Synergy_llm.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -Uqqq pip\n",
        "!pip install -qqq bitsandbytes\n",
        "!pip install -qqq torch\n",
        "!pip install -qqq -U git+https://github.com/huggingface/transformers.git@e03a9cc\n",
        "!pip install -qqq -U git+https://github.com/huggingface/peft.git@42a184f\n",
        "!pip install -qqq -U git+https://github.com/huggingface/accelerate.git@c9fbb71\n",
        "!pip install -qqq datasets\n",
        "!pip install -qqq loralib\n",
        "!pip install -qqq einops"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7yr6JXpYO7h",
        "outputId": "d7eb843d-a8ce-414d-f919-79f93ae72155"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33m  WARNING: Did not find branch or tag 'e03a9cc', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m55.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33m  WARNING: Did not find branch or tag '42a184f', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33m  WARNING: Did not find branch or tag 'c9fbb71', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.7/536.7 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import os\n",
        "from pprint import pprint\n",
        "import bitsandbytes as bnb\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import transformers\n",
        "from datasets import load_dataset\n",
        "from huggingface_hub import notebook_login\n",
        "from peft import (\n",
        "    LoraConfig,\n",
        "    PeftConfig,\n",
        "    PeftModel,\n",
        "    get_peft_model,\n",
        "    prepare_model_for_kbit_training\n",
        ")\n",
        "from transformers import (\n",
        "    AutoConfig,\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig\n",
        ")\n",
        "\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
      ],
      "metadata": {
        "id": "Z6v-eTdZYRpu"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
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          "height": 160,
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        "outputId": "ebfd9a17-60d5-474e-8d87-70f5ec799363"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = \"vilsonrodrigues/falcon-7b-instruct-sharded\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    MODEL_NAME,\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
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        "id": "1NUpNu2EhBt9",
        "outputId": "88b9ca05-c11e-43ef-c8c1-2596bf712ae8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded:\n",
            "- configuration_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded:\n",
            "- modeling_falcon.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "display_data",
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        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f421fbb37d6944089300e733821c8712"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a0cfee906ea42008b7a0f95e3690ac0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "459fc79cb67945e7a16b18d5b5387f05"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of FalconForCausalLM were not initialized from the model checkpoint at vilsonrodrigues/falcon-7b-instruct-sharded and are newly initialized: ['lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bc405b3a4fc4b8e90261770de9956a0"
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4e77204a6954e99bcc98c16e842699b"
            }
          },
          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c192463ab92c4a99a0727aa7f0fa3c08"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef93f38b665f4ee7955259e87e162d62"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_trainable_parameters(model):\n",
        "  \"\"\"\n",
        "  Prints the number of trainable parameters in the model.\n",
        "  \"\"\"\n",
        "  trainable_params = 0\n",
        "  all_param = 0\n",
        "  for _, param in model.named_parameters():\n",
        "    all_param += param.numel()\n",
        "    if param.requires_grad:\n",
        "      trainable_params += param.numel()\n",
        "  print(\n",
        "      f\"trainable params: {trainable_params} || all params: {all_param} || trainables%: {100 * trainable_params / all_param}\"\n",
        "  )"
      ],
      "metadata": {
        "id": "9TvFxPnjhR-A"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "uLRXzMVsmRYH",
        "outputId": "c790bc46-729b-4146-b292-5dac9e28e547"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'model' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-9afb3ef815d7>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient_checkpointing_enable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprepare_model_for_kbit_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"query_key_value\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ],
      "metadata": {
        "id": "9DOIzBDbmTJC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"\n",
        "<human>: I am depressed, what to do?\n",
        "<companion>:\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "D9-FMuZNmwIf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_config = model.generation_config\n",
        "generation_config.max_new_tokens = 200\n",
        "generation_config.temperature = 0.7\n",
        "generation_config.top_p = 0.7\n",
        "generation_config.num_return_sequences = 1\n",
        "generation_config.pad_token_id = tokenizer.eos_token_id\n",
        "generation_config.eos_token_id = tokenizer.eos_token_id"
      ],
      "metadata": {
        "id": "yMbbVlqDmzj5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "device = \"cuda:0\"\n",
        "\n",
        "encoding = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
        "with torch.inference_mode():\n",
        "  outputs = model.generate(\n",
        "      input_ids = encoding.input_ids,\n",
        "      attention_mask = encoding.attention_mask,\n",
        "      generation_config = generation_config\n",
        "  )\n",
        "\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "id": "IbneEcoSm1Y4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('Amod/mental_health_counseling_conversations', split='train')"
      ],
      "metadata": {
        "id": "cMxPqxRPm3M6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "hl1VYft-m8Mx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[0]"
      ],
      "metadata": {
        "id": "j8NMnaq9nDpN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(data_point):\n",
        "  return f\"\"\"\n",
        "<human>: {data_point[\"Context\"]}\n",
        "<companion>: {data_point[\"Response\"]}\n",
        "\"\"\".strip()\n",
        "\n",
        "def generate_and_tokenize_prompt(data_point):\n",
        "  full_prompt = generate_prompt(data_point)\n",
        "  tokenized_full_prompt = tokenizer(full_prompt, padding=True, truncation=True)\n",
        "  return tokenized_full_prompt"
      ],
      "metadata": {
        "id": "tv9k36KonGWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset.shuffle().map(generate_and_tokenize_prompt)"
      ],
      "metadata": {
        "id": "GNn9wmqjnIHQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = transformers.TrainingArguments(\n",
        "      per_device_train_batch_size=1,\n",
        "      gradient_accumulation_steps=4,\n",
        "      num_train_epochs=1,\n",
        "      learning_rate=2e-4,\n",
        "      fp16=True,\n",
        "      save_total_limit=3,\n",
        "      logging_steps=1,\n",
        "      output_dir=\"experiments\",\n",
        "      optim=\"paged_adamw_8bit\",\n",
        "      lr_scheduler_type=\"cosine\",\n",
        "      warmup_ratio=0.05,\n",
        ")\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data,\n",
        "    args=training_args,\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "JsCyZeH8nea1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lakCy0TdoOQb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}