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Add LLaVA series to evaluation #63

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Add LLaVA series to evaluation #63

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57 changes: 57 additions & 0 deletions agent_studio/llm/huggingface.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,25 @@ def generate_response(
.eval()
)
self.processor = AutoProcessor.from_pretrained(self.model_name)
elif "llava" in self.model_name:

from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path

disable_torch_init()
model_n=get_model_name_from_path(self.model_name)
self.tokenizer,self.model,self.image_processor,self.context_len=load_pretrained_model(self.model_name,None,model_n)
self.default_image_token=DEFAULT_IMAGE_TOKEN
self.image_token_index=IMAGE_TOKEN_INDEX
self.default_im_start_token=DEFAULT_IM_START_TOKEN
self.default_im_end_token=DEFAULT_IM_END_TOKEN
self.conv_templates=conv_templates
self.tokenizer_image_token=tokenizer_image_token
self.process_images=process_images

else:
self.tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name, trust_remote_code=True
Expand Down Expand Up @@ -209,6 +228,44 @@ def generate_response(
**kwargs,
)

elif "llava" in self.model_name:

assert (
len(model_message) == 2
and "text" in model_message[1]
and "image" in model_message[0]
), "Expected only 1 image and 1 text for LLaVA."

query = model_message[1]["text"]
image = model_message[0]['image']
if isinstance(model_message[0]["image"], str):
image = Image.open(model_message[0]["image"]).convert("RGB")
else: # is numpy array
image = Image.fromarray(model_message[0]["image"]).convert("RGB")

if self.model.config.mm_use_im_start_end:
query = self.default_im_start_token + self.default_image_token + self.default_im_end_token + '\n' + query
else:
query = self.default_image_token + '\n' + query

conv = self.conv_templates['llava_v1'].copy()
conv.append_message(conv.roles[0],query)
conv.append_message(conv.roles[1],None)
prompt = conv.get_prompt()
input_ids = self.tokenizer_image_token(prompt,self.tokenizer,self.image_token_index,return_tensors='pt').unsqueeze(0).cuda()
image_tensors = self.process_images([image],self.image_processor,self.model.config)[0]

with torch.inference_mode():
output_ids = self.model.generate(
input_ids,
images=image_tensors.unsqueeze(0).half().cuda(),
image_sizes=[image.size],
do_sample=True,
max_new_tokens=1024,
use_cache=True)

response = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()

else:
raise ValueError(f"Model {self.model_name} is not supported.")

Expand Down
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