phi-2_QLoRA.ipynb

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    "from transformers import AutoModelForCausalLM, BitsAndBytesConfig, set_seed, AutoTokenizer, TrainingArguments, Trainer\n",
    "from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model\n",
    "from datasets import load_dataset\n",
    "from functools import partial\n",
    "import os\n",
    "import torch\n",
    "\n",
    "set_seed(42)\n",
    "\n",
    "bs=8     # batch size for training\n",
    "bs_eval=16    # batch size for evaluation\n",
    "ga_steps=2  # gradient accumulation steps\n",
    "lr=0.00002  # learning rate\n",
    "epochs=20\n",
    "\n",
    "# Load model\n",
    "modelpath = \"microsoft/phi-2\"\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    modelpath,    \n",
    "    device_map=\"auto\",\n",
    "    quantization_config=BitsAndBytesConfig(\n",
    "        load_in_4bit=True,\n",
    "        bnb_4bit_compute_dtype=torch.bfloat16,\n",
    "        bnb_4bit_quant_type=\"nf4\",\n",
    "    ),\n",
    "    torch_dtype = torch.bfloat16,\n",
    "    use_cache = False,\n",
    "    # FA2 does not work yet\n",
    "    # attn_implementation=\"sdpa\",          \n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(modelpath, use_fast=False)    # fast tokenizer sometimes ignores the added tokens\n",
    "tokenizer.add_tokens([\"<|im_start|>\", \"<PAD>\"])\n",
    "tokenizer.pad_token = \"<PAD>\"\n",
    "tokenizer.add_special_tokens(dict(eos_token=\"<|im_end|>\"))\n",
    "model.config.eos_token_id = tokenizer.eos_token_id\n",
    "\n",
    "model = prepare_model_for_kbit_training(model) \n",
    "\n",
    "lora_config = LoraConfig(\n",
    "    r=32, \n",
    "    lora_alpha=32, \n",
    "    target_modules = [ \"q_proj\", \"k_proj\", \"v_proj\", \"dense\" ],\n",
    "    modules_to_save = [\"lm_head\", \"embed_tokens\"],\n",
    "    lora_dropout=0.1, \n",
    "    bias=\"none\", \n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "model = get_peft_model(model, lora_config)\n",
    "\n",
    "dataset = load_dataset(\"g-ronimo/riddles_evolved\")\n",
    "dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
    "\n",
    "# Format (chatML) and tokenize dataset\n",
    "templates=[\n",
    "    \"<|im_start|>assistant\\n{msg}<|im_end|>\",\n",
    "    \"<|im_start|>user\\n{msg}<|im_end|>\"\n",
    "]\n",
    "IGNORE_INDEX=-100\n",
    "\n",
    "def tokenize(input, max_length):\n",
    "    input_ids, attention_mask, labels = [], [], []\n",
    "\n",
    "    for i,msg in enumerate(input[\"messages\"]):\n",
    "        isHuman = i%2==0\n",
    "        msg_chatml=templates[isHuman].format(msg=msg)\n",
    "        msg_tokenized=tokenizer(msg_chatml, truncation=False, add_special_tokens=False)\n",
    "\n",
    "        input_ids+=msg_tokenized[\"input_ids\"]\n",
    "        attention_mask+=msg_tokenized[\"attention_mask\"]\n",
    "        labels+=[IGNORE_INDEX]*len(msg_tokenized[\"input_ids\"]) if isHuman else msg_tokenized[\"input_ids\"]\n",
    "\n",
    "    return {\n",
    "        \"input_ids\": input_ids[:max_length],\n",
    "        \"attention_mask\": attention_mask[:max_length],\n",
    "        \"labels\": labels[:max_length],\n",
    "    }\n",
    "\n",
    "dataset_tokenized = dataset.map(\n",
    "    partial(tokenize, max_length=1024), # max sample length 1024 tokens, enough for this dataset\n",
    "    batched=False, \n",
    "    num_proc=os.cpu_count(),    # multithreaded\n",
    "    remove_columns=dataset[\"train\"].column_names  # don't need this anymore, we have tokens from here on\n",
    ")\n",
    "\n",
    "# collate function - to transform list of dictionaries [ {input_ids: [123, ..]}, {.. ] to single batch dictionary { input_ids: [..], labels: [..], attention_mask: [..] }\n",
    "def collate(elements):\n",
    "    tokens=[e[\"input_ids\"] for e in elements]\n",
    "    tokens_maxlen=max([len(t) for t in tokens])\n",
    "\n",
    "    for i,sample in enumerate(elements):\n",
    "        input_ids=sample[\"input_ids\"]\n",
    "        labels=sample[\"labels\"]\n",
    "        attention_mask=sample[\"attention_mask\"]\n",
    "\n",
    "        pad_len=tokens_maxlen-len(input_ids)\n",
    "\n",
    "        input_ids.extend( pad_len * [tokenizer.pad_token_id] )   \n",
    "        labels.extend( pad_len * [IGNORE_INDEX] )    \n",
    "        attention_mask.extend( pad_len * [0] ) \n",
    "\n",
    "    batch={\n",
    "        \"input_ids\": torch.tensor( [e[\"input_ids\"] for e in elements] ),\n",
    "        \"labels\": torch.tensor( [e[\"labels\"] for e in elements] ),\n",
    "        \"attention_mask\": torch.tensor( [e[\"attention_mask\"] for e in elements] ),\n",
    "    }\n",
    "\n",
    "    return batch\n",
    "\n",
    "steps_per_epoch=len(dataset_tokenized[\"train\"])//(bs*ga_steps)\n",
    "\n",
    "args = TrainingArguments(\n",
    "    output_dir=\"out\",\n",
    "    per_device_train_batch_size=bs,\n",
    "    per_device_eval_batch_size=bs_eval,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    logging_steps=1,\n",
    "    eval_steps=steps_per_epoch,  \n",
    "    save_steps=steps_per_epoch,     \n",
    "    gradient_accumulation_steps=ga_steps,\n",
    "    num_train_epochs=epochs,\n",
    "    lr_scheduler_type=\"constant\",\n",
    "    optim=\"paged_adamw_32bit\",      # val_loss will go nan with paged_adamw_8bit\n",
    "    learning_rate=lr,\n",
    "    group_by_length = False,\n",
    "    bf16 = True,        \n",
    "    gradient_checkpointing = True,\n",
    "    ddp_find_unused_parameters = False,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    args=args,\n",
    "    data_collator=collate,\n",
    "    train_dataset=dataset_tokenized[\"train\"],\n",
    "    eval_dataset=dataset_tokenized[\"test\"],\n",
    ")\n",
    "\n",
    "# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):\n",
    "trainer.train()"
   ]
  }
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