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generate.py
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generate.py
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#
# 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.
#
import torch
import time
import argparse
import numpy as np
from ipex_llm.transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
# you could tune the prompt based on your own model,
# here the prompt tuning refers to https://huggingface.co/databricks/dolly-v2-12b/blob/main/instruct_pipeline.py#L15
DOLLY_V2_PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Dolly v2 model')
parser.add_argument('--repo-id-or-model-path', type=str, default="databricks/dolly-v2-12b",
help='The huggingface repo id for the Dolly v2 model to be downloaded'
', or the path to the huggingface checkpoint folder')
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')
args = parser.parse_args()
model_path = args.repo_id_or_model_path
# Load model in 4 bit,
# which convert the relevant layers in the model into INT4 format
# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
model = AutoModelForCausalLM.from_pretrained(model_path,
load_in_4bit=True,
trust_remote_code=True)
model = model.to('xpu')
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
# Generate predicted tokens
with torch.inference_mode():
prompt = DOLLY_V2_PROMPT_FORMAT.format(prompt=args.prompt)
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
end_key_token_id=tokenizer.encode("### End")[0]
st = time.time()
# if your selected model is capable of utilizing previous key/value attentions
# to enhance decoding speed, but has `"use_cache": false` in its model config,
# it is important to set `use_cache=True` explicitly in the `generate` function
# to obtain optimal performance with IPEX-LLM INT4 optimizations
output = model.generate(input_ids,
max_new_tokens=args.n_predict,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=end_key_token_id)
torch.xpu.synchronize()
end = time.time()
output = output.cpu()
end_token_position = None
end_token_positions = np.where(output[0] == end_key_token_id)[0]
if len(end_token_positions) > 0:
end_token_position = end_token_positions[0]
output_str = tokenizer.decode(output[0][:end_token_position], skip_special_tokens=False)
print(f'Inference time: {end-st} s')
print('-'*20, 'Prompt', '-'*20)
print(prompt)
print('-'*20, 'Output', '-'*20)
print(output_str)