saved/v1/shakespearegpt_v1.ipynb

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   "source": [
    "# **Shakespeare GPT**\n",
    "---\n",
    "\n",
    "from Andrej Karpathy's Tutorial: [ Let's build GPT: from scratch, in code, spelled out. ](https://www.youtube.com/watch?v=kCc8FmEb1nY)\n",
    "\n",
    "- As per the tutorial, the tokenizer used was character-level, which I made a notebook for separately here: [ShakespeareGPT [character-level]](https://www.kaggle.com/code/shreydan/shakespearegpt-character-level). The results were pretty incredible with character-level but it invented new words (just like Shakespeare :P).\n",
    "- In this notebook, the model is slightly larger, uses a Byte-Pair Encoding tokenizer via HuggingFace trained on the shakespeare corpus from scratch, following their [tutorial on how to build GPT2 tokenizer from scratch](https://huggingface.co/course/chapter6/8?fw=pt#building-a-bpe-tokenizer-from-scratch), hopefully the words in this are more coherent.\n",
    "- the purpose ofc here is to learn language-modelling, shakespeareGPT spits out text that looks like Shakespeare plays but they mostly dont make sense. We need to later fine tune the language model to output meaningful generated texts.\n",
    "- I still need to figure out how to use multiple GPUs to utilize the 2x T4s but this runs well on P100.\n",
    "- The full code I wrote following the incredible tutorial is here: [github/shreydan/shakespeareGPT](https://github.com/shreydan/shakespeareGPT)\n",
    "- This is a neat little playground to try it yourself and play with the hyperparameters!"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install torchinfo -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "599f1d92",
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f32bc656ef0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from dataclasses import dataclass\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tokenizers import Tokenizer\n",
    "from tokenizers.models import BPE\n",
    "import tokenizers.pre_tokenizers as pre_tokenizers\n",
    "import tokenizers.processors as processors\n",
    "import tokenizers.decoders as decoders\n",
    "from tokenizers.trainers import BpeTrainer\n",
    "\n",
    "from torchinfo import summary\n",
    "\n",
    "torch.manual_seed(1357)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00636102",
   "metadata": {
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     "exception": false,
     "start_time": "2023-02-15T07:25:43.089468",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c01e2423",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('/kaggle/input/shakespeare-full-text/shakespeare.txt','r',encoding='utf-8') as f:\n",
    "    data = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1a23e79",
   "metadata": {
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'First Citizen:\\nBefore we proceed any further, hear me speak.\\n\\nAll:\\nSpeak, speak.\\n\\nFirst Citizen:\\nYou'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770bae59",
   "metadata": {
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     "duration": 0.007586,
     "end_time": "2023-02-15T07:25:43.207449",
     "exception": false,
     "start_time": "2023-02-15T07:25:43.199863",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# BPE Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3581781",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2023-02-15T07:25:43.215288",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tokenizer_path = Path('./tokenizer/')\n",
    "tokenizer_path.mkdir(exist_ok=True)\n",
    "\n",
    "tokenizer = Tokenizer(BPE())\n",
    "\n",
    "tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)\n",
    "tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)\n",
    "tokenizer.decoder = decoders.ByteLevel()\n",
    "trainer = BpeTrainer(special_tokens=['<|endoftext|>'], min_frequency=2)\n",
    "\n",
    "tokenizer.train(['/kaggle/input/shakespeare-full-text/shakespeare.txt'],trainer)\n",
    "tokenizer.save(str(tokenizer_path / 'shakespeare.json'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd8c020b",
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     "start_time": "2023-02-15T07:25:44.671984",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Config"
   ]
  },
  {
   "cell_type": "code",
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   "id": "ac5c22bb",
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     "exception": false,
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "('cuda', 12516)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@dataclass\n",
    "class Config:\n",
    "    \n",
    "    block_size = 256 # context-length\n",
    "    batch_size = 64 # mini-batch size\n",
    "    \n",
    "    vocab_size = tokenizer.get_vocab_size()\n",
    "    \n",
    "    train_size = 0.8 \n",
    "    \n",
    "    n_embed = 384\n",
    "    n_heads = 12\n",
    "    head_size = n_embed // n_heads # computes to 384/12=32\n",
    "    \n",
    "    n_layers = 4\n",
    "    \n",
    "    train_iters = 5000 # no. of batches to train on\n",
    "    val_iters = 500 # no. of batches to validate on every eval_intervals\n",
    "    \n",
    "    eval_interval = 500 # validate after every eval_interval iterations while training\n",
    "    \n",
    "    lr = 6e-4 # also used by the GPT 3 Small, quite a lot more stable than 1e-3\n",
    "    \n",
    "    attn_dropout = 0.1\n",
    "    block_dropout = 0.1\n",
    "    \n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    \n",
    "Config.device, Config.vocab_size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7fce4c3",
   "metadata": {
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     "duration": 0.007814,
     "end_time": "2023-02-15T07:25:44.792989",
     "exception": false,
     "start_time": "2023-02-15T07:25:44.785175",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "049871de",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ShakespeareDataset:\n",
    "    def __init__(self, Config, is_test=False) -> None:\n",
    "        self.file_path = Path('/kaggle/input/shakespeare-full-text/shakespeare.txt')\n",
    "        self.tokenizer_path = Path('/kaggle/working/tokenizer/shakespeare.json')\n",
    "        with open(self.file_path,'r',encoding='utf-8') as f:\n",
    "            self.data = f.read()\n",
    "        self.tokenizer = Tokenizer.from_file(str(self.tokenizer_path))\n",
    "        \n",
    "        self.full_data = torch.tensor(self.tokenizer.encode(self.data).ids, dtype=torch.long)\n",
    "        \n",
    "        self.is_test = is_test\n",
    "        if self.is_test:\n",
    "            self.data = self.full_data[int(Config.train_size*len(self.full_data)):]\n",
    "        else:\n",
    "            self.data = self.full_data[:int(Config.train_size*len(self.full_data))]\n",
    "\n",
    "        self.block_size = Config.block_size\n",
    "        self.batch_size = Config.batch_size\n",
    "\n",
    "    def __len__(self) -> int:\n",
    "        return len(self.data)\n",
    "\n",
    "    def get_block_size(self) -> int:\n",
    "        return self.block_size\n",
    "\n",
    "    def get_vocab_size(self) -> int:\n",
    "        return self.tokenizer.get_vocab_size()\n",
    "\n",
    "    def __next__(self):\n",
    "        ix = torch.randint(len(self.data) - self.block_size, (self.batch_size,))\n",
    "        x = torch.stack([self.data[i:i+self.block_size] for i in ix])\n",
    "        y = torch.stack([self.data[i+1:i+self.block_size+1] for i in ix])\n",
    "        return x,y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c19d36",
   "metadata": {
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     "duration": 0.008116,
     "end_time": "2023-02-15T07:25:44.838232",
     "exception": false,
     "start_time": "2023-02-15T07:25:44.830116",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "# **GPT Model**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5f84d6",
   "metadata": {
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     "duration": 0.007728,
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     "exception": false,
     "start_time": "2023-02-15T07:25:44.846083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Single Attention Head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7805ee34",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class AttentionHead(nn.Module):\n",
    "    def __init__(self, Config):\n",
    "        super().__init__()\n",
    "\n",
    "        self.block_size = Config.block_size\n",
    "        self.n_embed = Config.n_embed\n",
    "        self.head_size = Config.head_size\n",
    "        \n",
    "        self.key = nn.Linear(self.n_embed, self.head_size, bias=False)\n",
    "        self.query = nn.Linear(self.n_embed, self.head_size, bias=False)\n",
    "        \n",
    "        self.value = nn.Linear(self.n_embed, self.head_size, bias=False)\n",
    "\n",
    "        self.register_buffer(\n",
    "            'tril',\n",
    "            torch.tril(torch.ones(self.block_size,self.block_size))\n",
    "        )\n",
    "        \n",
    "        self.dropout = nn.Dropout(Config.attn_dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        B,T,C = x.shape\n",
    "\n",
    "        k = self.key(x)\n",
    "        q = self.query(x)\n",
    "\n",
    "        wei = q@k.transpose(-2,-1) * (C ** 0.5)\n",
    "        wei = wei.masked_fill(self.tril[:T,:T]==0,float('-inf'))\n",
    "        wei = F.softmax(wei, dim=-1)\n",
    "        wei = self.dropout(wei)\n",
    "        \n",
    "        v = self.value(x)\n",
    "        out = wei @ v\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20020ea",
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     "start_time": "2023-02-15T07:25:44.889245",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## MultiHead Attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5596a6ff",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [],
   "source": [
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, Config):\n",
    "        super().__init__()\n",
    "        self.n_heads = Config.n_heads\n",
    "        self.head_size = Config.head_size\n",
    "        \n",
    "        self.heads = nn.ModuleList([AttentionHead(Config) for _ in range(self.n_heads)])\n",
    "        \n",
    "        self.projection = nn.Linear(Config.n_embed, Config.n_embed)\n",
    "        \n",
    "        self.dropout = nn.Dropout(Config.attn_dropout)\n",
    "    \n",
    "    def forward(self,x):\n",
    "        x = torch.cat([h(x) for h in self.heads],dim=-1)\n",
    "        x = self.projection(x)\n",
    "        x = self.dropout(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e09576c",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feed Forward Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b9a005d",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class FeedForward(nn.Module):\n",
    "    def __init__(self, Config):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(Config.n_embed,Config.n_embed * 4),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(Config.n_embed * 4, Config.n_embed), # projection\n",
    "            nn.Dropout(Config.block_dropout)\n",
    "        )\n",
    "    def forward(self,x):\n",
    "        return self.net(x)"
   ]
  },
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   "id": "7dc25bd4",
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     "duration": 0.007768,
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     "exception": false,
     "start_time": "2023-02-15T07:25:44.972256",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Transformer Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c6eef077",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:44.996924Z",
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     "duration": 0.016219,
     "end_time": "2023-02-15T07:25:45.004131",
     "exception": false,
     "start_time": "2023-02-15T07:25:44.987912",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, Config):\n",
    "        super().__init__()\n",
    "        self.attn = MultiHeadAttention(Config)\n",
    "        self.ff = FeedForward(Config)\n",
    "        self.ln1 = nn.LayerNorm(Config.n_embed)\n",
    "        self.ln2 = nn.LayerNorm(Config.n_embed)\n",
    "\n",
    "    def forward(self,x):\n",
    "        x = x + self.attn(self.ln1(x))\n",
    "        x = x + self.ff(self.ln2(x))\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2c3dac",
   "metadata": {
    "papermill": {
     "duration": 0.008096,
     "end_time": "2023-02-15T07:25:45.020092",
     "exception": false,
     "start_time": "2023-02-15T07:25:45.011996",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## GPT Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a3be6b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:45.037266Z",
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     "shell.execute_reply": "2023-02-15T07:25:45.045364Z"
    },
    "papermill": {
     "duration": 0.019988,
     "end_time": "2023-02-15T07:25:45.048161",
     "exception": false,
     "start_time": "2023-02-15T07:25:45.028173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ShakespeareGPT(nn.Module):\n",
    "    def __init__(self,Config):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.n_embed = Config.n_embed\n",
    "        self.block_size = Config.block_size\n",
    "        \n",
    "        self.token_embedding_table = nn.Embedding(Config.vocab_size,self.n_embed)\n",
    "        self.pos_embedding_table = nn.Embedding(self.block_size, self.n_embed)\n",
    "        \n",
    "        self.blocks = nn.Sequential(\n",
    "            *[TransformerBlock(Config)]*Config.n_layers,\n",
    "            nn.LayerNorm(self.n_embed)\n",
    "        )\n",
    "\n",
    "        self.lm_head = nn.Linear(self.n_embed,Config.vocab_size)\n",
    "        \n",
    "    def forward(self,idx):\n",
    "        \n",
    "        B,T = idx.shape\n",
    "        \n",
    "        token_embs = self.token_embedding_table(idx)\n",
    "        pos_embs = self.pos_embedding_table(torch.arange(T,device=Config.device))\n",
    "        \n",
    "        \n",
    "        x = token_embs + pos_embs\n",
    "        x = self.blocks(x)\n",
    "        logits = self.lm_head(x)\n",
    "        \n",
    "        return logits\n",
    "\n",
    "        \n",
    "    def generate(self,idx,total):\n",
    "        for _ in range(total):\n",
    "            idx_cond = idx[:, -self.block_size:]\n",
    "            logits= self(idx_cond)\n",
    "            logits = logits[:, -1, :]\n",
    "            probs = F.softmax(logits, dim=-1)\n",
    "            idx_next = torch.multinomial(probs, num_samples=1)\n",
    "            idx = torch.cat((idx, idx_next), dim=1)\n",
    "        return idx\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "170d194f",
   "metadata": {
    "papermill": {
     "duration": 0.007992,
     "end_time": "2023-02-15T07:25:45.064029",
     "exception": false,
     "start_time": "2023-02-15T07:25:45.056037",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "28ffb2ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:45.081987Z",
     "iopub.status.busy": "2023-02-15T07:25:45.081169Z",
     "iopub.status.idle": "2023-02-15T07:25:47.102450Z",
     "shell.execute_reply": "2023-02-15T07:25:47.101435Z"
    },
    "papermill": {
     "duration": 2.032665,
     "end_time": "2023-02-15T07:25:47.104912",
     "exception": false,
     "start_time": "2023-02-15T07:25:45.072247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_ds = ShakespeareDataset(Config)\n",
    "val_ds = ShakespeareDataset(Config,is_test=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3924dcf4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:47.124137Z",
     "iopub.status.busy": "2023-02-15T07:25:47.123271Z",
     "iopub.status.idle": "2023-02-15T07:25:50.239600Z",
     "shell.execute_reply": "2023-02-15T07:25:50.238584Z"
    },
    "papermill": {
     "duration": 3.128789,
     "end_time": "2023-02-15T07:25:50.242013",
     "exception": false,
     "start_time": "2023-02-15T07:25:47.113224",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lm = ShakespeareGPT(Config)\n",
    "lm = lm.to(device=Config.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68c2e19f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:50.261791Z",
     "iopub.status.busy": "2023-02-15T07:25:50.259979Z",
     "iopub.status.idle": "2023-02-15T07:25:51.351497Z",
     "shell.execute_reply": "2023-02-15T07:25:51.350561Z"
    },
    "papermill": {
     "duration": 1.102928,
     "end_time": "2023-02-15T07:25:51.353672",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===============================================================================================\n",
       "Layer (type:depth-idx)                        Output Shape              Param #\n",
       "===============================================================================================\n",
       "ShakespeareGPT                                [64, 256, 12516]          --\n",
       "├─Embedding: 1-1                              [64, 256, 384]            4,806,144\n",
       "├─Embedding: 1-2                              [256, 384]                98,304\n",
       "├─Sequential: 1-3                             [64, 256, 384]            --\n",
       "│    └─TransformerBlock: 2-1                  [64, 256, 384]            --\n",
       "│    │    └─LayerNorm: 3-1                    [64, 256, 384]            768\n",
       "│    │    └─MultiHeadAttention: 3-2           [64, 256, 384]            --\n",
       "│    │    │    └─ModuleList: 4-13             --                        (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-1      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-2      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-3      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-4      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-5      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-6      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-7      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-8      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-9      [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-10     [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-11     [64, 256, 32]             36,864\n",
       "│    │    │    │    └─AttentionHead: 5-12     [64, 256, 32]             36,864\n",
       "│    │    │    └─Linear: 4-2                  [64, 256, 384]            147,840\n",
       "│    │    │    └─Dropout: 4-3                 [64, 256, 384]            --\n",
       "│    │    └─LayerNorm: 3-3                    [64, 256, 384]            768\n",
       "│    │    └─FeedForward: 3-4                  [64, 256, 384]            --\n",
       "│    │    │    └─Sequential: 4-4              [64, 256, 384]            --\n",
       "│    │    │    │    └─Linear: 5-13            [64, 256, 1536]           591,360\n",
       "│    │    │    │    └─ReLU: 5-14              [64, 256, 1536]           --\n",
       "│    │    │    │    └─Linear: 5-15            [64, 256, 384]            590,208\n",
       "│    │    │    │    └─Dropout: 5-16           [64, 256, 384]            --\n",
       "│    └─TransformerBlock: 2-2                  [64, 256, 384]            (recursive)\n",
       "│    │    └─LayerNorm: 3-5                    [64, 256, 384]            (recursive)\n",
       "│    │    └─MultiHeadAttention: 3-6           [64, 256, 384]            (recursive)\n",
       "│    │    │    └─ModuleList: 4-13             --                        (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-17     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-18     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-19     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-20     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-21     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-22     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-23     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-24     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-25     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-26     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-27     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-28     [64, 256, 32]             (recursive)\n",
       "│    │    │    └─Linear: 4-6                  [64, 256, 384]            (recursive)\n",
       "│    │    │    └─Dropout: 4-7                 [64, 256, 384]            --\n",
       "│    │    └─LayerNorm: 3-7                    [64, 256, 384]            (recursive)\n",
       "│    │    └─FeedForward: 3-8                  [64, 256, 384]            (recursive)\n",
       "│    │    │    └─Sequential: 4-8              [64, 256, 384]            (recursive)\n",
       "│    │    │    │    └─Linear: 5-29            [64, 256, 1536]           (recursive)\n",
       "│    │    │    │    └─ReLU: 5-30              [64, 256, 1536]           --\n",
       "│    │    │    │    └─Linear: 5-31            [64, 256, 384]            (recursive)\n",
       "│    │    │    │    └─Dropout: 5-32           [64, 256, 384]            --\n",
       "│    └─TransformerBlock: 2-3                  [64, 256, 384]            (recursive)\n",
       "│    │    └─LayerNorm: 3-9                    [64, 256, 384]            (recursive)\n",
       "│    │    └─MultiHeadAttention: 3-10          [64, 256, 384]            (recursive)\n",
       "│    │    │    └─ModuleList: 4-13             --                        (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-33     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-34     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-35     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-36     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-37     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-38     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-39     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-40     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-41     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-42     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-43     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-44     [64, 256, 32]             (recursive)\n",
       "│    │    │    └─Linear: 4-10                 [64, 256, 384]            (recursive)\n",
       "│    │    │    └─Dropout: 4-11                [64, 256, 384]            --\n",
       "│    │    └─LayerNorm: 3-11                   [64, 256, 384]            (recursive)\n",
       "│    │    └─FeedForward: 3-12                 [64, 256, 384]            (recursive)\n",
       "│    │    │    └─Sequential: 4-12             [64, 256, 384]            (recursive)\n",
       "│    │    │    │    └─Linear: 5-45            [64, 256, 1536]           (recursive)\n",
       "│    │    │    │    └─ReLU: 5-46              [64, 256, 1536]           --\n",
       "│    │    │    │    └─Linear: 5-47            [64, 256, 384]            (recursive)\n",
       "│    │    │    │    └─Dropout: 5-48           [64, 256, 384]            --\n",
       "│    └─TransformerBlock: 2-4                  [64, 256, 384]            (recursive)\n",
       "│    │    └─LayerNorm: 3-13                   [64, 256, 384]            (recursive)\n",
       "│    │    └─MultiHeadAttention: 3-14          [64, 256, 384]            (recursive)\n",
       "│    │    │    └─ModuleList: 4-13             --                        (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-49     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-50     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-51     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-52     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-53     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-54     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-55     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-56     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-57     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-58     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-59     [64, 256, 32]             (recursive)\n",
       "│    │    │    │    └─AttentionHead: 5-60     [64, 256, 32]             (recursive)\n",
       "│    │    │    └─Linear: 4-14                 [64, 256, 384]            (recursive)\n",
       "│    │    │    └─Dropout: 4-15                [64, 256, 384]            --\n",
       "│    │    └─LayerNorm: 3-15                   [64, 256, 384]            (recursive)\n",
       "│    │    └─FeedForward: 3-16                 [64, 256, 384]            (recursive)\n",
       "│    │    │    └─Sequential: 4-16             [64, 256, 384]            (recursive)\n",
       "│    │    │    │    └─Linear: 5-61            [64, 256, 1536]           (recursive)\n",
       "│    │    │    │    └─ReLU: 5-62              [64, 256, 1536]           --\n",
       "│    │    │    │    └─Linear: 5-63            [64, 256, 384]            (recursive)\n",
       "│    │    │    │    └─Dropout: 5-64           [64, 256, 384]            --\n",
       "│    └─LayerNorm: 2-5                         [64, 256, 384]            768\n",
       "├─Linear: 1-4                                 [64, 256, 12516]          4,818,660\n",
       "===============================================================================================\n",
       "Total params: 11,497,188\n",
       "Trainable params: 11,497,188\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (G): 1.10\n",
       "===============================================================================================\n",
       "Input size (MB): 0.13\n",
       "Forward/backward pass size (MB): 3956.54\n",
       "Params size (MB): 45.99\n",
       "Estimated Total Size (MB): 4002.66\n",
       "==============================================================================================="
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(lm,input_size=(Config.batch_size, Config.block_size),dtypes=[torch.long],depth=5,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7effbfb8",
   "metadata": {
    "papermill": {
     "duration": 0.008219,
     "end_time": "2023-02-15T07:25:51.370486",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.362267",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d294329",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:51.388903Z",
     "iopub.status.busy": "2023-02-15T07:25:51.388045Z",
     "iopub.status.idle": "2023-02-15T07:25:51.393284Z",
     "shell.execute_reply": "2023-02-15T07:25:51.392351Z"
    },
    "papermill": {
     "duration": 0.016493,
     "end_time": "2023-02-15T07:25:51.395282",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.378789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optim = torch.optim.AdamW(lm.parameters(), lr=Config.lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f1c6ad",
   "metadata": {
    "papermill": {
     "duration": 0.008127,
     "end_time": "2023-02-15T07:25:51.411713",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.403586",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Loss Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "086f1997",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:51.429888Z",
     "iopub.status.busy": "2023-02-15T07:25:51.429613Z",
     "iopub.status.idle": "2023-02-15T07:25:51.435255Z",
     "shell.execute_reply": "2023-02-15T07:25:51.434366Z"
    },
    "papermill": {
     "duration": 0.017077,
     "end_time": "2023-02-15T07:25:51.437319",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.420242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def loss_fn(logits, targets):\n",
    "    B,T,C = logits.shape\n",
    "    logits = logits.view(B*T, C)\n",
    "    targets = targets.view(B*T)\n",
    "    loss = F.cross_entropy(logits,targets)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75c133d",
   "metadata": {
    "papermill": {
     "duration": 0.008113,
     "end_time": "2023-02-15T07:25:51.457591",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.449478",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eceb344",
   "metadata": {
    "papermill": {
     "duration": 0.008123,
     "end_time": "2023-02-15T07:25:51.474057",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.465934",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Validation Loop\n",
    "\n",
    "averages over `val_iters` mini-batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed3b5393",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:51.491770Z",
     "iopub.status.busy": "2023-02-15T07:25:51.491493Z",
     "iopub.status.idle": "2023-02-15T07:25:51.497649Z",
     "shell.execute_reply": "2023-02-15T07:25:51.496678Z"
    },
    "papermill": {
     "duration": 0.017211,
     "end_time": "2023-02-15T07:25:51.499553",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.482342",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def valid_N_iters():\n",
    "    val_step_losses = []\n",
    "    for batch in tqdm(range(Config.val_iters)):\n",
    "        inputs, targets = next(val_ds)\n",
    "        inputs, targets = inputs.to(device=Config.device), targets.to(device=Config.device)\n",
    "        logits = lm(inputs)\n",
    "        loss = loss_fn(logits,targets)\n",
    "        val_step_losses.append(loss.item())\n",
    "        \n",
    "        del inputs, targets, loss, logits\n",
    "    \n",
    "    val_loss = torch.tensor(val_step_losses).mean()\n",
    "    print(f'val loss: {val_loss}')\n",
    "    return val_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f80ca9",
   "metadata": {
    "papermill": {
     "duration": 0.008231,
     "end_time": "2023-02-15T07:25:51.516048",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.507817",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training Loop\n",
    "\n",
    "trains over `train_iters` mini-batches, calls `valid_N_iters()` every `eval_interval`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3c0fd65c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:51.534230Z",
     "iopub.status.busy": "2023-02-15T07:25:51.533494Z",
     "iopub.status.idle": "2023-02-15T07:25:51.541687Z",
     "shell.execute_reply": "2023-02-15T07:25:51.540784Z"
    },
    "papermill": {
     "duration": 0.019422,
     "end_time": "2023-02-15T07:25:51.543777",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.524355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_N_iters():\n",
    "    lm.train()\n",
    "    train_step_losses = []\n",
    "    val_losses = []\n",
    "    for batch in tqdm(range(Config.train_iters)):\n",
    "        optim.zero_grad()\n",
    "        inputs, targets = next(train_ds)\n",
    "        inputs, targets = inputs.to(device=Config.device), targets.to(device=Config.device)\n",
    "        logits = lm(inputs)\n",
    "        loss = loss_fn(logits,targets)\n",
    "        loss.backward()\n",
    "        optim.step()\n",
    "        train_step_losses.append(loss.item())\n",
    "        \n",
    "        if batch%(Config.train_iters//10)==0 or batch==Config.train_iters-1:\n",
    "            print(f\"\\n{'-'*50}\\nbatch {batch} train step loss: {loss.item()}\")\n",
    "            print(f\"train loss so far: {torch.tensor(train_step_losses).mean()}\\n{'-'*50}\\n\")\n",
    "            \n",
    "        if batch%Config.eval_interval==0 or batch==Config.train_iters-1:\n",
    "            lm.eval()\n",
    "            val_loss = valid_N_iters()\n",
    "            lm.train()\n",
    "            val_losses.append(val_loss.item())\n",
    "            \n",
    "            del val_loss\n",
    "            \n",
    "        del inputs, targets, loss, logits\n",
    "        \n",
    "    return train_step_losses, val_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0c41d39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:51.561782Z",
     "iopub.status.busy": "2023-02-15T07:25:51.561515Z",
     "iopub.status.idle": "2023-02-15T07:25:51.566623Z",
     "shell.execute_reply": "2023-02-15T07:25:51.565755Z"
    },
    "papermill": {
     "duration": 0.016164,
     "end_time": "2023-02-15T07:25:51.568598",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.552434",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def save_lm():\n",
    "    state_dict = lm.state_dict()\n",
    "    save_path = Path('./').resolve() / 'shakespeareGPT'\n",
    "    save_path.mkdir(exist_ok=True)\n",
    "    model_path = save_path / f'shakespeareGPT.pth'\n",
    "    torch.save(state_dict, model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1e9cc3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:51.586191Z",
     "iopub.status.busy": "2023-02-15T07:25:51.585923Z",
     "iopub.status.idle": "2023-02-15T07:25:51.590331Z",
     "shell.execute_reply": "2023-02-15T07:25:51.589320Z"
    },
    "papermill": {
     "duration": 0.015779,
     "end_time": "2023-02-15T07:25:51.592620",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.576841",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_lm():\n",
    "    train_step_losses,val_losses = train_N_iters()\n",
    "    save_lm()\n",
    "    return train_step_losses,val_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6cc42a27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T07:25:51.610303Z",
     "iopub.status.busy": "2023-02-15T07:25:51.610038Z",
     "iopub.status.idle": "2023-02-15T08:00:35.960025Z",
     "shell.execute_reply": "2023-02-15T08:00:35.958867Z"
    },
    "papermill": {
     "duration": 2084.362058,
     "end_time": "2023-02-15T08:00:35.962895",
     "exception": false,
     "start_time": "2023-02-15T07:25:51.600837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca9acf289ddf4056b1f485d2db429744",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------------------\n",
      "batch 0 train step loss: 9.596895217895508\n",
      "train loss so far: 9.596895217895508\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f0fb69990ff406da9834ff5c2dc945d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 8.731734275817871\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 500 train step loss: 4.1488189697265625\n",
      "train loss so far: 4.848390579223633\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77f7ca5a8a084680b781b2a945159b88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 5.483344078063965\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 1000 train step loss: 3.7014334201812744\n",
      "train loss so far: 4.348177909851074\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0d42e8c7b7447b4a79522e3aa6ed76d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 5.811985969543457\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 1500 train step loss: 3.8010146617889404\n",
      "train loss so far: 4.138303756713867\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a940a4426bc34e9fa3aff0c2b2a8d376",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 5.919593334197998\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 2000 train step loss: 3.9127607345581055\n",
      "train loss so far: 4.051540374755859\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e01903e2fc542d49d5f2e83a4a3a758",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 5.968822479248047\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 2500 train step loss: 4.075058460235596\n",
      "train loss so far: 4.029229164123535\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e905c81048fc49609ced7b219dbd56f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 6.074030876159668\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 3000 train step loss: 4.1568803787231445\n",
      "train loss so far: 4.06320333480835\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28e9945a0a344c22a21a8105df77a16d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 6.033890724182129\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 3500 train step loss: 3.895667791366577\n",
      "train loss so far: 4.05436372756958\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25d16f43bb5049689bfffb86d64c8a82",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 6.209318161010742\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 4000 train step loss: 3.9817676544189453\n",
      "train loss so far: 4.041043281555176\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ea5985251aa45b7bb866e3054280ce3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 6.255189418792725\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 4500 train step loss: 3.8858635425567627\n",
      "train loss so far: 4.032486438751221\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b24f4ca093084b1d9200734fc2760046",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 6.262547492980957\n",
      "\n",
      "--------------------------------------------------\n",
      "batch 4999 train step loss: 4.0820536613464355\n",
      "train loss so far: 4.020419597625732\n",
      "--------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ec12726c76645caa95d92e6d27c18bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val loss: 6.213085174560547\n"
     ]
    }
   ],
   "source": [
    "tsl,vl=train_lm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dac72ea4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T08:00:35.987018Z",
     "iopub.status.busy": "2023-02-15T08:00:35.986261Z",
     "iopub.status.idle": "2023-02-15T08:00:35.993183Z",
     "shell.execute_reply": "2023-02-15T08:00:35.991763Z"
    },
    "papermill": {
     "duration": 0.020828,
     "end_time": "2023-02-15T08:00:35.995226",
     "exception": false,
     "start_time": "2023-02-15T08:00:35.974398",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 4.020419597625732\n"
     ]
    }
   ],
   "source": [
    "tsl_mean = torch.tensor(tsl).mean()\n",
    "print('Train Loss:',tsl_mean.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a47338",
   "metadata": {
    "papermill": {
     "duration": 0.010521,
     "end_time": "2023-02-15T08:00:36.016509",
     "exception": false,
     "start_time": "2023-02-15T08:00:36.005988",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Loss Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a018b58c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T08:00:36.040784Z",
     "iopub.status.busy": "2023-02-15T08:00:36.039114Z",
     "iopub.status.idle": "2023-02-15T08:00:36.432270Z",
     "shell.execute_reply": "2023-02-15T08:00:36.431428Z"
    },
    "papermill": {
     "duration": 0.406975,
     "end_time": "2023-02-15T08:00:36.434179",
     "exception": false,
     "start_time": "2023-02-15T08:00:36.027204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tsl,label='train step loss',color='orange')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.plot(vl,label='validation loss',color='green')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd569cd9",
   "metadata": {
    "papermill": {
     "duration": 0.011183,
     "end_time": "2023-02-15T08:00:36.457078",
     "exception": false,
     "start_time": "2023-02-15T08:00:36.445895",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Generating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e48f275c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-15T08:00:36.480610Z",
     "iopub.status.busy": "2023-02-15T08:00:36.480249Z",
     "iopub.status.idle": "2023-02-15T08:01:15.462693Z",
     "shell.execute_reply": "2023-02-15T08:01:15.461659Z"
    },
    "papermill": {
     "duration": 39.009613,
     "end_time": "2023-02-15T08:01:15.477893",
     "exception": false,
     "start_time": "2023-02-15T08:00:36.468280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generated (100 tokens)\n",
      "==================================================\n",
      " Mortimer's dear, 'My:\n",
      "DORSET head; all posterity with ease and it, I were as a Richard lady.\n",
      "Why you not a\n",
      "\n",
      "Cho I could\n",
      "Soity\n",
      "COMINIUS:\n",
      "\n",
      "But 'Ay,' please you in their masters's hence,\n",
      "Virtue 'ay a false to the ground you have spoke that loves me, in thy bred their l thoughts let's\n",
      "Had thou shouldst and heavy where we may conquer the city.\n",
      "Or all to the mouse,\n",
      "==================================================\n",
      "\n",
      "\n",
      "generated (300 tokens)\n",
      "==================================================\n",
      " eyes.\n",
      "A lip;\n",
      " not,\n",
      "ANTIGONUS:\n",
      "To be.\n",
      "\n",
      "LEONTES:\n",
      "ESCALUS.\n",
      "ESCALUS:\n",
      "No hither said\n",
      "\n",
      "First Servant he have paid so, let me to whip him of the dish it, a man be ingrateful'stor; but right the better whatsoever\n",
      "Third my long on my\n",
      "\n",
      "Go beginserion have said's death present partner, are up the and take.\n",
      "is! Camillo you:\n",
      "POLIXENES:\n",
      "Both dame with a wife, therefore law of year lord become so too,\n",
      "Woe to my dignity, sir, where he came: I warrant to be of her he?\n",
      "He not to my honest bawd, is\n",
      "First unto\n",
      "Clown:\n",
      "\n",
      "boy's,\n",
      "AUTOLYCUS:\n",
      "virt me;\n",
      "\n",
      "and buy; but tradesmen,\n",
      "Behold vengeance have I'll turn my sir, I cannotes?\n",
      "Clown:, let't\n",
      "In a pair of his dict it spit.\n",
      "When we swear for your youth, he here it to move to alms a well\n",
      "ser: and,\n",
      "Shepherd; I had the growth the gods and must not all the Roman knew him.\n",
      "AUTOLYCUS is a fo-side first in her kinsman of all thingss; but\n",
      "Clown:\n",
      "Indeed\n",
      "CAMILLO:\n",
      "Why, my other sweet.\n",
      "with I am:\n",
      "But\n",
      "SICINIUS: and his't be found.\n",
      "Nay, sir.\n",
      "AUTOLYCUS:\n",
      "Sweet is a time of\n",
      "==================================================\n",
      "\n",
      "\n",
      "generated (500 tokens)\n",
      "==================================================\n",
      " to win, sight ' redeem by is.\n",
      "s, if you, then, and the people; for lost on her:\n",
      "\n",
      "So doves me bid of those you go, to be thus\n",
      "DUKE OF YORK!'\n",
      "They singish.\n",
      "Cleomenes broke to-service prince;\n",
      "If not been their magistrate and the field; fourteen with you report it: but grief and from windows there thou have ta' the sacrament, uncle ground-owl.\n",
      "\n",
      "KING RICHARD what news.\n",
      "And, making; but remember against the prince,\n",
      "The chancesserp, marry;\n",
      "Noged to earth eye.\n",
      "Lest in hourly is way of audience, I render vengeance would keep;\n",
      "HENRY BOLINGBROKE:\n",
      "KING RICHARD II:\n",
      "KING RICHARD II:\n",
      "Pit Richard likesor is lost the sun, and darts\n",
      "Take heed,\n",
      "Mistake.\n",
      "Thy part reserved yourelect what art:\n",
      "DUKE OF GAUNT terms\n",
      "RICHARD:\n",
      "\n",
      "A' browsus:\n",
      "Our how sour her breast; I fear not,\n",
      "Servant, that I slewge and they, Bagot.\n",
      "Suggest this night and all dear on me, but one wise words so soon perform.\n",
      "Why, and breaks'st\n",
      "Bishop, cease not linger thee ha serves to us.\n",
      "KING RICHARD II on my unpleased to un.\n",
      "Not your\n",
      "PRINCE EDWARD IV-learned:\n",
      "\n",
      "The world elsewhere are.\n",
      "MENENIUS:\n",
      "She had state,\n",
      "COMINIUS:\n",
      "Will of Edward? Had to jumpeth fairly, then ensue let the duke enforced is for Ireland\n",
      "Some of grief, that flatter the Duke, that I dream of yonder not such a consules; the king, good stars but I shall pity to can bear the king,\n",
      "And looks of your standards have my house being plainly and it cannot\n",
      "\n",
      "Peace sole like mine opinion.\n",
      "Profan will make me, I never may be calms,\n",
      "QUEEN: thou to grant at him take him, whither weal would owe than France.\n",
      "Thou hast thou shalt be remains you chance this a greater:\n",
      "Breathe the dignity,\n",
      "I'Twas your brother's daughter look'd,\n",
      " is the nature is?\n",
      "And both.\n",
      "\n",
      "how\n",
      "you but Fl thy fair posterity:\n",
      "Do never willless.\n",
      "\n",
      "KING to,\n",
      "Not to do but thou wounded off with his care, those that men ne o'clock:\n",
      "As being me not but hear my sword unsway.\n",
      "\n",
      "==================================================\n",
      "\n",
      "\n",
      "generated (700 tokens)\n",
      "==================================================\n",
      "-in-owl un this's pluck fortuneed\n",
      "QUEEN ELIZABETH:\n",
      "\n",
      "\n",
      "RIVERS,\n",
      "HENRY BOLINGBROKE:\n",
      "Edward, to- I\n",
      "TYRREL:\n",
      "Thy Lord destruction left, royal presence:\n",
      "Red offence, that slew, why noble have for help me do lose his bad wither.\n",
      "\n",
      "\n",
      "HENRY BOLINGBROKE, I will shrink this for your presence of God above the Clarence to attain the king's pardon here name your comforter\n",
      "YORK you, C news:\n",
      "\n",
      "My tongue old lord;\n",
      "QUEEN ELIZABETH:\n",
      "\n",
      "RIVERS than you confer,\n",
      "O ill that means\n",
      "The sky,\n",
      "\n",
      "Shall in my glory feeling do it should welcome just with that ignoble miscreant a high deserts- lux.\n",
      "\n",
      "WARWICK:\n",
      "\n",
      "Like up when that by one\n",
      "\n",
      "QUEEN MARGARET:\n",
      "That when I holdorthampton.\n",
      "GLOUCESTER: come;\n",
      "\n",
      "While my expedition cowards to a burthen,\n",
      "EXETER:\n",
      "\n",
      "Methinks the portsHow now hence, and all that's body their sons of Exton,\n",
      "Laid suspicion 'twere time, if this king: I\n",
      "Commendis.\n",
      "And more, your gallery not contented will, and six,\n",
      "Tread, Warwick;\n",
      "Brother's in one doubt\n",
      "YORK:\n",
      "And none\n",
      "Of your, if you\n",
      "Ah, what, ingenious'st kindness\n",
      "Plain, G's sin\n",
      "Which puts a faint it was not, where thou sprang\n",
      "I have pitied hung woman;\n",
      "\n",
      "GLOUCESTER:\n",
      "The purity,\n",
      "The George supply!\n",
      "\n",
      "Foul I was my wrinkled your will lie and appeal in my flatterers\n",
      "\n",
      "Your eyes go.\n",
      "And given I do protest?\n",
      "Or? close,\n",
      "O before, why, and for a well as my grace but I am but thou lovest thoughts more let us king,\n",
      "without!\n",
      "I do notake it thou shalt o' wilfulingst it\n",
      "What answers in the strumpet the earth, or of thee,\n",
      "My elder to the sky, but you would thou talk by-book.\n",
      "Ay,\n",
      "To the hatch:\n",
      "Is\n",
      "The general;' and rather at\n",
      "HENRY BOLINGBROKE:\n",
      "COMINIUS:\n",
      "\n",
      "Down well of that thou unfit, go of those whom thee, stay thee.\n",
      "BUCKINGHAM: the innocent met them now accept to my cabin tempted to me;\n",
      "Have I am none of the breech-cloth none the queen to use me answer of death, thou shalt be crown is the king'st quiet itial:\n",
      "GLOUCESTER:\n",
      "Is here,\n",
      "\n",
      "\n",
      "GLOUCESTER: but shall be done's my free!\n",
      "Yet,\n",
      "And quite mine enemies are weigh him both at Pomfret?\n",
      "'Tis wondrous\n",
      "My crown'd for her,\n",
      "\n",
      "Draw.\n",
      "Nay me,\n",
      "\n",
      "And there you would tell what thou owest again in my father and let me and heaven-hardIf I lament is no;\n",
      "KING RICHARD III:\n",
      "But doth tell the bondage of this cause-night's letters no such stay, they passing to look me?\n",
      "GLOUCESTER:\n",
      "\n",
      "Do thee of mine honour you curse the Tower the all obst me.\n",
      "Without soul.\n",
      "BUCKINGHAM:\n",
      "The common object is my teeth no more holy rood on upon:\n",
      "GLOUCESTER:\n",
      "Where peremptory, for them here, then death.\n",
      "\n",
      "\n",
      "Cousin to be!\n",
      "I would unfit:\n",
      "The chapel,\n",
      "As fat, think I will ye is, you on farewell me my daughter thy love,\n",
      "==================================================\n",
      "\n",
      "\n",
      "generated (1000 tokens)\n",
      "==================================================\n",
      " upon no ag!\n",
      "of season Sicilia\n",
      "Unlawfully 'emember' me embrace sun, my brother.\n",
      "Clubs, if I rise keep;\n",
      "So it with the south't last words's noses, remove fate to fight,\n",
      "Of twenty.\n",
      "CORIOLANUS:\n",
      "Of this blood's,\n",
      "Than, gentle brows, Claudio our cut,\n",
      "COMINIUS:\n",
      "Stands king:\n",
      "\n",
      "SICINIUS:\n",
      "And they areations-trees hours my bidding. This.\n",
      "From slaves shall not this from him:\n",
      "Who's no blood upon souls,\n",
      "Hence to play from wild'd,\n",
      "So much their drums is the drum world\n",
      "They are we held\n",
      "In heaps'd to say,\n",
      "Strike impossibilities.\n",
      "First Citizenaff mock:\n",
      "Each is not stand me?\n",
      "For finger.\n",
      "Ah not stay with Rome\n",
      "\n",
      "Beseech-, seeken the wheel; a creature died of noblerled in his brothers, I have seen\n",
      "First Senator:\n",
      "cond villain should, my lords's time saw have prevented, I hope is their help.\n",
      "Of the Antiates from'd Roman, my gaoler to stop first from her to your senate it.\n",
      "AUFIDIUS:\n",
      "\n",
      "Peace,\n",
      "Offended; Rome'st he lose, shall have drunk\n",
      "SICINIUS:\n",
      "We'll readambering\n",
      "How,\n",
      "CORIOLANUS: yet distinctly good time, husband before him many frown\n",
      "Than of what the means to their\n",
      "Than Hector my incorporate to him.\n",
      "His going the his's begin on in the field,\n",
      "Ay, as God he hath mun him.\n",
      "Of the Volsces thou certain proceed and how are\n",
      "And for his own no more about me;\n",
      "These,\n",
      "His reasons: nothing to fly to many or's brought at the work,\n",
      "That a friend\n",
      "I'll not Marcius\n",
      "CORIOLANUS: what he hath spent out.\n",
      "You know'st,--\n",
      "The gods. I see particular:\n",
      "As cheap were it undo I prize?\n",
      "Makes me be lodged,\n",
      "Where thou knowest my execution's\n",
      "\n",
      "\n",
      "A a royal in despite\n",
      "O is banish, but they are gone man there,\n",
      "The which burns;\n",
      "That chanotion me do it,\n",
      "I got on I did, madam, and envy is it, as\n",
      "\n",
      "'er.\n",
      "Yet-like! by day,\n",
      "If he did threat, and it but rather make in talk'd, for the house I can by the adderers the honourable to then to Rome in myself noble mother own course.\n",
      "SAMPSON prime and who he stay you have it is a reel,\n",
      "\n",
      "None; let them both\n",
      "house me to smile his ill to turn'd forth ere be spare thee that ever you:\n",
      "\n",
      "A yet shame physic,\n",
      "Even had been des our millions her chamber!\n",
      "To say and me not what along;\n",
      "But with\n",
      "Sir you have be by his tears,\n",
      "LADY can you would forget!\n",
      "Of; O,\n",
      "Ne' heads of bleeding had my throne and went of Rome, my time, this mockery sword in France he'st thou lovest out of his own\n",
      "KING RICHARD III:\n",
      "That to this noble child been my tent hither shall be a dream from your another and happy the kingdom I were a look leave, now, I swear here, whereof,\n",
      "QUEEN ELIZABETH:\n",
      "\n",
      "Take not so fitted,\n",
      "\n",
      "\n",
      "\n",
      "Ay, and dom,\n",
      "KING RICHARD III:\n",
      "Children my trembling me.\n",
      "KING RICHARD III:\n",
      "And take thou,\n",
      "\n",
      "As I not a pitch to make him!\n",
      "QUEEN ELIZABETH: he that murders peers his majesty, fair majesty die me\n",
      "DORSET:\n",
      "Our crown and I,\n",
      "And brought?\n",
      "Say a blow I draw! why;\n",
      "Or my crown and my life being sorrow is it how the corse, didst her:\n",
      "\n",
      "' those that Henry is stale,\n",
      "\n",
      "Nay, for I tell me.\n",
      "What devilists of God, be perform'd her\n",
      "Where my Richard kill the realm laghearts me, Vaughans.\n",
      "\n",
      "Whilst me? and doth wounds\n",
      "And I may prevail I have not thou diest,\n",
      "BUCKINGHAM:\n",
      "KING RICHARD III:\n",
      "Thou didst a story her?\n",
      "CATESBY:\n",
      "That he is mew her mother!\n",
      "His body\n",
      "But I behold of my dece time.\n",
      "And frowning\n",
      "BUCKINGHAM:\n",
      "And over not I have prevented not rise up that didst presume lie on at order, ay is apart be not cry him a subject be amended on my threshold can we have but from the flatter their sakelately:\n",
      "Leave some league; and never is it are?\n",
      "QUEEN ELIZABETH:\n",
      "My brothers to us, hence to death in bloody, Jule:\n",
      "What,\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "That'st me with a merryed, farewell of thy business do possess thee,\n",
      "CAPULET:\n",
      "\n",
      "RIVERS, for kings too a long' mouth of that thou overheard\n",
      "==================================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "generated_texts = []\n",
    "for length in [100,300,500,700,1000]:\n",
    "    generated = lm.generate(\n",
    "    torch.zeros((1,1),dtype=torch.long,device=Config.device), # initial context 0\n",
    "    total=length\n",
    ")\n",
    "    generated = tokenizer.decode(generated[0].cpu().numpy())\n",
    "    text=f'generated ({length} tokens)\\n{\"=\"*50}\\n{generated}\\n{\"=\"*50}\\n\\n'\n",
    "    generated_texts.append(text)\n",
    "    print(text)"
   ]
  },
  {
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     "exception": false,
     "start_time": "2023-02-15T08:01:15.492233",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "with open('generated.txt','w') as f:\n",
    "    for text in generated_texts:\n",
    "        f.write(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790103f5",
   "metadata": {
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     "exception": false,
     "start_time": "2023-02-15T08:01:15.524520",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "Full code: [shakespeareGPT](https://github.com/shreydan/shakespeareGPT)\n",
    "\n",
    "Follow [@shreydan](https://kaggle.com/shreydan) for more :)"
   ]
  }
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