BAdam_Qwen1_5_1.8B_Dutch_Chat_SFT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3lPgvN_pk-m"
      },
      "source": [
        "## Install and Import Modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'BAdam'...\n",
            "remote: Enumerating objects: 494, done.\u001b[K\n",
            "remote: Counting objects: 100% (120/120), done.\u001b[K\n",
            "remote: Compressing objects: 100% (90/90), done.\u001b[K\n",
            "remote: Total 494 (delta 53), reused 71 (delta 25), pack-reused 374\u001b[K\n",
            "Receiving objects: 100% (494/494), 106.36 MiB | 19.59 MiB/s, done.\n",
            "Resolving deltas: 100% (113/113), done.\n",
            "Updating files: 100% (304/304), done.\n"
          ]
        }
      ],
      "source": [
        "# Clone BAdam Repository\n",
        "!git clone https://github.com/Ledzy/BAdam.git\n",
        "!cp -R ./BAdam/src/badam/*.py ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLCEjnOZiVP7",
        "outputId": "155e1356-624f-4ce8-b863-7a354e72c2d8"
      },
      "outputs": [],
      "source": [
        "# Install Modules\n",
        "#!pip install -q accelerate==0.27.2\n",
        "#!pip install -q bitsandbytes==0.43.0\n",
        "#!pip install -q datasets==2.17.1\n",
        "#!pip install -q peft==0.9.0\n",
        "#!pip install -q transformers==4.38.2\n",
        "#!pip install -q trl==0.8.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "7R3VLs6PHZQX"
      },
      "outputs": [],
      "source": [
        "# Import Modules\n",
        "from datasets import load_dataset\n",
        "from huggingface_hub import notebook_login\n",
        "from transformers import (AutoTokenizer,\n",
        "                          AutoModelForCausalLM,\n",
        "                          DataCollatorForLanguageModeling,\n",
        "                          TrainingArguments)\n",
        "import torch\n",
        "from trl import SFTTrainer\n",
        "\n",
        "# Import BAdam\n",
        "from block_optim import BlockOptimizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2ELwb27pc2p"
      },
      "source": [
        "## Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Uhk1Rkk_gNCK"
      },
      "outputs": [],
      "source": [
        "# Set Name Constants\n",
        "model_name = 'Qwen/Qwen1.5-1.8B-Chat'\n",
        "hf_model_name = 'BAdam-Qwen1.5-1.8B-Dutch-Chat-Sft'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NquXCba0pW8w"
      },
      "source": [
        "## HuggingFace Login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166,
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          ]
        },
        "id": "HHeHs7goh3yP",
        "outputId": "59c0e12b-e3df-43bf-a87d-4f0824631f98"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "94ebcbdae27849a9a684c801faa65d74",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# HuggingFace Hub Login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xED9rD-2h3yQ"
      },
      "source": [
        "## Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373,
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        },
        "id": "ewyYBs9-h3yR",
        "outputId": "3a1867cb-572f-4c97-ef44-f470faf21413"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Qwen2TokenizerFast(name_or_path='Qwen/Qwen1.5-1.8B-Chat', vocab_size=151643, model_max_length=32768, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>']}, clean_up_tokenization_spaces=False),  added_tokens_decoder={\n",
            "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "# Create Tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Max Length\n",
        "MAX_LEN = 1024\n",
        "\n",
        "# Tokenizer Summary\n",
        "print(tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fo0E4IRFh3yR"
      },
      "source": [
        "## Create Model based on Qwen1.5_1.8B_Chat Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "BtQrABkuh3yR",
        "outputId": "0b99f895-febf-42db-83c9-b215b25434eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Qwen2ForCausalLM(\n",
            "  (model): Qwen2Model(\n",
            "    (embed_tokens): Embedding(151936, 2048)\n",
            "    (layers): ModuleList(\n",
            "      (0-23): 24 x Qwen2DecoderLayer(\n",
            "        (self_attn): Qwen2SdpaAttention(\n",
            "          (q_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (k_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (v_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
            "          (rotary_emb): Qwen2RotaryEmbedding()\n",
            "        )\n",
            "        (mlp): Qwen2MLP(\n",
            "          (gate_proj): Linear(in_features=2048, out_features=5504, bias=False)\n",
            "          (up_proj): Linear(in_features=2048, out_features=5504, bias=False)\n",
            "          (down_proj): Linear(in_features=5504, out_features=2048, bias=False)\n",
            "          (act_fn): SiLU()\n",
            "        )\n",
            "        (input_layernorm): Qwen2RMSNorm()\n",
            "        (post_attention_layernorm): Qwen2RMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): Qwen2RMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=2048, out_features=151936, bias=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "# Create Model\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map = \"auto\",\n",
        "                                             torch_dtype = torch.bfloat16)\n",
        "\n",
        "# Set cache to False\n",
        "model.config.use_cache = False\n",
        "\n",
        "# Show Model Summary\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gMIhn1Nh3yS"
      },
      "source": [
        "## Modify Default Chat template to Dutch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "kOn0_PLCh3yT",
        "outputId": "da04e346-950b-4fec-8f61-f4230b573f30"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\\nJe bent een behulpzame AI assistent<|im_end|>\\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\\n'}}{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}\""
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Modify Default Chattemplate with Dutch System Message\n",
        "tokenizer.chat_template = \"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\\nJe bent een behulpzame AI assistent<|im_end|>\\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\\n'}}{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}\"\n",
        "\n",
        "# Summary Chat Template\n",
        "tokenizer.chat_template"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z0QxjiyLb32-"
      },
      "source": [
        "## Test Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXliS4gCEi1V",
        "outputId": "6edb82a2-c648-45dd-86b6-ba8fda72a89c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset({\n",
            "    features: ['messages'],\n",
            "    num_rows: 3072\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# Load Test Dataset\n",
        "test_data = load_dataset('BramVanroy/ultrachat_200k_dutch', split = 'test_sft')\n",
        "test_data = test_data.select(range(3072)).remove_columns([\"prompt\", \"prompt_id\"]) # Only part of the test data to limit the time spent on validation.\n",
        "\n",
        "# Summary\n",
        "print(test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nxcv_pBVbWrp"
      },
      "source": [
        "## Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-tGAWKKZSc_",
        "outputId": "75eb1413-ca4e-4a41-db6a-d83315f2f7aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset({\n",
            "    features: ['messages'],\n",
            "    num_rows: 192598\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "# Load Train Dataset\n",
        "train_data = load_dataset('BramVanroy/ultrachat_200k_dutch', split = 'train_sft').remove_columns([\"prompt\", \"prompt_id\"])\n",
        "\n",
        "# Summary\n",
        "print(train_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39p5KtlibNRy"
      },
      "source": [
        "## Chat Template Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAS45lwFP_KI",
        "outputId": "52ba3fe4-7cf7-4d5e-8a9b-69b11da41a66"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<|im_start|>system\n",
            "Je bent een behulpzame AI assistent<|im_end|>\n",
            "<|im_start|>user\n",
            "Kan je mij vertellen welke versie van mijn website thema ik gebruik? Er staat iets over sectie-gebaseerde thema's zoals Responsive 6.0+, Retina 4.0+, Parallax 3.0+ Turbo 2.0+, Mobilia 5.0+. En ook, hoe zit het met die functie die toelaat het tweede productafbeelding te tonen wanneer ik erover hover? Is dat voor alle secties of enkel de genoemden?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Om te bepalen welke themaversie u gebruikt, moet u meestal in de instellingen van uw websiteachtergrond kijken. Deze kunnen vaak gevonden worden in de thema-editor of het dashboard waar informatie staat over het huidige thema. Wat betreft de functie om de secundaire afbeelding van een product te tonen bij het erover hoveren; dit is een instellingsmogelijkheid die vaak in de secties 'Collecties pagina's' en 'Uitgelichte Collecties' zich bevindt. Deze functie is doorgaans beperkt tot de secties die in het materiaal genoemd worden, tenzij het thema die mogelijkheid ook expliciet in andere secties aanbiedt.<|im_end|>\n",
            "<|im_start|>user\n",
            "Aha, begrijp ik het goed dat ik zelf even moet spitten in de thema-instellingen voor de exacte versie? Zou je me kunnen wijzen waar ik dat normaal vind?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Ja, dat klopt. U kunt meestal de themaversie vinden door in te loggen op uw beheerdersdashboard en naar het gedeelte 'Thema's' te navigeren. Daarna selecteert u 'Aanpassen' bij het thema dat u momenteel gebruikt. Veel thema's hebben informatie over de versie onderaan in de voettekst van de thema-editor of soms onder 'Instellingen' of 'Informatie' binnen de thema-editor.<|im_end|>\n",
            "<|im_start|>user\n",
            "Oké, tof. En nu ben ik ook benieuwd naar iets anders. Stel dat ik die hover-functie heb aanstaan, heeft dit dan invloed op hoe snel mijn pagina laadt?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Het toevoegen van de functie om bij hover een secundaire afbeelding te tonen kan enige invloed hebben op de laadtijd van een pagina, voornamelijk als de afbeeldingsbestanden groot zijn. Echter, dit soort functies zijn normaal gesproken geoptimaliseerd om de impact op de laadtijd te minimaliseren. Het is wel belangrijk om ervoor te zorgen dat de gebruikte afbeeldingen geoptimaliseerd zijn voor het web, om de beste laadtijden te garanderen.<|im_end|>\n",
            "<|im_start|>user\n",
            "Interessant, ik zal ervoor zorgen dat mijn afbeeldingen geoptimaliseerd zijn dan. Kan ik ook nog iets toevoegen zodat de klanten de afbeeldingen in een lichtbox kunnen zien als ze erop klikken?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Veel thema's bieden de mogelijkheid om een lichtbox-functie te activeren. Dit laat klanten toe om op de afbeelding te klikken en deze in een groter formaat te bekijken zonder de pagina te verlaten. Of uw thema deze functie heeft, kunt u controleren in de thema-instellingen onder de productpagina opties. Zoek naar 'lichtbox' of iets vergelijkbaars in de omschrijving van de instelmogelijkheden.<|im_end|>\n",
            "<|im_start|>user\n",
            "Perfect, dan ga ik dat ook nog instellen. Nu iets heel anders, hoe zou ik het beste social media kunnen inzetten voor meer verkeer naar mijn website?<|im_end|>\n",
            "<|im_start|>assistant\n",
            "Om social media effectief te gebruiken voor meer websiteverkeer, kunt u overwegen om regelmatig content te delen die waarde biedt aan uw volgers, zoals promoties, informatieve blogposts, of interactieve content zoals wedstrijden. Het vormen van relaties met influencers of andere merken voor samenwerkingen kan ook helpen. Vergeet niet om directe links naar uw website of specifieke productpagina's te plaatsen om het bezoekers gemakkelijk te maken uw website te vinden.\n"
          ]
        }
      ],
      "source": [
        "# Show sample\n",
        "item_data = train_data[0]\n",
        "chat_template_string = tokenizer.apply_chat_template(item_data[\"messages\"], tokenize = False)\n",
        "print(chat_template_string)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poEgM4M-h3yU"
      },
      "source": [
        "## Create BAdam Block Optimzer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create BAdam Optimizer\n",
        "optimizer = BlockOptimizer(base_optimizer = torch.optim.AdamW(model.parameters(), lr = 2.0e-5, weight_decay = 0.001),\n",
        "                           named_parameters_list = list(model.named_parameters()),\n",
        "                           block_prefix_list = None,\n",
        "                           switch_block_every = 16,\n",
        "                           switch_mode = \"descending\",\n",
        "                           verbose = 0)\n",
        "\n",
        "# Set Learning Rate Scheduler\n",
        "scheduler = torch.optim.lr_scheduler.LinearLR(optimizer,\n",
        "                                              start_factor = 0.01,\n",
        "                                              end_factor = 1.0,\n",
        "                                              total_iters = 512,\n",
        "                                              verbose = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523,
          "referenced_widgets": [
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        "outputId": "b3aeabb3-db9f-4b5a-95b9-fded8ee17b0f"
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      "outputs": [
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            "/home/robin/environments/LLM/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:341: UserWarning: You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached.\n",
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            "{'eval_loss': 1.6840535402297974, 'eval_runtime': 340.3833, 'eval_samples_per_second': 9.025, 'eval_steps_per_second': 4.513, 'epoch': 0.65}\n",
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          "text": [
            "{'eval_loss': 1.6769460439682007, 'eval_runtime': 341.7537, 'eval_samples_per_second': 8.989, 'eval_steps_per_second': 4.494, 'epoch': 0.68}\n",
            "{'loss': 1.7236, 'grad_norm': 0.78125, 'learning_rate': 1.999999999999996e-05, 'epoch': 0.69}\n",
            "{'loss': 1.7382, 'grad_norm': 0.416015625, 'learning_rate': 1.999999999999996e-05, 'epoch': 0.7}\n"
          ]
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'eval_loss': 1.6739139556884766, 'eval_runtime': 341.7161, 'eval_samples_per_second': 8.99, 'eval_steps_per_second': 4.495, 'epoch': 0.7}\n",
            "{'train_runtime': 45170.7961, 'train_samples_per_second': 2.902, 'train_steps_per_second': 0.181, 'train_loss': 1.9023650288581848, 'epoch': 0.7}\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=8192, training_loss=1.9023650288581848, metrics={'train_runtime': 45170.7961, 'train_samples_per_second': 2.902, 'train_steps_per_second': 0.181, 'train_loss': 1.9023650288581848, 'epoch': 0.7})"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Set Steps\n",
        "eval_steps = 256\n",
        "save_steps = 1024\n",
        "logging_steps = 128\n",
        "\n",
        "# Set TrainingArguments\n",
        "training_args = TrainingArguments(num_train_epochs = 1,\n",
        "                                  max_steps = 8192,\n",
        "                                  evaluation_strategy = \"steps\",\n",
        "                                  logging_steps = logging_steps,\n",
        "                                  save_strategy = 'steps',\n",
        "                                  eval_steps = eval_steps,\n",
        "                                  save_steps = save_steps,\n",
        "                                  save_total_limit = 1,\n",
        "                                  per_device_train_batch_size = 2,\n",
        "                                  per_device_eval_batch_size = 2,\n",
        "                                  gradient_accumulation_steps = 8,\n",
        "                                  gradient_checkpointing = True,\n",
        "                                  gradient_checkpointing_kwargs = {'use_reentrant': False},\n",
        "                                  bf16 = True,\n",
        "                                  output_dir = hf_model_name,\n",
        "                                  hub_model_id = hf_model_name,\n",
        "                                  push_to_hub = True,\n",
        "                                  hub_private_repo = True,\n",
        "                                  report_to = 'tensorboard')\n",
        "\n",
        "# Config SFTTrainer\n",
        "trainer = SFTTrainer(model,\n",
        "                     train_dataset = train_data,\n",
        "                     eval_dataset = test_data,\n",
        "                     tokenizer = tokenizer,\n",
        "                     packing = True,\n",
        "                     eval_packing = False,\n",
        "                     max_seq_length = MAX_LEN,\n",
        "                     optimizers = (optimizer, scheduler),\n",
        "                     data_collator = DataCollatorForLanguageModeling(tokenizer, mlm = False),\n",
        "                     args = training_args)\n",
        "\n",
        "# Perform Training\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjTQt-aIh3yU"
      },
      "source": [
        "## Push to Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
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            "9ac6f0efb590471ca8fd20317d735484",
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