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
4.4 KiB
Python
106 lines
4.4 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 ipex_llm.utils.common.log4Error import invalidInputError
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# you could tune the prompt based on your own model,
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LLAMA2_PROMPT_FORMAT = """<s> [INST] <<SYS>>
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<</SYS>>
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{prompt} [/INST]
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Example of saving and loading the optimized model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
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help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--save-directory', type=str, default=None,
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help='The path to save the low-bit model.')
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parser.add_argument('--load-directory', type=str, default=None,
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help='The path to load the low-bit model.')
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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,
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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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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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save_directory = args.save_directory
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load_directory = args.load_directory
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if save_directory:
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# first time to load and save
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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=save_directory
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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tokenizer.save_pretrained(save_directory)
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print(f"Finish to load model from {model_path} and save to {save_directory}")
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elif load_directory:
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# load low-bit model
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model = AutoModelForCausalLM.load_low_bit(
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load_directory,
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attn_implementation="eager",
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torch_dtype=torch.float16,
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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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)
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tokenizer = AutoTokenizer.from_pretrained(load_directory, trust_remote_code=True)
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print(f"Finish to load model from {load_directory}")
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else:
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invalidInputError(False,
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"Both `--save-directory` and `--load-directory` are None, please provide one of this.")
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# Generate predicted tokens
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with torch.inference_mode():
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for i in range(3):
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prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
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_input_ids = tokenizer.encode(prompt, return_tensors="pt")
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st = time.time()
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output = model.generate(
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_input_ids, num_beams=1, do_sample=False, max_new_tokens=args.n_predict
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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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