236 lines
7.4 KiB
Plaintext
236 lines
7.4 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1a884871-7a65-4501-9063-c85ad260d0da",
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"metadata": {},
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"source": [
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"このnotebookはstockmark/stockmark-13bのモデルをkunishou/databricks-dolly-15k-jaのデータセットを用いてLoRA tuningするためのコードの例です。A100またはH100のGPUを用いることを想定しています。\n",
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"\n",
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"- モデル：https://huggingface.co/stockmark/stockmark-13b\n",
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"- データ：https://github.com/kunishou/databricks-dolly-15k-ja\n",
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"\n",
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"以下の例では、学習を1 epochを行います。A100 GPUで実行すると30分ほどかかります。\n",
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"\n",
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"また、ここで用いられているハイパーパラメータは最適化されたものではありませんので、必要に応じて調整してください。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "93b3f4b5-2825-4ef3-a0ee-7a60155aee5d",
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"metadata": {},
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"source": [
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"# 準備"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6a694ba9-a0fa-4f14-81cf-f35f683ba889",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import datasets\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments\n",
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"from peft import get_peft_model, LoraConfig, PeftModel, PeftConfig\n",
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"\n",
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"model_name = \"stockmark/stockmark-13b\"\n",
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"peft_model_name = \"stockmark-13b-adapter\"\n",
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"\n",
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"prompt_template = \"\"\"### Instruction:\n",
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"{instruction}\n",
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"\n",
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"### Input:\n",
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"{input}\n",
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"\n",
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"### Response:\n",
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"\"\"\"\n",
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"\n",
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"def encode(sample):\n",
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" prompt = prompt_template.format(instruction=sample[\"instruction\"], input=sample[\"input\"])\n",
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" target = sample[\"output\"]\n",
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" input_ids_prompt, input_ids_target = tokenizer([prompt, target], add_special_tokens=False).input_ids\n",
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" input_ids_prompt = [ tokenizer.bos_token_id ] + input_ids_prompt\n",
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" input_ids_target = input_ids_target + [ tokenizer.eos_token_id ]\n",
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" input_ids = input_ids_prompt + input_ids_target\n",
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" labels = input_ids.copy()\n",
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" labels[:len(input_ids_prompt)] = [-100] * len(input_ids_prompt) # ignore label tokens in a prompt for loss calculation\n",
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" return {\"input_ids\": input_ids, \"labels\": labels}\n",
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"\n",
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"def get_collator(tokenizer, max_length):\n",
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" def collator(batch):\n",
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" batch = [{ key: value[:max_length] for key, value in sample.items() } for sample in batch ]\n",
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" batch = tokenizer.pad(batch)\n",
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" batch[\"labels\"] = [ e + [-100] * (len(batch[\"input_ids\"][0]) - len(e)) for e in batch[\"labels\"] ]\n",
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" batch = { key: torch.tensor(value) for key, value in batch.items() }\n",
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" return batch\n",
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"\n",
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" return collator"
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]
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},
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{
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"cell_type": "markdown",
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"id": "51e6cfcf-1ac1-400e-a4bc-ea64375d0f9e",
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"metadata": {},
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"source": [
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"# データセットとモデルのロード"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3ac80067-4e60-46c4-90da-05647cf96ccd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load_tokenizer\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
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"\n",
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"# prepare dataset\n",
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"dataset_name = \"kunishou/databricks-dolly-15k-ja\"\n",
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"dataset = datasets.load_dataset(dataset_name)\n",
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"dataset = dataset.map(encode)\n",
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"dataset = dataset[\"train\"].train_test_split(0.1)\n",
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"train_dataset = dataset[\"train\"]\n",
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"val_dataset = dataset[\"test\"]\n",
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"\n",
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"# load model\n",
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"model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", torch_dtype=torch.bfloat16)\n",
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"\n",
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"peft_config = LoraConfig(\n",
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" task_type=\"CAUSAL_LM\",\n",
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" inference_mode=False,\n",
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" target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n",
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" r=16,\n",
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" lora_alpha=32,\n",
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" lora_dropout=0.05\n",
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")\n",
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"\n",
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"model = get_peft_model(model, peft_config)\n",
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"model.print_trainable_parameters()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9b471da0-7fba-4127-8b07-22da4cbee6a9",
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"metadata": {},
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"source": [
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"# LoRA Tuning"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b9bafa12-538c-4abb-b8b3-bffeb0990b46",
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"metadata": {},
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"outputs": [],
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"source": [
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"training_args = TrainingArguments(\n",
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" output_dir=\"./log_stockmark_13b\",\n",
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" learning_rate=2e-4,\n",
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" per_device_train_batch_size=2,\n",
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" gradient_accumulation_steps=8,\n",
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" per_device_eval_batch_size=16,\n",
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" num_train_epochs=1,\n",
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" logging_strategy='steps',\n",
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" logging_steps=10,\n",
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" save_strategy='epoch',\n",
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" evaluation_strategy='epoch',\n",
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" load_best_model_at_end=True,\n",
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" metric_for_best_model=\"eval_loss\",\n",
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" greater_is_better=False,\n",
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" save_total_limit=2\n",
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")\n",
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"\n",
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"trainer = Trainer(\n",
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" model=model,\n",
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" args=training_args,\n",
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" train_dataset=train_dataset,\n",
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" eval_dataset=val_dataset,\n",
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" data_collator=get_collator(tokenizer, 320)\n",
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")\n",
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"\n",
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"# LoRA tuning\n",
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"trainer.train()\n",
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"\n",
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"# save model\n",
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"model = trainer.model\n",
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"model.save_pretrained(peft_model_name)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a3f80a8e-1ac2-4bdc-8232-fe0ee18ffff5",
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"metadata": {},
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"source": [
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"# 学習したモデルのロード（Optional）\n",
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"異なるセッションでモデルを読み込む場合、まず最初の準備のセクションのコードを実行して、このコードを実行してください。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "43241395-3035-4cb9-8c1c-45ffe8cd48be",
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
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"model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\", torch_dtype=torch.bfloat16)\n",
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"model = PeftModel.from_pretrained(model, peft_model_name)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2ce4db1f-9bad-4c8e-9c04-d1102b299f24",
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"metadata": {},
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"source": [
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"# 推論"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d7d6359b-e0ac-49df-a178-39bb9f79ca93",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = prompt_template.format(instruction=\"自然言語処理とは？\", input=\"\")\n",
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"\n",
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"inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
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"with torch.no_grad():\n",
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" tokens = model.generate(\n",
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" **inputs,\n",
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" max_new_tokens=128,\n",
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" do_sample=True,\n",
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" temperature=0.7\n",
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" )\n",
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"\n",
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"output = tokenizer.decode(tokens[0], skip_special_tokens=True)\n",
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"print(output)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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