Fewshot_LLM_Prompting.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Manual Prompting for Compositionality with Large Language Models \n",
        "\n",
        "## **[ Produced by ]**\n",
        "\n",
        "[Eunchan Lee of UNIST-LIL-LAB](https://github.com/purang2)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nr5CTloRas9w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "install\n",
        "- torch\n",
        "- pip install transformers"
      ],
      "metadata": {
        "id": "4XSpG7-PT347"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LX_l7Im6Wc59",
        "outputId": "14404710-7273-465d-ea35-ca825bf14279",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nimport gc\\nimport torch \\n  \\ngc.collect()\\ntorch.cuda.empty_cache()\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "''' #if your RAM down...\n",
        "import gc\n",
        "import torch \n",
        "  \n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Put datapair"
      ],
      "metadata": {
        "id": "KfKGHuv7LfF5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "few_src = [] \n",
        "few_tgt = []\n",
        "\n",
        "\n",
        "def put_datapair(src, tgt):\n",
        "  few_src.append(src)\n",
        "  few_tgt.append(tgt)\n",
        "\n",
        "\n",
        "# k=4\n",
        "'''\n",
        "put_datapair(\"k\", \"x\")\n",
        "put_datapair(\"kk\", \"xx\")\n",
        "put_datapair(\"kkk\", \"xxx\")\n",
        "put_datapair(\"kkkk\", \"xxxx\")\n",
        "'''\n",
        "\n",
        "\n",
        "# k=4 -> Manual example: Text style transfer\n",
        "#put_datapair(A,B): Style : A--> B\n",
        "put_datapair(\"South Korea's Capital is Seoul\", \"Seoul -> South Korea\")\n",
        "put_datapair(\"Mexico's Capital is Mexico City\", \"Mexico City -> Mexico\")\n",
        "put_datapair(\"Germany's Capital is Berlin\", \"Berlin -> Germany\")\n",
        "put_datapair(\"Uganda's Capital is Kampala\", \"Kampala -> Uganda\")\n",
        "\n",
        "\n",
        "\n",
        "print(few_src)\n",
        "print(few_tgt)\n"
      ],
      "metadata": {
        "id": "0Ki7stDebE_D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6eca1e10-6b8c-4ade-b951-c2ebb3adc7c0"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\"South Korea's Capital is Seoul\", \"Mexico's Capital is Mexico City\", \"Germany's Capital is Berlin\", \"Uganda's Capital is Kampala\"]\n",
            "['Seoul -> South Korea', 'Mexico City -> Mexico', 'Berlin -> Germany', 'Kampala -> Uganda']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prompt\n",
        "\n",
        "***Reference: [A Recipe for Arbitrary Text Style Transfer with Large Language Models, (Reif et al. (Google Research), ACL 2022)](https://aclanthology.org/2022.acl-short.94/)***"
      ],
      "metadata": {
        "id": "ZwMZvIpCKOiP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "in_context_text = ''\n",
        "\n",
        "for i in range(len(few_src)):\n",
        "  #in_context_text += f'Here is some text: {few_src[i]}. Here is a rewrite of the text, which is more simple: {few_tgt[i]}\\n'\n",
        "  in_context_text += f'Q: {few_src[i]}. \\nA: {few_tgt[i]}\\n\\n'\n",
        "\n",
        "print(in_context_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5LM18a7f_FQ",
        "outputId": "a57b05d5-e470-4244-959a-ecb31dd22608"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q: South Korea's Capital is Seoul. \n",
            "A: Seoul -> South Korea\n",
            "\n",
            "Q: Mexico's Capital is Mexico City. \n",
            "A: Mexico City -> Mexico\n",
            "\n",
            "Q: Germany's Capital is Berlin. \n",
            "A: Berlin -> Germany\n",
            "\n",
            "Q: Uganda's Capital is Kampala. \n",
            "A: Kampala -> Uganda\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "transformers model"
      ],
      "metadata": {
        "id": "N8qXu5MTKStT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM\n",
        "from transformers import pipeline\n",
        "\n",
        "\n",
        "# GPT-2 (This is the smallest version of GPT-2, with 124M parameters.) = \"gpt2\"\n",
        "# GPT-2 (XL) (1.5B parameters.) = \"gpt2-xl\"\n",
        "# T5 (base, with 220 million parameters.) = \"t5-base\"\n",
        "# T5 (3B parameters.) = \"t5-3b\"\n",
        "# T5 (11B parameters.) = \"t5-11b\" -> 45.2GB (Colab may not cover this size..)\n",
        "# GPT-J (6B parameters.) = \"EleutherAI/gpt-j-6B\"\n",
        "#model_zoo = [\"gpt2\",\"gpt2-xl\",\"t5-base\",\"t5-3b\",\"t5-11b\",\"EleutherAI/gpt-j-6B\"]\n",
        "\n",
        "# BLOOM ([Recommended] This is popular GPT-Like open-source model. 7B parameters.) [Decoder-only]= \"bigscience/bloom-7b1\"\n",
        "# BLOOM (6B parameters.) [Decoder-only]= \"bigscience/test-bloomd-6b3\"\n",
        "# T-Zero-plus-plus (popular 0-shot LM, 11B parameters.) [Encoder-decoder]= \"bigscience/T0pp\"\n",
        "# LLAMA-7B ([Recommended] This is popular GPT-Like open-source model.but when during test i got some errors.) [Decoder-only]= \"decapoda-research/llama-7b-hf\"\n",
        "# T-Zero (popular 0-shot LM, 11B parameters.) [Encoder-decoder]=\"bigscience/T0\"\n",
        "# In-context learning may work well at least 1 billions parameters model. -eunchan-\n",
        "\n",
        "model_zoo = [\"bigscience/bloom-7b1\",\"bigscience/test-bloomd-6b3\",\"bigscience/T0pp\",\"decapoda-research/llama-7b-hf\",\"bigscience/T0\"]\n",
        "\n",
        "#https://huggingface.co/bigscience/T0_3B\n",
        "\n",
        "\n",
        "model_name = model_zoo[0]\n",
        "\n",
        "\n",
        "if model_name.find('t5') > -1 or model_name.lower().find('t0') > -1: #model = t5, t0 [encoder-decoder]\n",
        "  tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "  model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
        "  nlg_pipeline = pipeline('text2text-generation',model=model, tokenizer=tokenizer)\n",
        "\n",
        "else: #model = gpt,etc [decoder-only]\n",
        "  tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "  model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "  #nlg_pipeline = pipeline(model=model, tokenizer=tokenizer)"
      ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/222 [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "model inference"
      ],
      "metadata": {
        "id": "1mfaqHtbKWYD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#test_input_text = 'Q: ~~~~~~. \\nA:'\n",
        "test_input_text = \"Italy's Capital is Roma\"\n",
        "\n",
        "test_output_length = 8 #token length  you should change for your desired output size.\n",
        "\n",
        "\n",
        "if model_name.find('t5') > -1 or model_name.lower().find('t0') > -1: #model = t5, t0 [encoder-decoder]\n",
        "  def generate_text(pipe, text, num_return_sequences=5, max_length=512):\n",
        "    text = f\"{text}\"\n",
        "    out = pipe(text, num_return_sequences=num_return_sequences, max_length=max_length, num_beams=5, no_repeat_ngram_size=2,)\n",
        "    return [x['generated_text'] for x in out]\n",
        "\n",
        "  #target_text = 'kjkjkjkj'\n",
        "  #src_text = in_context_text + f\"Here is some text: {test_input_text}. Here is a rewrite of the text, which is more simple: \"\n",
        "  src_text = f\"Q:{test_input_text}\\nA:\"\n",
        "  \n",
        "  print(\"Input text:\", src_text)\n",
        "  test_output_text = generate_text(nlg_pipeline, src_text, num_return_sequences=1, max_length=test_output_length)\n",
        "\n",
        "\n",
        "  #you can cook this output anything you want!  \n",
        "  print(test_output_text)\n",
        "\n",
        "\n",
        "else:  #model = GPT, T0, ETC\n",
        "  #src_text = in_context_text + f\"Here is some text: {test_input_text}. Here is a rewrite of the text, which is more simple: \"\n",
        "  src_text = f\"Q:{test_input_text}\\nA:\"\n",
        "  tokens = tokenizer.encode(src_text, return_tensors='pt')\n",
        "  gen_tokens = model.generate(tokens, do_sample=True, temperature=0.8, max_length=len(tokens[0])+test_output_length)\n",
        "  generated = tokenizer.batch_decode(gen_tokens)[0] \n",
        "  #test_output_text = generated[generated.rfind('more simple:')+12:] #here is some text: 이하를 저장\n",
        "  test_output_text = generated\n",
        "  print(generated)\n",
        "\n",
        "  #you can cook this output anything you want!\n",
        "  print(test_output_text)\n"
      ],
      "metadata": {
        "id": "n71_yzKjz-gL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}