404e101ded
* Support qa-lora * init * update * update * update * update * update * update merge * update * fix style & update scripts * update * address comments * fix typo * fix typo --------- Co-authored-by: Yang Wang <yang3.wang@intel.com>
86 lines
3.5 KiB
Python
86 lines
3.5 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 os
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import transformers
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from transformers import LlamaTokenizer
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import intel_extension_for_pytorch as ipex
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from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training,\
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LoraConfig
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from bigdl.llm.transformers import AutoModelForCausalLM
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from datasets import load_dataset
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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-hf",
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help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-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('--dataset', type=str, default="Abirate/english_quotes")
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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dataset_path = args.dataset
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tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
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data = load_dataset(dataset_path)
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data = data.map(lambda samples: tokenizer(samples["quote"]), batched=True)
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_low_bit="nf4",
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optimize_model=False,
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torch_dtype=torch.float16,
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modules_to_not_convert=["lm_head"],)
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model = model.to('xpu')
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# Enable gradient_checkpointing if your memory is not enough,
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# it will slowdown the training speed
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# model.gradient_checkpointing_enable()
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model = prepare_model_for_kbit_training(model)
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config = LoraConfig(
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r=8,
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lora_alpha=32,
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target_modules=["q_proj", "k_proj", "v_proj"],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM"
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)
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model = get_peft_model(model, config)
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tokenizer.pad_token_id = 0
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tokenizer.padding_side = "left"
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trainer = transformers.Trainer(
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model=model,
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train_dataset=data["train"],
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args=transformers.TrainingArguments(
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per_device_train_batch_size=4,
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gradient_accumulation_steps= 1,
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warmup_steps=20,
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max_steps=200,
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learning_rate=2e-5,
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save_steps=100,
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bf16=True, # bf16 is more stable in training
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logging_steps=20,
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output_dir="outputs",
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optim="adamw_hf", # paged_adamw_8bit is not supported yet
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# gradient_checkpointing=True, # can further reduce memory but slower
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),
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data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
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)
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model.config.use_cache = False # silence the warnings. Please re-enable for inference!
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result = trainer.train()
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print(result)
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