3fe2ea3081
* first commit * update example * fix style * update example * embedding as const * fix generate * code refactor * meet code review * fix style * change max_output_len to max_context_len * fix all-in-one * fix example * add check for new tokens
104 lines
3.6 KiB
Python
104 lines
3.6 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 the 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("--disable-transpose-value-cache", action="store_true", default=False)
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parser.add_argument("--intra-pp", type=int, default=2)
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parser.add_argument("--inter-pp", type=int, default=2)
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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.float32,
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trust_remote_code=True,
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attn_implementation="eager",
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load_in_low_bit="sym_int4",
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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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intra_pp=args.intra_pp,
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inter_pp=args.inter_pp,
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transpose_value_cache=not args.disable_transpose_value_cache,
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modules_to_not_convert=['vpm', 'resampler']
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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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