{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
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    "id": "dbsnrDKKVarI",
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    "outputId": "5808189b-e624-42d7-856f-bc3b0201fab9",
    "ExecuteTime": {
     "end_time": "2024-04-16T22:59:34.679348Z",
     "start_time": "2024-04-16T22:59:19.314163Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in c:\\users\\saad.naeem\\appdata\\local\\anaconda3\\envs\\minerva-prototype\\lib\\site-packages (2.18.0)\n",
      "Requirement already satisfied: wandb in c:\\users\\saad.naeem\\appdata\\local\\anaconda3\\envs\\minerva-prototype\\lib\\site-packages (0.16.6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: WARNING Calling wandb.login() after wandb.init() has no effect.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Token has not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.Requirement already satisfied: filelock in c:\\users\\saad.naeem\\appdata\\local\\anaconda3\\envs\\minerva-prototype\\lib\\site-packages (from datasets) (3.9.0)\n",
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      "Token is valid (permission: write).\n",
      "Your token has been saved to C:\\Users\\saad.naeem\\.cache\\huggingface\\token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "# @title # 🌊 AutoBitnet\n",
    "\n",
    "# @markdown ---\n",
    "\n",
    "# @markdown ### ✨ Model Parameters\n",
    "\n",
    "MODEL_CONFIG = \"NousResearch/Nous-Hermes-llama-2-7b\" # @param {type:\"string\"}\n",
    "HEADS = 6 # @param {type: \"number\"}\n",
    "DIMENSIONS = 768 # @param {type: \"number\"}\n",
    "LAYERS = 6 # @param {type: \"number\"}\n",
    "INTERMEDIATE_SIZE= 1024 # @param {type: \"number\"}\n",
    "CONTEXT_LENGTH = 256 # @param {type: \"number\"}\n",
    "HUGGINGFACE_ID = \"saadnaeem\" # @param {type:\"string\"}\n",
    "NEW_MODEL = \"Llama2-70M-Cosmopedia-100k-Pretrained\" # @param {type:\"string\"}\n",
    "WANDB_TOKEN=''\n",
    "HF_TOKEN=''\n",
    "\n",
    "# @markdown ---\n",
    "\n",
    "# @markdown ### 💥 Training Parameters\n",
    "\n",
    "DATASET = \"abideen/Cosmopedia-100k-pretrain\" # @param {type:\"string\"}\n",
    "BATCH_SIZE = 32 # @param {type:\"number\"}\n",
    "LEARNING_RATE = 1.5e-4 # @param {type:\"number\"}\n",
    "EPOCHS = 1 # @param {type:\"number\"}\n",
    "!pip install datasets wandb accelerate\n",
    "from torch import nn\n",
    "from transformers.models.llama.modeling_llama import *\n",
    "from transformers import (AutoTokenizer, AutoConfig, LlamaForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments, AutoModel)\n",
    "from datasets import load_dataset\n",
    "from huggingface_hub import login\n",
    "import wandb\n",
    "# wandb.ai/saadnaeem-dev\n",
    "\n",
    "from huggingface_hub import create_repo, HfApi\n",
    "\n",
    "def activation_quant(x):\n",
    "    scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5)\n",
    "    y = (x * scale).round().clamp_(-128, 127) / scale\n",
    "    return y\n",
    "def weight_quant(w):\n",
    "    scale = 1.0 / w.abs().mean().clamp_(min=1e-5)\n",
    "    u = (w * scale).round().clamp_(-1, 1) / scale\n",
    "    return u\n",
    "\n",
    "class BitLinear(nn.Linear):\n",
    "    def forward(self, x):\n",
    "        w = self.weight # a weight tensor with shape [d, k]\n",
    "        x = x.to(w.device)\n",
    "        RMSNorm = LlamaRMSNorm(x.shape[-1]).to(w.device)\n",
    "        x_norm = RMSNorm(x)\n",
    "        # A trick for implementing Straight−Through−Estimator (STE) using detach()\n",
    "        x_quant = x_norm + (activation_quant(x_norm) - x_norm).detach()\n",
    "        w_quant = w + (weight_quant(w) - w).detach()\n",
    "        y = F.linear(x_quant, w_quant)\n",
    "        return y\n",
    "\n",
    "def convert_to_bitnet(model, copy_weights):\n",
    "    for name, module in model.named_modules():\n",
    "        # Replace linear layers with BitNet\n",
    "        if isinstance(module, LlamaSdpaAttention) or isinstance(module, LlamaMLP):\n",
    "            for child_name, child_module in module.named_children():\n",
    "                if isinstance(child_module, nn.Linear):\n",
    "                    bitlinear = BitLinear(child_module.in_features, child_module.out_features, child_module.bias is not None).to(device=\"cuda:0\")\n",
    "                    if copy_weights:\n",
    "                        bitlinear.weight = child_module.weight\n",
    "                        if child_module.bias is not None:\n",
    "                            bitlinear.bias = child_module.bias\n",
    "                    setattr(module, child_name, bitlinear)\n",
    "        # Remove redundant input_layernorms\n",
    "        elif isinstance(module, LlamaDecoderLayer):\n",
    "            for child_name, child_module in module.named_children():\n",
    "                if isinstance(child_module, LlamaRMSNorm) and child_name == \"input_layernorm\":\n",
    "                    setattr(module, child_name, nn.Identity().to(device=\"cuda:0\"))\n",
    "\n",
    "\n",
    "wandb.login(key=WANDB_TOKEN)\n",
    "login(token=HF_TOKEN)\n",
    "data = load_dataset(DATASET)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_CONFIG)\n",
    "\n",
    "def tokenize(element):\n",
    "    outputs = tokenizer(\n",
    "        element[\"text\"],\n",
    "        truncation=False,\n",
    "        max_length=CONTEXT_LENGTH,\n",
    "        return_overflowing_tokens=True,\n",
    "        return_length=True,\n",
    "    )\n",
    "    # Combine all tokens\n",
    "    combined = []\n",
    "    for tokenized_doc in outputs['input_ids']:\n",
    "        combined += tokenized_doc + [tokenizer.eos_token_id]\n",
    "    # Chunk\n",
    "    input_batch = []\n",
    "    for i in range(0, len(combined) - CONTEXT_LENGTH, CONTEXT_LENGTH):\n",
    "        input_batch.append(combined[i:i+CONTEXT_LENGTH])\n",
    "    return {\"input_ids\": input_batch}\n",
    "\n",
    "\n",
    "\n",
    "tokenized_data = data.map(\n",
    "    tokenize, batched=True, remove_columns=data[\"train\"].column_names,\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:55:40.522736Z",
     "start_time": "2024-04-16T22:55:39.803487Z"
    }
   },
   "execution_count": 80
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "DatasetDict({\n    train: Dataset({\n        features: ['input_ids'],\n        num_rows: 476702\n    })\n})"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:55:41.976375Z",
     "start_time": "2024-04-16T22:55:41.955375Z"
    }
   },
   "execution_count": 81
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from datasets import DatasetDict\n",
    "\n",
    "# Set the number of rows\n",
    "tokenized_data['train'].set_format(type='pandas')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:55:43.097378Z",
     "start_time": "2024-04-16T22:55:43.076377Z"
    }
   },
   "execution_count": 82
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "sampled_dataset = tokenized_data['train'].select(range(1000))\n",
    "sampled_dataset_dict = DatasetDict({\n",
    "    'train': sampled_dataset\n",
    "})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:55:44.559805Z",
     "start_time": "2024-04-16T22:55:44.537823Z"
    }
   },
   "execution_count": 83
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "                                           input_ids\n0  [1, 2266, 338, 385, 6597, 515, 263, 24499, 299...",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>input_ids</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[1, 2266, 338, 385, 6597, 515, 263, 24499, 299...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_dataset_dict['train'][0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:56:05.152275Z",
     "start_time": "2024-04-16T22:56:05.132254Z"
    }
   },
   "execution_count": 85
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "DatasetDict({\n    train: Dataset({\n        features: ['input_ids'],\n        num_rows: 1000\n    })\n})"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_data = sampled_dataset_dict\n",
    "tokenized_data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:56:06.004257Z",
     "start_time": "2024-04-16T22:56:05.990254Z"
    }
   },
   "execution_count": 86
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on 256_000 tokens\n",
      "Model size: 77.5M parameters\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saad.naeem\\AppData\\Local\\anaconda3\\envs\\minerva-prototype\\lib\\site-packages\\accelerate\\accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
      "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "total_tokens = tokenized_data['train'].num_rows * CONTEXT_LENGTH\n",
    "print(f\"Training on {total_tokens:_} tokens\")\n",
    "\n",
    "config = AutoConfig.from_pretrained(\n",
    "    MODEL_CONFIG,\n",
    "    vocab_size=len(tokenizer),\n",
    "    n_ctx=CONTEXT_LENGTH,\n",
    "    bos_token_id=tokenizer.bos_token_id,\n",
    "    eos_token_id=tokenizer.eos_token_id,\n",
    ")\n",
    "\n",
    "config.hidden_size = DIMENSIONS\n",
    "config.max_position_embeddings = DIMENSIONS\n",
    "config.num_attention_heads = HEADS\n",
    "config.num_hidden_layers = LAYERS\n",
    "config.num_key_value_heads = HEADS\n",
    "config.intermediate_size = INTERMEDIATE_SIZE\n",
    "\n",
    "### Create the llama model with our custom config. Convert it to bitnet.\n",
    "model = LlamaForCausalLM(config)\n",
    "convert_to_bitnet(model, copy_weights=False)\n",
    "model_size = sum(t.numel() for t in model.parameters())\n",
    "print(f\"Model size: {model_size/1000**2:.1f}M parameters\")\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
    "\n",
    "output_path = \"./Llama2-70M-Cosmopedia-100k-Pretrained\"\n",
    "args = TrainingArguments(\n",
    "    output_dir=output_path,\n",
    "    per_device_train_batch_size=BATCH_SIZE,\n",
    "    logging_steps=100,\n",
    "    gradient_accumulation_steps=2,\n",
    "    num_train_epochs=EPOCHS,\n",
    "    weight_decay=0.01,\n",
    "    warmup_steps=0.1,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    learning_rate=LEARNING_RATE,\n",
    "    # max_steps=5000,\n",
    "    save_steps=0.25,\n",
    "    fp16=True,\n",
    "    report_to=\"wandb\"\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    args=args,\n",
    "    data_collator=data_collator,\n",
    "    train_dataset=tokenized_data[\"train\"],\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:56:11.670768Z",
     "start_time": "2024-04-16T22:56:09.804760Z"
    }
   },
   "execution_count": 87
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<IPython.core.display.HTML object>",
      "text/html": "\n    <div>\n      \n      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      [16/16 01:46, Epoch 1/1]\n    </div>\n    <table border=\"1\" class=\"dataframe\">\n  <thead>\n <tr style=\"text-align: left;\">\n      <th>Step</th>\n      <th>Training Loss</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table><p>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "TrainOutput(global_step=16, training_loss=9.391032218933105, metrics={'train_runtime': 110.6973, 'train_samples_per_second': 9.034, 'train_steps_per_second': 0.145, 'total_flos': 81244717056000.0, 'train_loss': 9.391032218933105, 'epoch': 1.0})"
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T22:58:19.694121Z",
     "start_time": "2024-04-16T22:56:28.225808Z"
    }
   },
   "execution_count": 88
  },
  {
   "cell_type": "code",
   "source": [
    "trainer.save_model(f\"{output_path}\")\n",
    "folder = f\"{output_path}\"\n",
    "api = HfApi()\n",
    "create_repo(\n",
    "    repo_id = f\"{HUGGINGFACE_ID}/{NEW_MODEL}\",\n",
    "    repo_type=\"model\",\n",
    "    exist_ok=True,\n",
    "    token=HF_TOKEN,\n",
    ")\n",
    "\n",
    "api.upload_folder(\n",
    "    folder_path=folder,\n",
    "    repo_type=\"model\",\n",
    "    repo_id=f\"{HUGGINGFACE_ID}/{NEW_MODEL}\",\n",
    "    token=HF_TOKEN,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 180,
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    "outputId": "bfa63618-ae11-4415-a695-0349dfecf4ad",
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2024-04-16T22:59:36.439911Z"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "Upload 9 LFS files:   0%|          | 0/9 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from transformers.models.llama.modeling_llama import *\n",
    "# Load a pretrained BitNet model\n",
    "model = \"saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model)\n",
    "model = AutoModelForCausalLM.from_pretrained(model)\n",
    "\n",
    "\n",
    "def activation_quant(x):\n",
    "    scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5)\n",
    "    y = (x * scale).round().clamp_(-128, 127)\n",
    "    y = y / scale\n",
    "    return y\n",
    "def weight_quant(w):\n",
    "    scale = 1.0 / w.abs().mean().clamp_(min=1e-5)\n",
    "    u = (w * scale).round().clamp_(-1, 1)\n",
    "    u = u / scale\n",
    "    return u\n",
    "\n",
    "class BitLinear(nn.Linear):\n",
    "    def forward(self, x):\n",
    "        w = self.weight # a weight tensor with shape [d, k]\n",
    "        x = x.to(w.device)\n",
    "        RMSNorm = LlamaRMSNorm(x.shape[-1]).to(w.device)\n",
    "        x_norm = RMSNorm(x)\n",
    "        # A trick for implementing Straight−Through−Estimator (STE) using detach()\n",
    "        x_quant = x_norm + (activation_quant(x_norm) - x_norm).detach()\n",
    "        w_quant = w + (weight_quant(w) - w).detach()\n",
    "        y = F.linear(x_quant, w_quant)\n",
    "        return y\n",
    "\n",
    "def convert_to_bitnet(model, copy_weights):\n",
    "    for name, module in model.named_modules():\n",
    "        # Replace linear layers with BitNet\n",
    "        if isinstance(module, LlamaSdpaAttention) or isinstance(module, LlamaMLP):\n",
    "            for child_name, child_module in module.named_children():\n",
    "                if isinstance(child_module, nn.Linear):\n",
    "                    bitlinear = BitLinear(child_module.in_features, child_module.out_features, child_module.bias is not None).to(device=\"cuda:0\")\n",
    "                    if copy_weights:\n",
    "                        bitlinear.weight = child_module.weight\n",
    "                        if child_module.bias is not None:\n",
    "                            bitlinear.bias = child_module.bias\n",
    "                    setattr(module, child_name, bitlinear)\n",
    "        # Remove redundant input_layernorms\n",
    "        elif isinstance(module, LlamaDecoderLayer):\n",
    "            for child_name, child_module in module.named_children():\n",
    "                if isinstance(child_module, LlamaRMSNorm) and child_name == \"input_layernorm\":\n",
    "                    setattr(module, child_name, nn.Identity().to(device=\"cuda:0\"))\n",
    "\n",
    "\n",
    "convert_to_bitnet(model, copy_weights=True)\n",
    "model.to(device=\"cuda:0\")\n",
    "\n",
    "prompt = \"What is Machine Learning?\"\n",
    "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
    "generate_ids = model.generate(inputs.input_ids, max_length=50)\n",
    "tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 107
    },
    "id": "wtB3ZOBB_8E6",
    "outputId": "39e3df74-5ade-4ff1-e997-28042e178dde"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of LlamaForCausalLM were not initialized from the model checkpoint at abideen/Bitnet-Llama-70M and are newly initialized: ['model.layers.0.input_layernorm.weight', 'model.layers.1.input_layernorm.weight', 'model.layers.2.input_layernorm.weight', 'model.layers.3.input_layernorm.weight', 'model.layers.4.input_layernorm.weight', 'model.layers.5.input_layernorm.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": "execute_result",
     "data": {
      "text/plain": [
       "'What is Machine Learning?\\n\\nIn today’s digital age, machine learning has become a crucial aspect of our lives. With the increasing popularity of machine learning, machine learning has become a powerful tool for learning and learning. With the'"
      ],
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      }
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = \"Write a short poem\"\n",
    "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
    "generate_ids = model.generate(inputs.input_ids, max_length=50)\n",
    "tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
   ],
   "metadata": {
    "id": "nQya_hPJEa2M",
    "ExecuteTime": {
     "end_time": "2024-04-16T19:10:03.797580Z",
     "start_time": "2024-04-16T19:10:01.243070Z"
    }
   },
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "'Write a short poem about a \"The Witcher\" by the author of the book \"The Witcher of the Book of the Book of the Book of the Book of the Book of the Book of the Book of the Book of the'"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LlamaForCausalLM(\n",
      "  (model): LlamaModel(\n",
      "    (embed_tokens): Embedding(32001, 768, padding_idx=0)\n",
      "    (layers): ModuleList(\n",
      "      (0-5): 6 x LlamaDecoderLayer(\n",
      "        (self_attn): LlamaAttention(\n",
      "          (q_proj): Linear(in_features=768, out_features=768, bias=False)\n",
      "          (k_proj): Linear(in_features=768, out_features=768, bias=False)\n",
      "          (v_proj): Linear(in_features=768, out_features=768, bias=False)\n",
      "          (o_proj): Linear(in_features=768, out_features=768, bias=False)\n",
      "          (rotary_emb): LlamaRotaryEmbedding()\n",
      "        )\n",
      "        (mlp): LlamaMLP(\n",
      "          (gate_proj): BitLinear(in_features=768, out_features=1024, bias=False)\n",
      "          (up_proj): BitLinear(in_features=768, out_features=1024, bias=False)\n",
      "          (down_proj): BitLinear(in_features=1024, out_features=768, bias=False)\n",
      "          (act_fn): SiLU()\n",
      "        )\n",
      "        (input_layernorm): Identity()\n",
      "        (post_attention_layernorm): LlamaRMSNorm()\n",
      "      )\n",
      "    )\n",
      "    (norm): LlamaRMSNorm()\n",
      "  )\n",
      "  (lm_head): Linear(in_features=768, out_features=32001, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T19:10:30.619003Z",
     "start_time": "2024-04-16T19:10:30.598004Z"
    }
   },
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model size: 77.5M parameters\n"
     ]
    }
   ],
   "source": [
    "# print number of parameters\n",
    "model_size = sum(t.numel() for t in model.parameters())\n",
    "print(f\"Model size: {model_size/1000**2:.1f}M parameters\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T19:11:02.552839Z",
     "start_time": "2024-04-16T19:11:02.539841Z"
    }
   },
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Save the model to disk\n",
    "import torch\n",
    "\n",
    "# Assuming that `model` is your model\n",
    "torch.save(model.state_dict(), 'Llama2-70M-Cosmopedia-100k-Pretrain.pth')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T19:31:47.960780Z",
     "start_time": "2024-04-16T19:31:47.016780Z"
    }
   },
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'pwd' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T19:32:17.975619Z",
     "start_time": "2024-04-16T19:32:17.707622Z"
    }
   },
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "transformers.trainer.Trainer"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Trainer"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T19:49:52.040088Z",
     "start_time": "2024-04-16T19:49:52.027087Z"
    }
   },
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "(('Llama2-70M-Cosmopedia-100k-Pretrained\\\\tokenizer_config.json',\n  'Llama2-70M-Cosmopedia-100k-Pretrained\\\\special_tokens_map.json',\n  'Llama2-70M-Cosmopedia-100k-Pretrained\\\\tokenizer.json'),)"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.save_model(\"Llama2-70M-Cosmopedia-100k-Pretrained\")\n",
    "tokenizer.save_pretrained(\"Llama2-70M-Cosmopedia-100k-Pretrained\"),"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T19:52:28.336234Z",
     "start_time": "2024-04-16T19:52:14.732448Z"
    }
   },
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Testing model from Huggingface Hub"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "\"what is machine learning' di'''.icaiaian, isé isé\\ninestinieninamentWriteinieningienAienAest\\ninamenterninest\\ninest\\ninest\\ninament\""
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = \"what is machine learning\"\n",
    "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
    "generate_ids = model.generate(inputs.input_ids, max_length=50)\n",
    "tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T20:35:45.132443Z",
     "start_time": "2024-04-16T20:35:43.770680Z"
    }
   },
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "folder = r\"C:\\Users\\saad.naeem\\PycharmProjects\\NLP-Projects-NHV-1-Bit-LLM\\Llama2-70M-Cosmopedia-100k-Pretrain\"\n",
    "api = HfApi()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:15:39.350857Z",
     "start_time": "2024-04-16T21:15:39.327854Z"
    }
   },
   "execution_count": 30
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "RepoUrl('https://huggingface.co./saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained', endpoint='https://huggingface.co.', repo_type='model', repo_id='saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained')"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.create_repo(\n",
    "    repo_id = f\"saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained\",\n",
    "    repo_type=\"model\",\n",
    "    exist_ok=True\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:15:51.034996Z",
     "start_time": "2024-04-16T21:15:48.858598Z"
    }
   },
   "execution_count": 31
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "optimizer.pt:   0%|          | 0.00/620M [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "217543fd247f45d6b07f99164c0373cb"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "rng_state.pth:   0%|          | 0.00/14.6k [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "de6fece2a84e4380b79bb35dce9a1e70"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "scheduler.pt:   0%|          | 0.00/627 [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ceb905ce4caa4228a8cdab6dd310f180"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "training_args.bin:   0%|          | 0.00/4.47k [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "79735360ed464a54807709a1eed7a4a5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "model.safetensors:   0%|          | 0.00/310M [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2b2fb879e008444ea14a456864d92fd9"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Upload 5 LFS files:   0%|          | 0/5 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0dd001dbcf6d49578c9f1064394ceac5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "CommitInfo(commit_url='https://huggingface.co./saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained/commit/b3b67c7a7dcb199a07244be3a493cd649cf3731f', commit_message='Upload folder using huggingface_hub', commit_description='', oid='b3b67c7a7dcb199a07244be3a493cd649cf3731f', pr_url=None, pr_revision=None, pr_num=None)"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.upload_folder(\n",
    "    folder_path=folder,\n",
    "    repo_type=\"model\",\n",
    "    repo_id=f\"saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained\",\n",
    "    token=\"\",\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:22:10.115935Z",
     "start_time": "2024-04-16T21:16:46.527703Z"
    }
   },
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from transformers import (AutoTokenizer, AutoModelForCausalLM)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:27:41.041614Z",
     "start_time": "2024-04-16T21:27:41.019615Z"
    }
   },
   "execution_count": 35
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Load a pretrained BitNet model\n",
    "model = \"saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained\""
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:28:06.283415Z",
     "start_time": "2024-04-16T21:28:06.258415Z"
    }
   },
   "execution_count": 36
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of LlamaForCausalLM were not initialized from the model checkpoint at saadnaeem/Llama2-70M-Cosmopedia-100k-Pretrained and are newly initialized: ['model.layers.0.input_layernorm.weight', 'model.layers.1.input_layernorm.weight', 'model.layers.2.input_layernorm.weight', 'model.layers.3.input_layernorm.weight', 'model.layers.4.input_layernorm.weight', 'model.layers.5.input_layernorm.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": "generation_config.json:   0%|          | 0.00/154 [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "54059190e6eb4dc595bcb0b5590c948d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model)\n",
    "model = AutoModelForCausalLM.from_pretrained(model)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:28:10.449481Z",
     "start_time": "2024-04-16T21:28:07.098728Z"
    }
   },
   "execution_count": 37
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "convert_to_bitnet(model, copy_weights=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:29:02.212560Z",
     "start_time": "2024-04-16T21:29:02.054560Z"
    }
   },
   "execution_count": 38
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "LlamaForCausalLM(\n  (model): LlamaModel(\n    (embed_tokens): Embedding(32001, 768, padding_idx=0)\n    (layers): ModuleList(\n      (0-5): 6 x LlamaDecoderLayer(\n        (self_attn): LlamaAttention(\n          (q_proj): Linear(in_features=768, out_features=768, bias=False)\n          (k_proj): Linear(in_features=768, out_features=768, bias=False)\n          (v_proj): Linear(in_features=768, out_features=768, bias=False)\n          (o_proj): Linear(in_features=768, out_features=768, bias=False)\n          (rotary_emb): LlamaRotaryEmbedding()\n        )\n        (mlp): LlamaMLP(\n          (gate_proj): BitLinear(in_features=768, out_features=1024, bias=False)\n          (up_proj): BitLinear(in_features=768, out_features=1024, bias=False)\n          (down_proj): BitLinear(in_features=1024, out_features=768, bias=False)\n          (act_fn): SiLU()\n        )\n        (input_layernorm): Identity()\n        (post_attention_layernorm): LlamaRMSNorm()\n      )\n    )\n    (norm): LlamaRMSNorm()\n  )\n  (lm_head): Linear(in_features=768, out_features=32001, bias=False)\n)"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device=\"cuda:0\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:29:14.806083Z",
     "start_time": "2024-04-16T21:29:14.521081Z"
    }
   },
   "execution_count": 39
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "\"What is Machine Learning?\\n\\nI've been working on a project for a project that has been working on a project that has been working on a project. I am not sure what I am doing. I am not sure what I do\""
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = \"What is Machine Learning?\"\n",
    "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
    "generate_ids = model.generate(inputs.input_ids, max_length=50)\n",
    "tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-16T21:29:29.324485Z",
     "start_time": "2024-04-16T21:29:27.586135Z"
    }
   },
   "execution_count": 40
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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