about the retrieval example
#3
by
hugginL
- opened
The example seems to be not right in
text_features = l2v.encode(captions, convert_to_tensor=True).to('cuda')
with torch.no_grad(), torch.cuda.amp.autocast():
...
text_features = model.get_text_features(text_features)
since
l2v.encode() returns embeddings of the sentences.Embeddings
and
inputs = tokenizer(captions, padding=True, return_tensors="pt")
text_features = model.get_text_features(**inputs)
When loading the llm_model, I am getting the warning info. as follows:
Some weights of CLIPModel were not initialized from the model checkpoint at microsoft/LLM2CLIP-Openai-B-16 and are newly initialized: ['text_model.embeddings.position_embedding.weight', 'text_model.embeddings.token_embedding.weight', 'text_model.encoder.layers.0.layer_norm1.bias', 'text_model.encoder.layers.0.layer_norm1.weight', 'text_model.encoder.layers.0.layer_norm2.bias', 'text_model.encoder.layers.0.layer_norm2.weight', 'text_model.encoder.layers.0.mlp.fc1.bias', 'text_model.encoder.layers.0.mlp.fc1.weight', 'text_model.encoder.layers.0.mlp.fc2.bias', 'text_model.encoder.layers.0.mlp.fc2.weight', 'text_model.encoder.layers.0.self_attn.k_proj.bias', 'text_model.encoder.layers.0.self_attn.k_proj.weight', 'text_model.encoder.layers.0.self_attn.out_proj.bias', 'text_model.encoder.layers.0.self_attn.out_proj.weight', 'text_model.encoder.layers.0.self_attn.q_proj.bias', 'text_model.encoder.layers.0.self_attn.q_proj.weight', 'text_model.encoder.layers.0.self_attn.v_proj.bias', 'text_model.encoder.layers.0.self_attn.v_proj.weight', 'text_model.encoder.layers.1.layer_norm1.bias', 'text_model.encoder.layers.1.layer_norm1.weight', 'text_model.encoder.layers.1.layer_norm2.bias', 'text_model.encoder.layers.1.layer_norm2.weight', 'text_model.encoder.layers.1.mlp.fc1.bias', 'text_model.encoder.layers.1.mlp.fc1.weight', 'text_model.encoder.layers.1.mlp.fc2.bias', 'text_model.encoder.layers.1.mlp.fc2.weight', 'text_model.encoder.layers.1.self_attn.k_proj.bias', 'text_model.encoder.layers.1.self_attn.k_proj.weight', 'text_model.encoder.layers.1.self_attn.out_proj.bias', 'text_model.encoder.layers.1.self_attn.out_proj.weight', 'text_model.encoder.layers.1.self_attn.q_proj.bias', 'text_model.encoder.layers.1.self_attn.q_proj.weight', 'text_model.encoder.layers.1.self_attn.v_proj.bias', 'text_model.encoder.layers.1.self_attn.v_proj.weight', 'text_model.encoder.layers.10.layer_norm1.bias', 'text_model.encoder.layers.10.layer_norm1.weight', 'text_model.encoder.layers.10.layer_norm2.bias', 'text_model.encoder.layers.10.layer_norm2.weight', 'text_model.encoder.layers.10.mlp.fc1.bias', 'text_model.encoder.layers.10.mlp.fc1.weight', 'text_model.encoder.layers.10.mlp.fc2.bias', 'text_model.encoder.layers.10.mlp.fc2.weight', 'text_model.encoder.layers.10.self_attn.k_proj.bias', 'text_model.encoder.layers.10.self_attn.k_proj.weight', 'text_model.encoder.layers.10.self_attn.out_proj.bias', 'text_model.encoder.layers.10.self_attn.out_proj.weight', 'text_model.encoder.layers.10.self_attn.q_proj.bias', 'text_model.encoder.layers.10.self_attn.q_proj.weight', 'text_model.encoder.layers.10.self_attn.v_proj.bias', 'text_model.encoder.layers.10.self_attn.v_proj.weight', 'text_model.encoder.layers.11.layer_norm1.bias', 'text_model.encoder.layers.11.layer_norm1.weight', 'text_model.encoder.layers.11.layer_norm2.bias', 'text_model.encoder.layers.11.layer_norm2.weight', 'text_model.encoder.layers.11.mlp.fc1.bias', 'text_model.encoder.layers.11.mlp.fc1.weight', 'text_model.encoder.layers.11.mlp.fc2.bias', 'text_model.encoder.layers.11.mlp.fc2.weight', 'text_model.encoder.layers.11.self_attn.k_proj.bias', 'text_model.encoder.layers.11.self_attn.k_proj.weight', 'text_model.encoder.layers.11.self_attn.out_proj.bias', 'text_model.encoder.layers.11.self_attn.out_proj.weight', 'text_model.encoder.layers.11.self_attn.q_proj.bias', 'text_model.encoder.layers.11.self_attn.q_proj.weight', 'text_model.encoder.layers.11.self_attn.v_proj.bias', 'text_model.encoder.layers.11.self_attn.v_proj.weight', 'text_model.encoder.layers.2.layer_norm1.bias', 'text_model.encoder.layers.2.layer_norm1.weight', 'text_model.encoder.layers.2.layer_norm2.bias', 'text_model.encoder.layers.2.layer_norm2.weight', 'text_model.encoder.layers.2.mlp.fc1.bias', 'text_model.encoder.layers.2.mlp.fc1.weight', 'text_model.encoder.layers.2.mlp.fc2.bias', 'text_model.encoder.layers.2.mlp.fc2.weight', 'text_model.encoder.layers.2.self_attn.k_proj.bias', 'text_model.encoder.layers.2.self_attn.k_proj.weight', 'text_model.encoder.layers.2.self_attn.out_proj.bias', 'text_model.encoder.layers.2.self_attn.out_proj.weight', 'text_model.encoder.layers.2.self_attn.q_proj.bias', 'text_model.encoder.layers.2.self_attn.q_proj.weight', 'text_model.encoder.layers.2.self_attn.v_proj.bias', 'text_model.encoder.layers.2.self_attn.v_proj.weight', 'text_model.encoder.layers.3.layer_norm1.bias', 'text_model.encoder.layers.3.layer_norm1.weight', 'text_model.encoder.layers.3.layer_norm2.bias', 'text_model.encoder.layers.3.layer_norm2.weight', 'text_model.encoder.layers.3.mlp.fc1.bias', 'text_model.encoder.layers.3.mlp.fc1.weight', 'text_model.encoder.layers.3.mlp.fc2.bias', 'text_model.encoder.layers.3.mlp.fc2.weight', 'text_model.encoder.layers.3.self_attn.k_proj.bias', 'text_model.encoder.layers.3.self_attn.k_proj.weight', 'text_model.encoder.layers.3.self_attn.out_proj.bias', 'text_model.encoder.layers.3.self_attn.out_proj.weight', 'text_model.encoder.layers.3.self_attn.q_proj.bias', 'text_model.encoder.layers.3.self_attn.q_proj.weight', 'text_model.encoder.layers.3.self_attn.v_proj.bias', 'text_model.encoder.layers.3.self_attn.v_proj.weight', 'text_model.encoder.layers.4.layer_norm1.bias', 'text_model.encoder.layers.4.layer_norm1.weight', 'text_model.encoder.layers.4.layer_norm2.bias', 'text_model.encoder.layers.4.layer_norm2.weight', 'text_model.encoder.layers.4.mlp.fc1.bias', 'text_model.encoder.layers.4.mlp.fc1.weight', 'text_model.encoder.layers.4.mlp.fc2.bias', 'text_model.encoder.layers.4.mlp.fc2.weight', 'text_model.encoder.layers.4.self_attn.k_proj.bias', 'text_model.encoder.layers.4.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.out_proj.bias', 'text_model.encoder.layers.4.self_attn.out_proj.weight', 'text_model.encoder.layers.4.self_attn.q_proj.bias', 'text_model.encoder.layers.4.self_attn.q_proj.weight', 'text_model.encoder.layers.4.self_attn.v_proj.bias', 'text_model.encoder.layers.4.self_attn.v_proj.weight', 'text_model.encoder.layers.5.layer_norm1.bias', 'text_model.encoder.layers.5.layer_norm1.weight', 'text_model.encoder.layers.5.layer_norm2.bias', 'text_model.encoder.layers.5.layer_norm2.weight', 'text_model.encoder.layers.5.mlp.fc1.bias', 'text_model.encoder.layers.5.mlp.fc1.weight', 'text_model.encoder.layers.5.mlp.fc2.bias', 'text_model.encoder.layers.5.mlp.fc2.weight', 'text_model.encoder.layers.5.self_attn.k_proj.bias', 'text_model.encoder.layers.5.self_attn.k_proj.weight', 'text_model.encoder.layers.5.self_attn.out_proj.bias', 'text_model.encoder.layers.5.self_attn.out_proj.weight', 'text_model.encoder.layers.5.self_attn.q_proj.bias', 'text_model.encoder.layers.5.self_attn.q_proj.weight', 'text_model.encoder.layers.5.self_attn.v_proj.bias', 'text_model.encoder.layers.5.self_attn.v_proj.weight', 'text_model.encoder.layers.6.layer_norm1.bias', 'text_model.encoder.layers.6.layer_norm1.weight', 'text_model.encoder.layers.6.layer_norm2.bias', 'text_model.encoder.layers.6.layer_norm2.weight', 'text_model.encoder.layers.6.mlp.fc1.bias', 'text_model.encoder.layers.6.mlp.fc1.weight', 'text_model.encoder.layers.6.mlp.fc2.bias', 'text_model.encoder.layers.6.mlp.fc2.weight', 'text_model.encoder.layers.6.self_attn.k_proj.bias', 'text_model.encoder.layers.6.self_attn.k_proj.weight', 'text_model.encoder.layers.6.self_attn.out_proj.bias', 'text_model.encoder.layers.6.self_attn.out_proj.weight', 'text_model.encoder.layers.6.self_attn.q_proj.bias', 'text_model.encoder.layers.6.self_attn.q_proj.weight', 'text_model.encoder.layers.6.self_attn.v_proj.bias', 'text_model.encoder.layers.6.self_attn.v_proj.weight', 'text_model.encoder.layers.7.layer_norm1.bias', 'text_model.encoder.layers.7.layer_norm1.weight', 'text_model.encoder.layers.7.layer_norm2.bias', 'text_model.encoder.layers.7.layer_norm2.weight', 'text_model.encoder.layers.7.mlp.fc1.bias', 'text_model.encoder.layers.7.mlp.fc1.weight', 'text_model.encoder.layers.7.mlp.fc2.bias', 'text_model.encoder.layers.7.mlp.fc2.weight', 'text_model.encoder.layers.7.self_attn.k_proj.bias', 'text_model.encoder.layers.7.self_attn.k_proj.weight', 'text_model.encoder.layers.7.self_attn.out_proj.bias', 'text_model.encoder.layers.7.self_attn.out_proj.weight', 'text_model.encoder.layers.7.self_attn.q_proj.bias', 'text_model.encoder.layers.7.self_attn.q_proj.weight', 'text_model.encoder.layers.7.self_attn.v_proj.bias', 'text_model.encoder.layers.7.self_attn.v_proj.weight', 'text_model.encoder.layers.8.layer_norm1.bias', 'text_model.encoder.layers.8.layer_norm1.weight', 'text_model.encoder.layers.8.layer_norm2.bias', 'text_model.encoder.layers.8.layer_norm2.weight', 'text_model.encoder.layers.8.mlp.fc1.bias', 'text_model.encoder.layers.8.mlp.fc1.weight', 'text_model.encoder.layers.8.mlp.fc2.bias', 'text_model.encoder.layers.8.mlp.fc2.weight', 'text_model.encoder.layers.8.self_attn.k_proj.bias', 'text_model.encoder.layers.8.self_attn.k_proj.weight', 'text_model.encoder.layers.8.self_attn.out_proj.bias', 'text_model.encoder.layers.8.self_attn.out_proj.weight', 'text_model.encoder.layers.8.self_attn.q_proj.bias', 'text_model.encoder.layers.8.self_attn.q_proj.weight', 'text_model.encoder.layers.8.self_attn.v_proj.bias', 'text_model.encoder.layers.8.self_attn.v_proj.weight', 'text_model.encoder.layers.9.layer_norm1.bias', 'text_model.encoder.layers.9.layer_norm1.weight', 'text_model.encoder.layers.9.layer_norm2.bias', 'text_model.encoder.layers.9.layer_norm2.weight', 'text_model.encoder.layers.9.mlp.fc1.bias', 'text_model.encoder.layers.9.mlp.fc1.weight', 'text_model.encoder.layers.9.mlp.fc2.bias', 'text_model.encoder.layers.9.mlp.fc2.weight', 'text_model.encoder.layers.9.self_attn.k_proj.bias', 'text_model.encoder.layers.9.self_attn.k_proj.weight', 'text_model.encoder.layers.9.self_attn.out_proj.bias', 'text_model.encoder.layers.9.self_attn.out_proj.weight', 'text_model.encoder.layers.9.self_attn.q_proj.bias', 'text_model.encoder.layers.9.self_attn.q_proj.weight', 'text_model.encoder.layers.9.self_attn.v_proj.bias', 'text_model.encoder.layers.9.self_attn.v_proj.weight', 'text_model.final_layer_norm.bias', 'text_model.final_layer_norm.weight', 'text_projection.weight']
Yes. l2v.encode() returns embeddings of the sentences.Embeddings. The input to the model is the sentence embeddings, eliminating the need for re-tokenization.
The example should be functional without requiring any modifications.
text_embeddings = l2v.encode(captions, convert_to_tensor=True).to('cuda')
with torch.no_grad(), torch.cuda.amp.autocast():
text_features = model.get_text_features(text_embeddings)
Thank you for your reply.
However, when I run text_features = model.get_text_features(text), errors occur:
Traceback (most recent call last):
File "/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3508, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-9-63fa963a5782>", line 3, in <module>
text_features = model.get_text_features(text_features)
File "/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py", line 1001, in get_text_features
text_outputs = self.text_model(
File "/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py", line 697, in forward
hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
File "/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py", line 222, in forward
inputs_embeds = self.token_embedding(input_ids)
File "/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/lib/python3.8/site-packages/torch/nn/modules/sparse.py", line 163, in forward
return F.embedding(
File "/lib/python3.8/site-packages/torch/nn/functional.py", line 2237, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.cuda.FloatTensor instead (while checking arguments for embedding)
I successfully ran it on the '4.40.2' version of transformers. Could it be related to the transformers version?
Also transformers-4.40.2. By the way, torch-2.2.1, llm2vec 0.2.2.
Did you make a slight modification to the example? Could you please confirm if the following example will run successfully?
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from PIL import Image
import torch
from llm2vec import LLM2Vec
from transformers import AutoModel, AutoConfig, AutoTokenizer
from transformers import CLIPImageProcessor
processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch16")
model_name_or_path = "microsoft/LLM2CLIP-Openai-B-16" # or /path/to/local/LLM2CLIP-Openai-B-16
model = AutoModel.from_pretrained(
model_name_or_path,
torch_dtype=torch.bfloat16,
trust_remote_code=True).to('cuda').eval()
llm_model_name = 'microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned'
config = AutoConfig.from_pretrained(
llm_model_name, trust_remote_code=True
)
llm_model = AutoModel.from_pretrained(llm_model_name, torch_dtype=torch.bfloat16, config=config, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
llm_model.config._name_or_path = 'meta-llama/Meta-Llama-3-8B-Instruct' # Workaround for LLM2VEC
l2v = LLM2Vec(llm_model, tokenizer, pooling_mode="mean", max_length=512, doc_max_length=512)
captions = ["a diagram", "a dog", "a cat"]
image_path = "CLIP.png"
image = Image.open(image_path)
input_pixels = processor(images=image, return_tensors="pt").pixel_values.to('cuda')
text_features = l2v.encode(captions, convert_to_tensor=True).to('cuda')
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.get_image_features(input_pixels)
text_features = model.get_text_features(text_features)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs)
You are right. I have solved this problem, thank you so much for your reminder and help.
Is there any way to get the text encodings without using such a memory intensive version?
I tried referencing the code above and using the following:
llm_model_name = 'microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned'
config = AutoConfig.from_pretrained(
llm_model_name, trust_remote_code=True
)
llm_model = AutoModel.from_pretrained(llm_model_name, torch_dtype=torch.bfloat16, config=config, trust_remote_code=True)
but I don't have enough memory to ultimately get this to work - I am fairly new to all of this so would appreciate any advice
We may release a version of LLaMA-1B in the near future. Please stay tuned
Is there any way to get the text encodings without using such a memory intensive version?
I tried referencing the code above and using the following:
llm_model_name = 'microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned'
config = AutoConfig.from_pretrained(
llm_model_name, trust_remote_code=True
)
llm_model = AutoModel.from_pretrained(llm_model_name, torch_dtype=torch.bfloat16, config=config, trust_remote_code=True)but I don't have enough memory to ultimately get this to work - I am fairly new to all of this so would appreciate any advice
We have released the LLM2CLIP-Llama-3.2-1B-Instruct-CC-Finetuned model. We also plan to release the corresponding EVA02-L-14-224 ViT model.