121 lines
4.8 KiB
Python
121 lines
4.8 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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from transformers import BitsAndBytesConfig
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from bigdl.llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training, 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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from bigdl.llm.utils.isa_checker import ISAChecker
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current_dir = os.path.dirname(os.path.realpath(__file__))
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common_util_path = os.path.join(current_dir, '..', '..', 'GPU', 'LLM-Finetuning')
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import sys
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sys.path.append(common_util_path)
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from common.utils import Prompter, get_train_val_data
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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="yahma/alpaca-cleaned")
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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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if dataset_path.endswith(".json") or dataset_path.endswith(".jsonl"):
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data = load_dataset("json", data_files=dataset_path)
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else:
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data = load_dataset(dataset_path)
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# For illustration purpose, only use part of data to train
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data = data["train"].train_test_split(train_size=0.1, shuffle=False)
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# Data processing
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prompter = Prompter("alpaca")
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train_data, _ = get_train_val_data(data, tokenizer, prompter, train_on_inputs=True,
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add_eos_token=False, cutoff_len=256, val_set_size=0, seed=42)
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=False,
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bnb_4bit_quant_type="int4", # nf4 not supported on cpu yet
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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model = AutoModelForCausalLM.from_pretrained(model_path,
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quantization_config=bnb_config, )
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# below is also supported
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# model = AutoModelForCausalLM.from_pretrained(model_path,
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# # nf4 not supported on cpu yet
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# load_in_low_bit="sym_int4",
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# optimize_model=False,
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# torch_dtype=torch.bfloat16,
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# modules_to_not_convert=["lm_head"], )
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model = model.to('cpu')
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model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=False)
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model.enable_input_require_grads()
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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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# To avoid only one core is used on client CPU
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isa_checker = ISAChecker()
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bf16_flag = isa_checker.check_avx512()
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trainer = transformers.Trainer(
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model=model,
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train_dataset=train_data,
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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-4,
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save_steps=100,
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bf16=bf16_flag,
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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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# Inputs are dynamically padded to the maximum length of a batch
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data_collator=transformers.DataCollatorForSeq2Seq(
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tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
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),
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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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