123 lines
5.3 KiB
Python
123 lines
5.3 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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# Some parts of this file is adapted from
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# https://github.com/TimDettmers/bitsandbytes/blob/0.39.1/bitsandbytes/nn/modules.py
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# which is licensed under the MIT license:
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#
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# MIT License
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#
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import os
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import torch
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from transformers import AutoModelForCausalLM, LlamaTokenizer, AutoTokenizer
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import deepspeed
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from ipex_llm import optimize_model
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import torch
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import intel_extension_for_pytorch as ipex
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import time
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import argparse
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 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('--prompt', type=str, default="Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun",
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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('--local_rank', type=int, default=0, help='this is automatically set when using deepspeed launcher')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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world_size = int(os.getenv("WORLD_SIZE", "1"))
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local_rank = int(os.getenv("RANK", "-1")) # RANK is automatically set by CCL distributed backend
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if local_rank == -1: # args.local_rank is automatically set by deepspeed subprocess command
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local_rank = args.local_rank
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# Native Huggingface transformers loading
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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device_map={"": "cpu"},
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low_cpu_mem_usage=True,
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torch_dtype=torch.float16,
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trust_remote_code=True,
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use_cache=True
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)
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# Parallelize model on deepspeed
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model = deepspeed.init_inference(
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model,
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mp_size = world_size,
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dtype=torch.float16,
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replace_method="auto"
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)
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# Apply IPEX-LLM INT4 optimizations on transformers
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model = optimize_model(model.module.to(f'cpu'), low_bit='sym_int4')
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model = model.to(f'cpu:{local_rank}')
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print(model)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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# Batch tokenizing
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prompt = args.prompt
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(f'cpu:{local_rank}')
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# ipex-llm model needs a warmup, then inference time can be accurate
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict,
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use_cache=True)
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# start inference
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start = time.time()
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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with IPEX-LLM INT4 optimizations
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output = model.generate(input_ids,
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do_sample=False,
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max_new_tokens=args.n_predict)
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end = time.time()
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if local_rank == 0:
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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print(f'Inference time: {end - start} s')
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