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Support minicpm-1B in level0 pipeline (#12297)
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python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/minicpm.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,105 @@ | ||
| # | ||
| # Copyright 2016 The BigDL Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
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| import torch | ||
| import time | ||
| import argparse | ||
| from ipex_llm.transformers.npu_model import AutoModelForCausalLM | ||
| from transformers import AutoTokenizer | ||
| from transformers.utils import logging | ||
| import os | ||
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| logger = logging.get_logger(__name__) | ||
|
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser( | ||
| description="Predict Tokens using `generate()` API for npu model" | ||
| ) | ||
| parser.add_argument( | ||
| "--repo-id-or-model-path", | ||
| type=str, | ||
| default="openbmb/MiniCPM-1B-sft-bf16", | ||
| help="The huggingface repo id for the MiniCPM model to be downloaded" | ||
| ", or the path to the huggingface checkpoint folder", | ||
| ) | ||
| parser.add_argument("--lowbit-path", type=str, | ||
| default="", | ||
| help="The path to the lowbit model folder, leave blank if you do not want to save. \ | ||
| If path not exists, lowbit model will be saved there. \ | ||
| Else, lowbit model will be loaded.", | ||
| ) | ||
| parser.add_argument('--prompt', type=str, default="What is AI?", | ||
| help='Prompt to infer') | ||
| parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict") | ||
| parser.add_argument("--max-context-len", type=int, default=1024) | ||
| parser.add_argument("--max-prompt-len", type=int, default=512) | ||
| parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False) | ||
|
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| args = parser.parse_args() | ||
| 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): | ||
| model = AutoModelForCausalLM.from_pretrained(model_path, | ||
| optimize_model=True, | ||
| pipeline=True, | ||
| max_context_len=args.max_context_len, | ||
| max_prompt_len=args.max_prompt_len, | ||
| torch_dtype=torch.float16, | ||
| attn_implementation="eager", | ||
| transpose_value_cache=not args.disable_transpose_value_cache, | ||
| trust_remote_code=True) | ||
| else: | ||
| model = AutoModelForCausalLM.load_low_bit( | ||
| args.lowbit_path, | ||
| attn_implementation="eager", | ||
| torch_dtype=torch.float16, | ||
| max_context_len=args.max_context_len, | ||
| max_prompt_len=args.max_prompt_len, | ||
| pipeline=True, | ||
| transpose_value_cache=not args.disable_transpose_value_cache, | ||
| trust_remote_code=True | ||
| ) | ||
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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): | ||
| model.save_low_bit(args.lowbit_path) | ||
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| print("-" * 80) | ||
| print("done") | ||
| with torch.inference_mode(): | ||
| print("finish to load") | ||
| for i in range(5): | ||
| prompt = "<用户>{}<AI>".format(args.prompt) | ||
| _input_ids = tokenizer.encode(prompt, return_tensors="pt") | ||
| print("input length:", len(_input_ids[0])) | ||
| st = time.time() | ||
| output = model.generate( | ||
| _input_ids, max_new_tokens=args.n_predict, do_print=True | ||
| ) | ||
| end = time.time() | ||
| print(f"Inference time: {end-st} s") | ||
| input_str = tokenizer.decode(_input_ids[0], skip_special_tokens=False) | ||
| print("-" * 20, "Input", "-" * 20) | ||
| print(input_str) | ||
| output_str = tokenizer.decode(output[0], skip_special_tokens=False) | ||
| print("-" * 20, "Output", "-" * 20) | ||
| print(output_str) | ||
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| print("-" * 80) | ||
| print("done") | ||
| print("success shut down") |
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