74 lines
2.5 KiB
Python
74 lines
2.5 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.npu_model import EmbeddingModel
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from transformers.utils import logging
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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 `generate()` 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="maidalun1020/bce-embedding-base_v1",
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help="The huggingface repo id for the bce-embedding 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('--prompt', type=str, default="'sentence_0', 'sentence_1'",
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help='Prompt to infer')
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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("--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 = EmbeddingModel(
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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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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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transpose_value_cache=not args.disable_transpose_value_cache,
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save_directory=args.save_directory
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)
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# list of sentences
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split_items = args.prompt.split(',')
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sentences = [item.strip().strip("'") for item in split_items]
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# extract embeddings
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st = time.time()
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embeddings = model.encode(sentences)
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end = time.time()
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print(f'Inference time: {end-st} s')
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print(embeddings)
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