120 lines
5.1 KiB
Python
120 lines
5.1 KiB
Python
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#
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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 intel_extension_for_pytorch as ipex
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import time
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import argparse
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
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DEFAULT_SYSTEM_PROMPT = """\
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"""
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def get_prompt(message: str, chat_history: list[tuple[str, str]],
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system_prompt: str) -> str:
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texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
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# The first user input is _not_ stripped
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do_strip = False
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for user_input, response in chat_history:
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user_input = user_input.strip() if do_strip else user_input
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do_strip = True
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texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
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message = message.strip() if do_strip else message
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texts.append(f'{message} [/INST]')
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return ''.join(texts)
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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="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('--gpu-num', type=int, default=2, help='GPU number to use')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True)
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model_layers = ['model.embed_tokens']
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for i in range(model.config.num_hidden_layers):
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model_layers.append(f'model.layers.{i}')
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model_layers = model_layers + ['model.norm', 'lm_head']
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device_map = {}
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split_len = len(model_layers) // args.gpu_num
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for i in range(args.gpu_num):
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device_map.update({key: f'xpu:{i}' for key in model_layers[split_len * i: split_len * (i + 1)]})
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if i == args.gpu_num - 1:
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device_map.update({key: f'xpu:{i}' for key in model_layers[split_len * (i + 1): ]})
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from accelerate import dispatch_model
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model = dispatch_model(
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model,
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device_map=device_map,
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offload_dir=None,
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skip_keys=["past_key_value", "past_key_values"],
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)
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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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prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu:0')
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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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do_sample=False,
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max_new_tokens=args.n_predict)
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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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# start inference
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st = 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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torch.xpu.synchronize()
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end = time.time()
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output = output.cpu()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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