_im_patch_token: tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) if mm_use_im_start_end: tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) model.resize_token_embeddings(len(tokenizer)) vision_tower = model.get_vision_tower() # vision_tower.set_device(device) non_llava = True if pretrained_rob_path not in [None, 'None', 'none'] else False if not vision_tower.is_loaded: vision_tower.load_model(non_llava, pretrained_rob_path)#.to(device=device) # print(vision_tower.vision_tower) vision_tower.to(device=device, dtype=kwargs["torch_dtype"]) image_processor = vision_tower.image_processor if hasattr(model.config, "max_sequence_length"): context_len = model.config.max_sequence_length else: context_len = 2048 return model, image_processor, tokenizer, context_len