dong.hyun commited on
Commit
db17193
·
1 Parent(s): 82f2e21

Update README & remove redundant code

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Files changed (2) hide show
  1. README.md +4 -4
  2. processing_hyperclovax.py +1 -15
README.md CHANGED
@@ -85,7 +85,7 @@ from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer
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  model_name = "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B"
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  model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device="cuda")
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- preprocessor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  # LLM Example
@@ -106,7 +106,7 @@ llm_chat = [
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  model_inputs = processor.apply_chat_template(
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  llm_chat, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True
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  )
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- model_inputs = model_inputs.to(device=DEVICE)
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  # Please adjust parameters like top_p appropriately for your use case.
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  output_ids = model.generate(
@@ -165,8 +165,8 @@ vlm_chat = [
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  model_inputs = processor.apply_chat_template(
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  vlm_chat, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True,
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  )
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- model_inputs = model_inputs.to(device=DEVICE)
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- output_ids = model.generate(
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  **model_inputs,
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  max_new_tokens=64,
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  do_sample=True,
 
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  model_name = "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B"
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  model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device="cuda")
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+ processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  # LLM Example
 
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  model_inputs = processor.apply_chat_template(
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  llm_chat, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True
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  )
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+ model_inputs = model_inputs.to(device="cuda")
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  # Please adjust parameters like top_p appropriately for your use case.
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  output_ids = model.generate(
 
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  model_inputs = processor.apply_chat_template(
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  vlm_chat, tokenize=True, return_dict=True, return_tensors="pt", add_generation_prompt=True,
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  )
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+ model_inputs = model_inputs.to(device="cuda")
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+ output_ids = model.generate(
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  **model_inputs,
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  max_new_tokens=64,
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  do_sample=True,
processing_hyperclovax.py CHANGED
@@ -136,17 +136,6 @@ class HCXProcessor(ProcessorMixin):
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  # vllm needs vision_query_lengths, but we don't need it
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  del model_inputs["vision_query_lengths_images"]
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  del model_inputs["vision_query_lengths_videos"]
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-
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- # # vllm 호환성을 위해 이곳에서 token 을 vision_query_length만큼 늘리기 처리
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- # if "input_ids" in model_inputs:
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- # # self.image_token 모두 찾기
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- # input_ids = self.repeat_dummy_tokens(
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- # model_inputs["input_ids"], self.image_token_id, model_inputs["vision_query_lengths_images"]
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- # )
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- # input_ids = self.repeat_dummy_tokens(
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- # input_ids, self.video_token_id, model_inputs["vision_query_lengths_videos"]
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- # )
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- # model_inputs["input_ids"] = input_ids
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  return model_inputs
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@@ -439,10 +428,7 @@ class HCXProcessor(ProcessorMixin):
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  def _replacer(match_obj):
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  # return self.image_token
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  num_query_tokens = next(_iterator)
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- return "".join(
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- [_target_token for _ in range(num_query_tokens)]
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- ) # vision_query_legnth 만큼 image token 을 복제
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-
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  return _replacer
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  text_inputs = {}
 
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  # vllm needs vision_query_lengths, but we don't need it
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  del model_inputs["vision_query_lengths_images"]
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  del model_inputs["vision_query_lengths_videos"]
 
 
 
 
 
 
 
 
 
 
 
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  return model_inputs
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  def _replacer(match_obj):
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  # return self.image_token
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  num_query_tokens = next(_iterator)
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+ return "".join([_target_token for _ in range(num_query_tokens)])
 
 
 
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  return _replacer
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  text_inputs = {}