dcbf4d3d0a
* Add phi-3-vision example (HF-Automodels) * fix * fix * fix * Add phi-3-vision CPU example (HF-Automodels) * add in readme * fix * fix * fix * fix * use fp8 for gpu example * remove eval
105 lines
4.9 KiB
Python
105 lines
4.9 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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import requests
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from PIL import Image
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoProcessor
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi-3 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="microsoft/Phi-3-vision-128k-instruct",
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help='The huggingface repo id for the phi-3-vision model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--image-url-or-path', type=str,
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default="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg",
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help='The URL or path to the image to infer')
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parser.add_argument('--prompt', type=str, default="What is in the image?",
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help='Prompt to infer')
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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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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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image_path = args.image_url_or_path
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# Load model in FP8,
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# which convert the relevant layers in the model into FP8 format
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# We here use FP8 instead of INT4 for better output
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# You could also try `'sym_int4'` for INT4, `'sym_int8'` for INT8 and `'fp6'` for FP6
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# `_attn_implementation="eager"` is required for phi-3-vision
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# `modules_to_not_convert=["vision_embed_tokens"]` and `model = model.half()` are for acceleration and are optional
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# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
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# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
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model = AutoModelForCausalLM.from_pretrained(model_path,
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trust_remote_code=True,
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load_in_low_bit="fp8",
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_attn_implementation="eager",
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modules_to_not_convert=["vision_embed_tokens"])
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model = model.half().to('xpu')
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# Load processor
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processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
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# here the message formatting refers to https://huggingface.co/microsoft/Phi-3-vision-128k-instruct#sample-inference-code
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messages = [
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{"role": "user", "content": "<|image_1|>\n{prompt}".format(prompt=args.prompt)},
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]
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prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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if os.path.exists(image_path):
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image = Image.open(image_path)
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else:
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image = Image.open(requests.get(image_path, stream=True).raw)
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# Generate predicted tokens
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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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inputs = processor(prompt, [image], return_tensors="pt")
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inputs = inputs.to('xpu')
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output = model.generate(**inputs,
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eos_token_id=processor.tokenizer.eos_token_id,
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num_beams=1,
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do_sample=False,
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max_new_tokens=args.n_predict,
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temperature=0.0)
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# start inference
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st = time.time()
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inputs = processor(prompt, [image], return_tensors="pt")
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inputs = inputs.to('xpu')
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output = model.generate(**inputs,
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eos_token_id=processor.tokenizer.eos_token_id,
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num_beams=1,
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do_sample=False,
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max_new_tokens=args.n_predict,
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temperature=0.0)
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end = time.time()
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print(f'Inference time: {end-st} s')
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output_str = processor.decode(output[0],
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skip_special_tokens=True,
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clean_up_tokenization_spaces=False)
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print('-'*20, 'Prompt', '-'*20)
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print(f'Message: {messages}')
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print(f'Image link/path: {image_path}')
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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