381d448ee2
* Remove model with optimize_model=False in NPU verified models tables, and remove related example * Remove experimental in run optimized model section title * Unify model table order & example cmd * Move embedding example to separate folder & update quickstart example link * Add Quickstart reference in main NPU readme * Small fix * Small fix * Move save/load examples under NPU/HF-Transformers-AutoModels * Add low-bit and polish arguments for LLM Python examples * Small fix * Add low-bit and polish arguments for Multi-Model examples * Polish argument for Embedding models * Polish argument for LLM CPP examples * Add low-bit and polish argument for Save-Load examples * Add accuracy tuning tips for examples * Update NPU qucikstart accuracy tuning with low-bit optimizations * Add save/load section to qucikstart * Update CPP example sample output to EN * Add installation regarding cmake for CPP examples * Small fix * Small fix * Small fix * Small fix * Small fix * Small fix * Unify max prompt length to 512 * Change recommended low-bit for Qwen2.5-3B-Instruct to asym_int4 * Update based on comments * Small fix
106 lines
3.7 KiB
Python
106 lines
3.7 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 torch
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import time
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import argparse
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from ipex_llm.transformers.npu_model import AutoModel, AutoModelForCausalLM
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from transformers import AutoTokenizer
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from transformers.utils import logging
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import requests
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from PIL import Image
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logger = logging.get_logger(__name__)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Predict Tokens using `chat()` API for npu model"
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)
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parser.add_argument(
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"--repo-id-or-model-path",
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type=str,
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default="openbmb/MiniCPM-Llama3-V-2_5",
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help="The huggingface repo id for the MiniCPM-Llama3-V-2_5 model to be downloaded"
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", or the path to the huggingface checkpoint folder.",
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)
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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 this image?",
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help='Prompt to infer')
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parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict.")
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parser.add_argument("--max-context-len", type=int, default=1024)
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parser.add_argument("--max-prompt-len", type=int, default=512)
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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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parser.add_argument("--save-directory", type=str,
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required=True,
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help="The path of folder to save converted model, "
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"If path not exists, lowbit model will be saved there. "
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"Else, lowbit model will be loaded.",
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)
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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 = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.float16,
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trust_remote_code=True,
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attn_implementation="eager",
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load_in_low_bit=args.low_bit,
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optimize_model=True,
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max_context_len=args.max_context_len,
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max_prompt_len=args.max_prompt_len,
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save_directory=args.save_directory
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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print("-" * 80)
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print("done")
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msgs = [{'role': 'user', 'content': args.prompt}]
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image_path = args.image_url_or_path
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if os.path.exists(image_path):
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image = Image.open(image_path).convert('RGB')
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else:
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image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB')
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st = time.time()
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res = model.chat(
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image=image,
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msgs=msgs,
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tokenizer=tokenizer,
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sampling=True,
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temperature=0.7,
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# system_prompt='' # pass system_prompt if needed
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)
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end = time.time()
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Input', '-'*20)
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print(image_path)
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print('-'*20, 'Prompt', '-'*20)
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print(args.prompt)
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output_str = res
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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print("done")
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print("success shut down")
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