105 lines
4.3 KiB
Python
105 lines
4.3 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 torch
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import time
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import argparse
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from ipex_llm.transformers.npu_model import AutoModelForCausalLM
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from transformers import AutoTokenizer
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from transformers.utils import logging
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import os
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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="openbmb/MiniCPM-1B-sft-bf16",
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help="The huggingface repo id for the MiniCPM 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("--lowbit-path", type=str,
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default="",
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help="The path to the lowbit model folder, leave blank if you do not want to save. \
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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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parser.add_argument('--prompt', type=str, default="What is AI?",
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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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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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if not args.lowbit_path or not os.path.exists(args.lowbit_path):
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model = AutoModelForCausalLM.from_pretrained(model_path,
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optimize_model=True,
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pipeline=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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torch_dtype=torch.float16,
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attn_implementation="eager",
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transpose_value_cache=not args.disable_transpose_value_cache,
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trust_remote_code=True)
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else:
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model = AutoModelForCausalLM.load_low_bit(
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args.lowbit_path,
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attn_implementation="eager",
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torch_dtype=torch.float16,
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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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pipeline=True,
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transpose_value_cache=not args.disable_transpose_value_cache,
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trust_remote_code=True
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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if args.lowbit_path and not os.path.exists(args.lowbit_path):
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model.save_low_bit(args.lowbit_path)
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print("-" * 80)
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print("done")
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with torch.inference_mode():
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print("finish to load")
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for i in range(5):
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prompt = "<用户>{}<AI>".format(args.prompt)
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_input_ids = tokenizer.encode(prompt, return_tensors="pt")
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print("input length:", len(_input_ids[0]))
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st = time.time()
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output = model.generate(
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_input_ids, max_new_tokens=args.n_predict, do_print=True
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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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input_str = tokenizer.decode(_input_ids[0], skip_special_tokens=False)
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print("-" * 20, "Input", "-" * 20)
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print(input_str)
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output_str = tokenizer.decode(output[0], skip_special_tokens=False)
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print("-" * 20, "Output", "-" * 20)
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print(output_str)
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print("-" * 80)
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print("done")
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print("success shut down")
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