440cfe18ed
* modify aquila * modify aquila2 * add baichuan * modify baichuan2 * modify blue-lm * modify chatglm3 * modify chinese-llama2 * modiy codellama * modify distil-whisper * modify dolly-v1 * modify dolly-v2 * modify falcon * modify flan-t5 * modify gpt-j * modify internlm * modify llama2 * modify mistral * modify mixtral * modify mpt * modify phi-1_5 * modify qwen * modify qwen-vl * modify replit * modify solar * modify starcoder * modify vicuna * modify voiceassistant * modify whisper * modify yi * modify aquila2 * modify baichuan * modify baichuan2 * modify blue-lm * modify chatglm2 * modify chatglm3 * modify codellama * modify distil-whisper * modify dolly-v1 * modify dolly-v2 * modify flan-t5 * modify llama2 * modify llava * modify mistral * modify mixtral * modify phi-1_5 * modify qwen-vl * modify replit * modify solar * modify starcoder * modify yi * correct the comments * remove cpu_embedding in code for whisper and distil-whisper * remove comment * remove cpu_embedding for voice assistant * revert modify voice assistant * modify for voice assistant * add comment for voice assistant * fix comments * fix comments
86 lines
3.7 KiB
Python
86 lines
3.7 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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import numpy as np
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from bigdl.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/databricks/dolly-v1-6b#generate-text
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DOLLY_V1_PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Dolly v1 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="databricks/dolly-v1-6b",
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help='The huggingface repo id for the Dolly v1 model 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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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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# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
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# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True)
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model = model.to('xpu')
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = DOLLY_V1_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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end_key_token_id=tokenizer.encode("### End")[0]
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st = time.time()
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# enabling `use_cache=True` allows the model to utilize the previous
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# key/values attentions to speed up decoding;
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# to obtain optimal performance with BigDL-LLM INT4 optimizations,
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# it is important to set use_cache=True for Dolly v1 models
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output = model.generate(input_ids,
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use_cache=True,
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max_new_tokens=args.n_predict,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=end_key_token_id)
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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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end_token_position = None
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end_token_positions = np.where(output[0] == end_key_token_id)[0]
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if len(end_token_positions) > 0:
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end_token_position = end_token_positions[0]
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output_str = tokenizer.decode(output[0][:end_token_position], skip_special_tokens=False)
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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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