RuntimeError: linalg.vector_norm: Expected a floating point or complex tensor as input. Got Long

[gaussiangit]

Given code throws me following error.
eva_clip/model.py", line 248, in forward
text = torch.nn.functional.normalize(text, p=2, dim=-1)
File "/site-packages/torch/nn/functional.py", line 5373, in normalize
denom = input.norm(p, dim, keepdim=True).clamp_min(eps).expand_as(input)
File "site-packages/torch/_tensor.py", line 827, in norm
return torch.norm(self, p, dim, keepdim, dtype=dtype)
File "/torch/functional.py", line 1822, in norm
return torch.linalg.vector_norm(input, _p, _dim, keepdim, dtype=dtype)
RuntimeError: linalg.vector_norm: Expected a floating point or complex tensor as input. Got Long

from torch.utils.data import Dataset, DataLoader
from transformers import AutoModel, AutoConfig, AutoTokenizer
from eva_clip import create_model_and_transforms, get_tokenizer
device = 'cuda'
from PIL import Image
 
model_name = "EVA02-CLIP-L-14-336" 
pretrained = "EVA02_CLIP_L_336_psz14_s6B.pt" 
image_path = "CLIP.png"
aption = ["a diagram", "a dog", "a cat"]
device = "cuda" if torch.cuda.is_available() else "cpu"
model, _, preprocess = create_model_and_transforms(model_name, pretrained, force_custom_clip=True)
tokenizer = get_tokenizer(model_name)
model = model.to(device)
image = preprocess(Image.open(image_path)).unsqueeze(0).to(device)
text = tokenizer(["a diagram", "a dog", "a cat"]).to(device)
with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    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)  # prints: [[0.8275, 0.1372, 0.0352]]