LoRA_Fine_Tuning.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":140897,"status":"ok","timestamp":1709227687661,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"aUKkBzH5YxY-"},"outputs":[],"source":["# Copies the model weights from google drive into colab\n","%cp -r \"/content/drive/MyDrive/hf_llama_2_7b\" \"/content/\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58833,"status":"ok","timestamp":1709227763443,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"Xx1hxI8oWoIk","outputId":"1b70431c-4c15-452a-c4d6-e2882c2df98f"},"outputs":[],"source":["# Installs required python packages\n","!pip install torch accelerate bitsandbytes datasets transformers peft trl scipy astrapy llama_cpp_python"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":14154,"status":"ok","timestamp":1709227788234,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"-IeuI4kZXWTQ"},"outputs":[],"source":["# Import required python packages\n","from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, set_seed, Trainer, TrainingArguments, BitsAndBytesConfig, DataCollatorForLanguageModeling\n","import bitsandbytes as bnb\n","from torch import cuda, bfloat16\n","import transformers\n","import torch\n","import torch.nn as nn\n","from google.colab import userdata\n","from astrapy.db import AstraDBCollection, AstraDB\n","from datasets import Dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":238,"status":"ok","timestamp":1709227814580,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"cE2TpWy7Xhau"},"outputs":[],"source":["# Uses GPU for processing if a CUDA device is available\n","device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":219,"status":"ok","timestamp":1709227819044,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"if9qs0wZX_lq"},"outputs":[],"source":["# Set quantization settings\n","model_id = \"hf_llama_2_7b\"\n","\n","bnb_config = transformers.BitsAndBytesConfig(\n","    load_in_4bit=True,\n","    bnb_4bit_quant_type='nf4',\n","    bnb_4bit_use_double_quant=True,\n","    bnb_4bit_compute_dtype=bfloat16\n",")\n","model_config = AutoConfig.from_pretrained(model_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138,"referenced_widgets":["bacdc6ea57c84c978fae461814b88027","e9e3196d89b745bd9249f8cbfbcc2cff","a49b52d44f644942b1f1e3cabadb331b","1f259ff846244199962ae624b0c06e6d","9c6a11839b8844418cd7b834f6edede2","dcaa47b779224ebfafb32eafeffbe4ac","ea5a7fd80aa14629b7e32dd9e19f970a","2bd49741d8b6485292e292bbdb302073","d6ec197e104e44019d711e432723049b","340fe08472ee466496afe96bbcc4fd4f","0cc01befb2784a8885ea4e803967697a"]},"executionInfo":{"elapsed":32775,"status":"ok","timestamp":1709227857780,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"te3HESJHYJWQ","outputId":"5f3b6f67-a26b-4e63-ee1c-633cffaf566c"},"outputs":[],"source":["# Loads model weights\n","model = transformers.AutoModelForCausalLM.from_pretrained(\n","    model_id,\n","    trust_remote_code=True,\n","    config=model_config,\n","    quantization_config=bnb_config,\n","    device_map='auto'\n",")\n","\n","model.eval()\n","print(f\"Model loaded on {device}\")\n","\n","mem = model.get_memory_footprint()\n","print(\"Memory footprint: {} \".format(mem))"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":633,"status":"ok","timestamp":1709227880444,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"KCm9BZMyYoQ1"},"outputs":[],"source":["# Loads model's associated tokenizer\n","tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, use_fast=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21681,"status":"ok","timestamp":1709227914087,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"W_F04GMuapBg","outputId":"67b2d086-1f13-4877-c7c5-75afb8b49cb2"},"outputs":[],"source":["# Pulls all our instruction data from Astra\n","token = userdata.get('astra_token')\n","endpoint = userdata.get('astra_endpoint')\n","\n","collection_name = \"instructions\"\n","astra_db = AstraDB(token=token, api_endpoint=endpoint)\n","collection = AstraDBCollection(collection_name=collection_name, astra_db=astra_db)\n","\n","nextPageState = \"\"\n","raw_dataset = []\n","expected_columns = ['instruction', 'input', 'output']\n","\n","def check_expected_columns(raw_instruction):\n","  if all(column in raw_instruction for column in expected_columns):\n","    return True\n","  else:\n","    return False\n","\n","while nextPageState != None:\n","  if nextPageState == \"\":\n","    data = collection.find()\n","    nextPageState = data['data']['nextPageState']\n","    raw_instructions = [instruction for instruction in data['data']['documents'] if check_expected_columns(instruction)]\n","    raw_dataset.extend(raw_instructions)\n","  else:\n","    data = collection.find(options={\"pageState\":nextPageState}, sort = None)\n","    nextPageState = data['data']['nextPageState']\n","    raw_instructions = [instruction for instruction in data['data']['documents'] if check_expected_columns(instruction)]\n","    raw_dataset.extend(raw_instructions)\n","\n","print(raw_dataset[0])\n","print(len(raw_dataset))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":533,"status":"ok","timestamp":1709227922314,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"wL8-is3ThP_v","outputId":"00e3eddd-e687-416e-d6ab-b56f902eac55"},"outputs":[],"source":["# Turns separated instruction dicts from Astra into a dataset of combined instructions\n","def create_prompt(record):\n","\n","  start = \"Read the Instruction below and provide an answer.\"\n","  question = f\"### INSTRUCTION:\\n{record['instruction']}\\n\\n\"\n","  response = f\"### Context:\\n{record['input']}\\n\"\n","  answer = f\"### Response:\\n {record['output']}\\n\\n\"\n","  end = \"### End\"\n","\n","  parts = [part for part in [start, question, response, answer, end] if part]\n","\n","  formatted_prompt = \"\\n\\n\".join(parts)\n","  formatted_prompt = formatted_prompt.replace('\\\\n', '\\n')\n","\n","  record[\"text\"] = formatted_prompt\n","\n","  return record\n","\n","p = create_prompt(raw_dataset[0])\n","print(p[\"text\"])\n","combined_dataset = list(map(create_prompt, raw_dataset))\n","print(combined_dataset[1]['text'])\n","\n","dataset = Dataset.from_list([{\"text\" : record[\"text\"]} for record in combined_dataset])\n","print(dataset[2])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":292,"status":"ok","timestamp":1709227948666,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"3nI-iCP2ikTQ","outputId":"802098f4-8806-4f48-aad3-9ed9f31d1c88"},"outputs":[],"source":["#max length of the model\n","def get_max_length(model):\n","    conf = model.config\n","    max_length = None\n","    for length_setting in [\"n_positions\", \"max_position_embeddings\", \"seq_length\"]:\n","        max_length = getattr(model.config, length_setting, None)\n","        if max_length:\n","            print(f\"Found max lenth: {max_length}\")\n","            break\n","    if not max_length:\n","        max_length = 1024\n","        print(f\"Using default max length: {max_length}\")\n","    return max_length\n","mx = get_max_length(model)\n","mx"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["df6056460a524ecca4189c1d40c2ba38","fcc83d7ab8dd48199857af5714f45180","96a3a98105fa40949009af7b12209541","fdc26fe736ea45c4a26a34fe4b195670","b19352e59e1846bf9223ea5388ee26e2","c9e1433f461f4bac8228d8b36a98d82b","5d1783e73c9e40d2b3f2e10eb942e94d","7103b39f6c234c4fa0c3ab233370d675","d5cda39e45444fe9999c872ddedc5309","f6b763fd17074ed48c340ca210e81fdd","85999b4fb41542738dca753cc876db21","cb2cd8a796484973b998148354594770","00fc0700f65a47ebac052c615b5536f1","17c1318aadc04a3d822f9e167e4aeba3","cdc1f7fa9f5d412da780e9aebea1157d","c6924fea89d24b208e8b306d78a72715","9ed04d1dd6054762a10af7dcb973c029","d67a50415fb743bcb9e9c4001694dc3a","f62f5baa22084bda827926d45a44d4e1","365f2825781545a3a594788de1900e4d","37cd4fb07abc4d2796705f3b58538326","6c48c2c226054a0d8d288ae29c6b4b34"]},"executionInfo":{"elapsed":2737,"status":"ok","timestamp":1709227956544,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"GKP03beIj6qz","outputId":"4a71cab6-eb34-49d7-ca0f-07070f9596db"},"outputs":[],"source":["#tokenize dataset\n","dataset = dataset.map(lambda samples: tokenizer(samples['text']), batched=True)\n","dataset = dataset.filter(lambda sample: len(sample[\"input_ids\"]) < mx)\n","seed = 402\n","set_seed(seed)\n","dataset = dataset.shuffle(seed=seed)"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":550,"status":"ok","timestamp":1709227961988,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"-NwPrnbBkqTE"},"outputs":[],"source":["for param in model.parameters():\n","  param.requires_grad = False  # freeze the model - train adapters later\n","  if param.ndim == 1:\n","    # cast the small parameters (e.g. layernorm) to fp32 for stability\n","    param.data = param.data.to(torch.float32)\n","\n","model.gradient_checkpointing_enable()  # reduce number of stored activations\n","model.enable_input_require_grads()\n","\n","class CastOutputToFloat(nn.Sequential):\n","  def forward(self, x): return super().forward(x).to(torch.float32)\n","model.lm_head = CastOutputToFloat(model.lm_head)"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":341,"status":"ok","timestamp":1709227997846,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"SdADZR5xkyHU"},"outputs":[],"source":["def find_all_linear_names(model):\n","    cls = bnb.nn.Linear4bit #if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)\n","    lora_module_names = set()\n","    for name, module in model.named_modules():\n","        if isinstance(module, cls):\n","            names = name.split('.')\n","            lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n","\n","    if 'lm_head' in lora_module_names:  # needed for 16-bit\n","        lora_module_names.remove('lm_head')\n","    return list(lora_module_names)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":512,"status":"ok","timestamp":1709228020926,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"lSBZVlABk2qw","outputId":"2f9c11be-112d-40d8-aa00-c0c547f508e3"},"outputs":[],"source":["modules = find_all_linear_names(model)\n","print(modules)\n","\n","#['v_proj', 'up_proj', 'down_proj', 'k_proj', 'o_proj', 'q_proj', 'gate_proj']"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":1031,"status":"ok","timestamp":1709228028938,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"o42P4fckk5h6"},"outputs":[],"source":["from peft import LoraConfig, get_peft_model\n","\n","config = LoraConfig(\n","    r=16,  #attention heads\n","    lora_alpha=64,  #alpha scaling\n","    target_modules=modules,  #gonna train all\n","    lora_dropout=0.1,  # dropout probability for layers\n","    bias=\"none\",\n","    task_type=\"CAUSAL_LM\", #for Decoder models like GPT Seq2Seq for Encoder-Decoder models like T5\n",")\n","##Get the PEFT Model using the downloaded model and the loRA config\n","model = get_peft_model(model, config)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":541,"status":"ok","timestamp":1709228063777,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"qBGdsGhHk9Ou","outputId":"144b30d8-5e40-49f2-f192-25f87b8999fd"},"outputs":[],"source":["# Print Trainable parameters\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} || trainable%: {100 * trainable_params / all_param}\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":3661335,"status":"ok","timestamp":1709240044219,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"twPPdEbmlK8H","outputId":"f166cf65-dc6b-4ba5-df4e-01a73e5917d7"},"outputs":[],"source":["tokenizer.pad_token = tokenizer.eos_token\n","trainer = Trainer(\n","    model=model,\n","    train_dataset=dataset,\n","    args=TrainingArguments(\n","        per_device_train_batch_size=1,\n","        gradient_accumulation_steps=4,\n","        warmup_steps=10,\n","        max_steps=4000, #20,\n","        learning_rate=2e-4,\n","        fp16=True,\n","        logging_steps=1,\n","        output_dir=\"outputs\",\n","        optim=\"paged_adamw_8bit\",\n","    ),\n","    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",")\n","\n","model.config.use_cache = False  # re-enable for inference to speed up predictions for similar inputs\n","\n","trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":10068,"status":"ok","timestamp":1709240057755,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"4_TstLuRjCiT"},"outputs":[],"source":["%cp -r \"/content/outputs\" \"/content/drive/MyDrive\""]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":985,"status":"ok","timestamp":1709240045487,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"fJEJ6zIVx4s6"},"outputs":[],"source":["import datetime\n","now = str(datetime.datetime.now()).replace(\" \",\"_\")\n","model.save_pretrained(save_directory=\"outputs/lora_adapter_\"+now)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["b2846ec1d7a84376bb09a5caa8a4a221","3311c2462e304150a6257baf30b13abb","736e86cc4d3c41a198752e6d2903cdab","a31fc1594a1e445885075364dfa355bf","da52afdce3734a0ba83695fd0c814366","fcba154be46e4a2f8db0c83ced586cd2","5c21c32d89484893a7cebcce30ea75af","e475f80e3d474e18b5e775e3635315db","8277b5d9ae7a405bb1d727d41f61ac92","5102b0969912460eb27d557294c36a5d","13da0a9f40214886ab3f13eba0240de0"]},"executionInfo":{"elapsed":14016,"status":"ok","timestamp":1709226628087,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"l4MGiO2PyqCy","outputId":"e45aba65-9ac6-434e-cde6-800831492bfc"},"outputs":[],"source":["import torch\n","from peft import PeftModel, PeftConfig\n","from transformers import AutoModelForCausalLM, AutoTokenizer\n","\n","#The model loaded here is a separate object from the model loaded above. Unless your runtime has ~ 16GB of VRAM, you may need to restart to purge the old model from memory\n","\n","peft_model_id = \"/content/outputs/lora_adapter2024-02-29_16:56:54.106818\"\n","config = PeftConfig.from_pretrained(peft_model_id)\n","model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\n","tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n","\n","# Load the Lora model\n","trained_model = PeftModel.from_pretrained(model, peft_model_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211},"executionInfo":{"elapsed":195,"status":"error","timestamp":1709226991499,"user":{"displayName":"Obioma Nwabueze Anomnachi","userId":"18272483841001646532"},"user_tz":300},"id":"5SGc2m7J1_7E","outputId":"fc67f4c5-71ed-44d6-fbc9-ef657240c2b2"},"outputs":[],"source":["tst = \"\"\"Read the Instruction below and provide an answer.\n","\n","### INSTRUCTION:\n","In this task, you are given an input list A. You need to find all the elements of the list that are numbers and calculate their sum.\n","\n","['i', '33', 'h', '849', '77']\n","\n","\n","\n","### RESPONSE:\"\"\"\n","batch = tokenizer(tst, return_tensors='pt')\n","with torch.cuda.amp.autocast():\n","  output_tokens = trained_model.generate(**batch, max_new_tokens=90)\n","\n","print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"]}],"metadata":{"accelerator":"GPU","colab":{"authorship_tag":"ABX9TyPqMQDGhzRBlFndDSiyEa0q","gpuType":"V100","machine_shape":"hm","mount_file_id":"1--YrtJgiE5rqFqVwvKXIKWd5qqpHLMhx","provenance":[]},"kernelspec":{"display_name":"Python 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