166 lines
6.1 KiB
Python
166 lines
6.1 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 os
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from typing import List
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import fire
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import torch
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import transformers
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from datasets import load_dataset
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import accelerate
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from ipex_llm.transformers.lisa import DynamicLayerActivationCallback
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from transformers import AutoTokenizer
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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, '..')
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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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from ipex_llm.transformers import AutoModelForCausalLM
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from ipex_llm.utils.common import invalidInputError
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def train(
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# model/data params
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base_model: str = "meta-llama/Llama-2-7b-hf", # the only required argument, default to be "meta-llama/Llama-2-7b-hf"
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data_path: str = "yahma/alpaca-cleaned",
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output_dir: str = "./ipex-llm-lisa-alpaca",
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# training hyperparams
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bf16: bool = True, # default to bf16
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batch_size: int = 128,
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micro_batch_size: int = 8, # default to be 8, limited by GPU memory
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num_epochs: int = 1,
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learning_rate: float = 2e-5,
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cutoff_len: int = 256,
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val_set_size: int = 2000,
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# llm hyperparams
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train_on_inputs: bool = True, # if False, masks out inputs in loss
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add_eos_token: bool = False,
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group_by_length: bool = False, # faster, but produces an odd training loss curve
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resume_from_checkpoint: str = None, # either training checkpoint or final adapter
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prompt_template_name: str = "alpaca", # The prompt template to use, will default to alpaca.
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gradient_checkpointing: bool = False,
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deepspeed: str = None,
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training_mode: str = "lisa",
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lisa_activated_layers: int = 1,
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lisa_interval_steps: int = 20,
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):
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invalidInputError(training_mode == "lisa",
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f"This example is for lisa training mode, but got training_mode={training_mode}.")
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print(
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f"Training Alpaca-LoRA model with params:\n"
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f"base_model: {base_model}\n"
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f"data_path: {data_path}\n"
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f"output_dir: {output_dir}\n"
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f"batch_size: {batch_size}\n"
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f"micro_batch_size: {micro_batch_size}\n"
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f"num_epochs: {num_epochs}\n"
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f"learning_rate: {learning_rate}\n"
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f"cutoff_len: {cutoff_len}\n"
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f"val_set_size: {val_set_size}\n"
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f"train_on_inputs: {train_on_inputs}\n"
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f"add_eos_token: {add_eos_token}\n"
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f"group_by_length: {group_by_length}\n"
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f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
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f"prompt template: {prompt_template_name}\n"
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f"training_mode: {training_mode}\n"
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f"lisa_activated_layers: {lisa_activated_layers}\n"
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f"lisa_interval_steps: {lisa_interval_steps}\n"
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)
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assert (
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base_model
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), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
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gradient_accumulation_steps = batch_size // micro_batch_size
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prompter = Prompter(prompt_template_name)
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model = AutoModelForCausalLM.from_pretrained(
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base_model,
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load_in_low_bit="bf16",
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optimize_model=True,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True
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)
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model = model.to("xpu")
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tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
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tokenizer.pad_token_id = (
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0 # unk. we want this to be different from the eos token
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)
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tokenizer.padding_side = "left" # Allow batched inference
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print(model)
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if data_path.endswith(".json") or data_path.endswith(".jsonl"):
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data = load_dataset("json", data_files=data_path)
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else:
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data = load_dataset(data_path)
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train_data, val_data = get_train_val_data(data, tokenizer, prompter, train_on_inputs,
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add_eos_token, cutoff_len, val_set_size, seed=42)
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trainer_callbacks = []
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# Instantiate the callback
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dynamic_layer_activation_callback = DynamicLayerActivationCallback(
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n_layers=lisa_activated_layers, # Number of layers to activate
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interval_steps = lisa_interval_steps, # Step interval to update active layers
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model = model
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)
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trainer_callbacks.append(dynamic_layer_activation_callback)
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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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eval_dataset=val_data,
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args=transformers.TrainingArguments(
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per_device_train_batch_size=micro_batch_size,
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gradient_accumulation_steps=gradient_accumulation_steps,
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max_grad_norm=0.3,
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num_train_epochs=num_epochs,
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learning_rate=learning_rate,
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lr_scheduler_type="cosine",
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bf16=bf16, # ensure training more stable
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logging_steps=10,
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optim="adamw_hf",
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evaluation_strategy="steps" if val_set_size > 0 else "no",
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save_strategy="steps",
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eval_steps=200 if val_set_size > 0 else None,
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save_steps=200,
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output_dir=output_dir,
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load_best_model_at_end=True if val_set_size > 0 else False,
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group_by_length=group_by_length,
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gradient_checkpointing=gradient_checkpointing,
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deepspeed=deepspeed,
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save_safetensors=False,
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),
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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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callbacks=trainer_callbacks
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)
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model.config.use_cache = False
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trainer.train(resume_from_checkpoint=resume_from_checkpoint)
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# model.save_pretrained(output_dir)
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trainer.save_model()
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if __name__ == "__main__":
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fire.Fire(train)
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