1e00bed001
* Add example for Janus-Pro * Update model link * Fixes * Fixes --------- Co-authored-by: ATMxsp01 <shou.xu@intel.com> Co-authored-by: Yuwen Hu <yuwen.hu@intel.com>
130 lines
4.8 KiB
Python
130 lines
4.8 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import time
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import torch
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import argparse
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from ipex_llm.transformers import AutoModelForCausalLM
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from janus.models import VLChatProcessor
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from janus.utils.io import load_pil_images
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using generate() API for Janus-Pro model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="deepseek-ai/Janus-Pro-7B",
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help='The Hugging Face repo id for the Janus-Pro model to be downloaded'
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', or the path to the checkpoint folder')
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parser.add_argument('--image-path', type=str,
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help='The path to the image for inference.')
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parser.add_argument('--prompt', type=str,
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help='Prompt for inference.')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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parser.add_argument('--low-bit', type=str, default="sym_int4",
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help='Low bit optimizations that will be applied to the model.')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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model_name = os.path.basename(model_path)
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prompt = args.prompt
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image_path = args.image_path
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if prompt is None:
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if image_path is not None and os.path.exists(image_path):
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prompt = "Describe the image in detail."
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else:
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prompt = "What is AI?"
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# The following code is adapted from
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# https://github.com/deepseek-ai/Janus?tab=readme-ov-file#multimodal-understanding
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vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
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tokenizer = vl_chat_processor.tokenizer
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model_vl = AutoModelForCausalLM.from_pretrained(
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model_path,
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load_in_low_bit=args.low_bit,
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optimize_model=True,
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trust_remote_code=True,
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modules_to_not_convert=["vision_model"]
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).eval()
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model_vl = model_vl.half().to('xpu')
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if image_path is not None and os.path.exists(image_path):
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conversation = [
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{
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"role": "<|User|>",
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"content": f"<image_placeholder>\n{prompt}",
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"images": [image_path],
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},
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{"role": "<|Assistant|>", "content": ""},
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]
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else:
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conversation = [
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{
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"role": "<|User|>",
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"content": f"{prompt}",
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},
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{"role": "<|Assistant|>", "content": ""},
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]
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# load images and prepare for inputs
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pil_images = load_pil_images(conversation)
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prepare_inputs = vl_chat_processor(
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conversations=conversation, images=pil_images, force_batchify=True
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)
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prepare_inputs = prepare_inputs.to(device='xpu', dtype=torch.half)
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# run image encoder to get the image embeddings
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inputs_embeds = model_vl.prepare_inputs_embeds(**prepare_inputs)
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with torch.inference_mode():
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# ipex_llm model needs a warmup, then inference time can be accurate
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outputs = model_vl.language_model.generate(
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inputs_embeds=inputs_embeds,
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attention_mask=prepare_inputs.attention_mask,
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pad_token_id=tokenizer.eos_token_id,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=args.n_predict,
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do_sample=False,
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use_cache=True,
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)
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st = time.time()
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# run the model to get the response
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outputs = model_vl.language_model.generate(
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inputs_embeds=inputs_embeds,
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attention_mask=prepare_inputs.attention_mask,
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pad_token_id=tokenizer.eos_token_id,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=args.n_predict,
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do_sample=False,
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use_cache=True,
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)
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ed = time.time()
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reponse = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
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print(f'Inference time: {ed-st} s')
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print('-'*20, 'Input Image Path', '-'*20)
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print(image_path)
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print('-'*20, 'Input Prompt (Formatted)', '-'*20)
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print(f"{prepare_inputs['sft_format'][0]}")
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print('-'*20, 'Chat Output', '-'*20)
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print(reponse)
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