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很棒的工作!!!
但是有一个问题我有点疑惑,我发现在train_llava15.py文件中vision_tower似乎是带有梯度的,这样训练出来的模型视觉模块的参数应该是训练过的,但是在我下载的huggingface上rlaif-v 7b的参数到本地后运行chat.py文件会报以下的警告,似乎是视觉模块的参数没有被加载进来,并且还需要再从huggingface上下载clip-vit的参数:
Some weights of the model checkpoint at ./models/RLAIF-V-7B were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.encoder.layers.7.mlp.fc2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.7.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.20.self_attn.v_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.20.self_attn.out_proj.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.6.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.9.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.13.mlp.fc2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.14.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.21.mlp.fc2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.5.mlp.fc2.weight', 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'model.vision_tower.vision_tower.vision_model.encoder.layers.2.self_attn.v_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.22.mlp.fc2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.6.self_attn.v_proj.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.weight'] - This IS expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). /root/miniconda3/lib/python3.12/site-packages/transformers/modeling_utils.py:484: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
并且我查看了load_pretrained_model方法的源码发现LlavaLlamaForCausalLM中的model类LlavaLlamaModel的父类LlavaMetaModel中初始化调用的:
load_pretrained_model
LlavaLlamaForCausalLM
model
LlavaLlamaModel
LlavaMetaModel
self.vision_tower = build_vision_tower(config, delay_load=True)
发现build_vision_tower 方法使用的是原始llava的config,我通过在build_vision_tower中打印这个config:
build_vision_tower
Vision Tower: LlavaConfig { "_name_or_path": "./models/RLAIF-V-7B", "architectures": [ "LlavaLlamaForCausalLM" ], "attention_bias": false, "bos_token_id": 1, "eos_token_id": 2, "freeze_mm_mlp_adapter": false, "freeze_mm_vision_resampler": false, "hidden_act": "silu", "hidden_size": 4096, "image_aspect_ratio": "pad", "initializer_range": 0.02, "intermediate_size": 11008, "max_length": 4096, "max_position_embeddings": 4096, "mm_hidden_size": 1024, "mm_patch_merge_type": "flat", "mm_projector_lr": null, "mm_projector_type": "mlp2x_gelu", "mm_resampler_type": null, "mm_use_im_patch_token": false, "mm_use_im_start_end": false, "mm_vision_select_feature": "patch", "mm_vision_select_layer": -2, "mm_vision_tower": "openai/clip-vit-large-patch14-336", "model_type": "llava_llama", "num_attention_heads": 32, "num_hidden_layers": 32, "num_key_value_heads": 32, "pad_token_id": 0, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": null, "rope_theta": 10000.0, "tie_word_embeddings": false, "tokenizer_model_max_length": 2048, "tokenizer_padding_side": "right", "torch_dtype": "bfloat16", "transformers_version": "4.35.0", "tune_mm_mlp_adapter": false, "tune_mm_vision_resampler": false, "unfreeze_mm_vision_tower": false, "use_cache": false, "use_mm_proj": true, "vocab_size": 32000 }
发现视觉模块仍然是原始的vit,所以模型在初始化时是不是没有load训练过的视觉参数,而是load原始vit的参数?
The text was updated successfully, but these errors were encountered:
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很棒的工作!!!
但是有一个问题我有点疑惑,我发现在train_llava15.py文件中vision_tower似乎是带有梯度的,这样训练出来的模型视觉模块的参数应该是训练过的,但是在我下载的huggingface上rlaif-v 7b的参数到本地后运行chat.py文件会报以下的警告,似乎是视觉模块的参数没有被加载进来,并且还需要再从huggingface上下载clip-vit的参数:
并且我查看了
load_pretrained_model
方法的源码发现LlavaLlamaForCausalLM
中的model
类LlavaLlamaModel
的父类LlavaMetaModel
中初始化调用的:发现
build_vision_tower
方法使用的是原始llava的config,我通过在build_vision_tower
中打印这个config:发现视觉模块仍然是原始的vit,所以模型在初始化时是不是没有load训练过的视觉参数,而是load原始vit的参数?
The text was updated successfully, but these errors were encountered: