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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sanjeevanahilan/nanoChatGPT/blob/master/chatgpt_dev_teaching.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's train models for generating Shakespeare with different sentiments.\n",
        "\n",
        "Happy Shakespeare will say things like: \n",
        "\n",
        "*“Nay, thanks, then, I do meet change, this Romeo. \n",
        "The pleasure of his hair!”*\n",
        "\n",
        "Sad Shakespear will say things like:\n",
        "\n",
        "\n",
        "*“The senators's dead, of their world:\n",
        "Be not for your friends.”*\n",
        "\n"
      ],
      "metadata": {
        "id": "9-LuRS2wAaBS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Notebook created by Sanjeevan Ahilan\n",
        "# GPT implementation was borrowed from Andrej Karpathy \n",
        "# https://colab.research.google.com/drive/1JMLa53HDuA-i7ZBmqV7ZnA3c_fvtXnx-?usp=sharing\n",
        "# My repo: https://github.com/sanjeevanahilan/nanoChatGPT\n",
        "# My twitter: https://twitter.com/sanjeevanahilan\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import functional as F\n",
        "\n",
        "# hyperparameters\n",
        "batch_size = 16 # how many independent sequences will we process in parallel?\n",
        "block_size = 32 # what is the maximum context length for predictions?\n",
        "max_iters = 3000\n",
        "eval_interval = 100\n",
        "learning_rate = 1e-3\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "eval_iters = 200\n",
        "n_embd = 64\n",
        "n_head = 4\n",
        "n_layer = 4\n",
        "dropout = 0.0\n",
        "# ------------\n",
        "\n",
        "torch.manual_seed(1337)\n"
      ],
      "metadata": {
        "id": "aZGtyx5lAOzu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67a0cd77-355e-4c15-f07d-ef3758a87312"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7fa55d231410>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download Shakespeare\n",
        "%time\n",
        "!wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\n",
        "with open('input.txt', 'r', encoding='utf-8') as f:\n",
        "    text = f.read()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DA-Gox1YiM6l",
        "outputId": "e04b1540-6e70-41dc-d32f-a8e234a8c87d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs\n",
            "Wall time: 7.15 µs\n",
            "--2023-03-18 11:49:44--  https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1115394 (1.1M) [text/plain]\n",
            "Saving to: ‘input.txt’\n",
            "\n",
            "input.txt           100%[===================>]   1.06M  --.-KB/s    in 0.03s   \n",
            "\n",
            "2023-03-18 11:49:45 (40.0 MB/s) - ‘input.txt’ saved [1115394/1115394]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Rather than use character level tokenizer lets use tiktoken; Openai's implementation of a Byte Pair Encoding (BPE) tokenizer"
      ],
      "metadata": {
        "id": "SyyJfXbdAr81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tiktoken\n",
        "import tiktoken"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y7LBbGC9onTu",
        "outputId": "6a9ef1dd-2e22-4468-d797-d41cb0bc1de2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tiktoken\n",
            "  Downloading tiktoken-0.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.9/dist-packages (from tiktoken) (2022.10.31)\n",
            "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.9/dist-packages (from tiktoken) (2.27.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests>=2.26.0->tiktoken) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests>=2.26.0->tiktoken) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests>=2.26.0->tiktoken) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests>=2.26.0->tiktoken) (2.0.12)\n",
            "Installing collected packages: tiktoken\n",
            "Successfully installed tiktoken-0.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "enc = tiktoken.get_encoding(\"gpt2\")\n",
        "vocab_size = 50257"
      ],
      "metadata": {
        "id": "btWJmnfapKhn"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# You can see that the sequence of characters 'the' has been encoded as a single\n",
        "# number because it is commonly reoccuring whereas 'thh' requires two numbers to\n",
        "# encode 'th' and 'h'\n",
        "\n",
        "print(enc.encode('the'))\n",
        "print(enc.encode('thh'))\n",
        "print(enc.decode([400]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f9l4JdQCfSd",
        "outputId": "42ac95f9-96cb-4fd4-e110-a339c0e45fcf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1169]\n",
            "[400, 71]\n",
            "th\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and test splits\n",
        "data = torch.tensor(enc.encode(text), dtype=torch.long)\n",
        "n = int(0.9*len(data)) # first 90% will be train, rest val\n",
        "train_data = data[:n]\n",
        "val_data = data[n:]\n",
        "\n",
        "# data loading\n",
        "def get_batch(split):\n",
        "    # generate a small batch of data of inputs x and targets y\n",
        "    data = train_data if split == 'train' else val_data\n",
        "    ix = torch.randint(len(data) - block_size, (batch_size,))\n",
        "    x = torch.stack([data[i:i+block_size] for i in ix])\n",
        "    y = torch.stack([data[i+1:i+block_size+1] for i in ix])\n",
        "    x, y = x.to(device), y.to(device)\n",
        "    return x, y\n",
        "\n",
        "@torch.no_grad()\n",
        "def estimate_loss():\n",
        "    out = {}\n",
        "    model.eval()\n",
        "    for split in ['train', 'val']:\n",
        "        losses = torch.zeros(eval_iters)\n",
        "        for k in range(eval_iters):\n",
        "            X, Y = get_batch(split)\n",
        "            logits, loss = model(X, Y)\n",
        "            losses[k] = loss.item()\n",
        "        out[split] = losses.mean()\n",
        "    model.train()\n",
        "    return out\n",
        "\n",
        "class Head(nn.Module):\n",
        "    \"\"\" one head of self-attention \"\"\"\n",
        "\n",
        "    def __init__(self, head_size):\n",
        "        super().__init__()\n",
        "        self.key = nn.Linear(n_embd, head_size, bias=False)\n",
        "        self.query = nn.Linear(n_embd, head_size, bias=False)\n",
        "        self.value = nn.Linear(n_embd, head_size, bias=False)\n",
        "        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))\n",
        "\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, x):\n",
        "        B,T,C = x.shape\n",
        "        k = self.key(x)   # (B,T,C)\n",
        "        q = self.query(x) # (B,T,C)\n",
        "        # compute attention scores (\"affinities\")\n",
        "        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)\n",
        "        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)\n",
        "        wei = F.softmax(wei, dim=-1) # (B, T, T)\n",
        "        wei = self.dropout(wei)\n",
        "        # perform the weighted aggregation of the values\n",
        "        v = self.value(x) # (B,T,C)\n",
        "        out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)\n",
        "        return out\n",
        "\n",
        "class MultiHeadAttention(nn.Module):\n",
        "    \"\"\" multiple heads of self-attention in parallel \"\"\"\n",
        "\n",
        "    def __init__(self, num_heads, head_size):\n",
        "        super().__init__()\n",
        "        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n",
        "        self.proj = nn.Linear(n_embd, n_embd)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = torch.cat([h(x) for h in self.heads], dim=-1)\n",
        "        out = self.dropout(self.proj(out))\n",
        "        return out\n",
        "\n",
        "class FeedFoward(nn.Module):\n",
        "    \"\"\" a simple linear layer followed by a non-linearity \"\"\"\n",
        "\n",
        "    def __init__(self, n_embd):\n",
        "        super().__init__()\n",
        "        self.net = nn.Sequential(\n",
        "            nn.Linear(n_embd, 4 * n_embd),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(4 * n_embd, n_embd),\n",
        "            nn.Dropout(dropout),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.net(x)\n",
        "\n",
        "class Block(nn.Module):\n",
        "    \"\"\" Transformer block: communication followed by computation \"\"\"\n",
        "\n",
        "    def __init__(self, n_embd, n_head):\n",
        "        # n_embd: embedding dimension, n_head: the number of heads we'd like\n",
        "        super().__init__()\n",
        "        head_size = n_embd // n_head\n",
        "        self.sa = MultiHeadAttention(n_head, head_size)\n",
        "        self.ffwd = FeedFoward(n_embd)\n",
        "        self.ln1 = nn.LayerNorm(n_embd)\n",
        "        self.ln2 = nn.LayerNorm(n_embd)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x + self.sa(self.ln1(x))\n",
        "        x = x + self.ffwd(self.ln2(x))\n",
        "        return x\n",
        "\n",
        "class GPT(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        # each token directly reads off the logits for the next token from a lookup table\n",
        "        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n",
        "        self.position_embedding_table = nn.Embedding(block_size, n_embd)\n",
        "        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n",
        "        self.ln_f = nn.LayerNorm(n_embd) # final layer norm\n",
        "        self.lm_head = nn.Linear(n_embd, vocab_size)\n",
        "\n",
        "    def forward(self, idx, targets=None):\n",
        "        B, T = idx.shape\n",
        "\n",
        "        # idx and targets are both (B,T) tensor of integers\n",
        "        tok_emb = self.token_embedding_table(idx) # (B,T,C)\n",
        "        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\n",
        "        x = tok_emb + pos_emb # (B,T,C)\n",
        "        x = self.blocks(x) # (B,T,C)\n",
        "        x = self.ln_f(x) # (B,T,C)\n",
        "        logits = self.lm_head(x) # (B,T,vocab_size)\n",
        "\n",
        "        if targets is None:\n",
        "            loss = None\n",
        "        else:\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",
        "\n",
        "        return logits, loss\n",
        "\n",
        "    def generate(self, idx, max_new_tokens):\n",
        "        # idx is (B, T) array of indices in the current context\n",
        "        for _ in range(max_new_tokens):\n",
        "            # crop idx to the last block_size tokens\n",
        "            idx_cond = idx[:, -block_size:]\n",
        "            # get the predictions\n",
        "            logits, loss = self(idx_cond)\n",
        "            # focus only on the last time step\n",
        "            logits = logits[:, -1, :] # becomes (B, C)\n",
        "            # apply softmax to get probabilities\n",
        "            probs = F.softmax(logits, dim=-1) # (B, C)\n",
        "            # sample from the distribution\n",
        "            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n",
        "            # append sampled index to the running sequence\n",
        "            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)\n",
        "        return idx\n",
        "\n",
        "model = GPT()\n",
        "m = model.to(device)\n",
        "# print the number of parameters in the model\n",
        "print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')\n",
        "\n",
        "# create a PyTorch optimizer\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
        "\n",
        "for iter in range(max_iters):\n",
        "\n",
        "    # every once in a while evaluate the loss on train and val sets\n",
        "    if iter % eval_interval == 0 or iter == max_iters - 1:\n",
        "        losses = estimate_loss()\n",
        "        print(f\"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}\")\n",
        "\n",
        "    # sample a batch of data\n",
        "    xb, yb = get_batch('train')\n",
        "\n",
        "    # evaluate the loss\n",
        "    logits, loss = model(xb, yb)\n",
        "    optimizer.zero_grad(set_to_none=True)\n",
        "    loss.backward()\n",
        "    optimizer.step()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngS-jQschyJc",
        "outputId": "8dedfa53-bdf7-446c-d335-887bf740efab"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6.684497 M parameters\n",
            "step 0: train loss 10.9982, val loss 11.0105\n",
            "step 100: train loss 6.3955, val loss 6.4657\n",
            "step 200: train loss 6.0255, val loss 6.1584\n",
            "step 300: train loss 5.7608, val loss 5.9578\n",
            "step 400: train loss 5.4809, val loss 5.7327\n",
            "step 500: train loss 5.2543, val loss 5.5400\n",
            "step 600: train loss 5.1017, val loss 5.4008\n",
            "step 700: train loss 4.9718, val loss 5.3382\n",
            "step 800: train loss 4.8850, val loss 5.2539\n",
            "step 900: train loss 4.8084, val loss 5.1674\n",
            "step 1000: train loss 4.7187, val loss 5.0770\n",
            "step 1100: train loss 4.6621, val loss 5.1166\n",
            "step 1200: train loss 4.5916, val loss 5.0159\n",
            "step 1300: train loss 4.5599, val loss 5.0297\n",
            "step 1400: train loss 4.4998, val loss 4.9929\n",
            "step 1500: train loss 4.4481, val loss 4.9933\n",
            "step 1600: train loss 4.4182, val loss 4.9204\n",
            "step 1700: train loss 4.4202, val loss 4.9445\n",
            "step 1800: train loss 4.3644, val loss 4.9217\n",
            "step 1900: train loss 4.3479, val loss 4.9221\n",
            "step 2000: train loss 4.2925, val loss 4.8603\n",
            "step 2100: train loss 4.2446, val loss 4.8765\n",
            "step 2200: train loss 4.2491, val loss 4.8753\n",
            "step 2300: train loss 4.1900, val loss 4.8271\n",
            "step 2400: train loss 4.1705, val loss 4.8660\n",
            "step 2500: train loss 4.1608, val loss 4.8778\n",
            "step 2600: train loss 4.1379, val loss 4.8898\n",
            "step 2700: train loss 4.1021, val loss 4.8513\n",
            "step 2800: train loss 4.1099, val loss 4.8742\n",
            "step 2900: train loss 4.0874, val loss 4.8534\n",
            "step 2999: train loss 4.0720, val loss 4.8296\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate from the model\n",
        "context = torch.zeros((1, 1), dtype=torch.long, device=device)\n",
        "print(enc.decode(m.generate(context, max_new_tokens=100)[0].tolist()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9C93Lk1biRLE",
        "outputId": "94a58a5d-a9fb-438f-ec29-ab231b145951"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "! what any consul\n",
            "matter they have we be for so dishonour\n",
            "Redpt'd in our courtesy,\n",
            "Or his highness i' the embracements, nor feeds had an err:\n",
            "I have the hallissign daughter to pawn'd.\n",
            "and stout see me: but God they forget\n",
            "And meet the tribranch man,\n",
            "Of a brace art'd; I'll plant show't\n",
            "From whom I a joyful with a chair access with callat.\n",
            "\n",
            "HENRY\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "from transformers import pipeline\n",
        "sentiment_pipeline = pipeline(model=\"finiteautomata/bertweet-base-sentiment-analysis\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675,
          "referenced_widgets": [
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            "c7570cd91b564389b59c98a694ab7771"
          ]
        },
        "id": "f93j9jAXuK8S",
        "outputId": "d585128a-3fad-4415-e564-fe9d338c4726"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.27.1-py3-none-any.whl (6.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m90.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.2-py3-none-any.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.10.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m93.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.2 tokenizers-0.13.2 transformers-4.27.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8fb0a31c3ecf4a4880803e81e1bc581d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ddbbcddaba34416881ebb023864b0f2d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4712217939b44f59e58a0f23a8d4df7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "emoji is not installed, thus not converting emoticons or emojis into text. Install emoji: pip3 install emoji==0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inps = [\"I'm the king of the world!\", \n",
        "        \"I'll be back.\",\n",
        "        \"The cake is a lie\", \n",
        "        \"To be forgotten is worse than death\",\n",
        "        \"All happy families are alike; each unhappy family is unhappy in its own way.\",\n",
        "        \"You don't need a reason to help people\",\n",
        "        ]\n",
        "res = sentiment_pipeline(inps)\n",
        "\n",
        "for i in range(len(inps)):\n",
        "  res[i]['text'] = inps[i]\n",
        "  print(res[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-fryBdOyKSD",
        "outputId": "671ed206-a9eb-4793-992b-da9378535088"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'label': 'POS', 'score': 0.9771729707717896, 'text': \"I'm the king of the world!\"}\n",
            "{'label': 'POS', 'score': 0.5481611490249634, 'text': \"I'll be back.\"}\n",
            "{'label': 'NEG', 'score': 0.7581188678741455, 'text': 'The cake is a lie'}\n",
            "{'label': 'NEG', 'score': 0.8209365606307983, 'text': 'To be forgotten is worse than death'}\n",
            "{'label': 'NEU', 'score': 0.7874237895011902, 'text': 'All happy families are alike; each unhappy family is unhappy in its own way.'}\n",
            "{'label': 'NEU', 'score': 0.8731082081794739, 'text': \"You don't need a reason to help people\"}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_reward(text, mode):\n",
        "  sent = sentiment_pipeline(text)\n",
        "  if mode == '+ve':\n",
        "    labels = torch.tensor([a['label']=='POS' for a in sent],dtype=torch.float16).unsqueeze(-1).to(device)\n",
        "  elif mode == '-ve':\n",
        "    labels = torch.tensor([a['label']=='NEG' for a in sent],dtype=torch.float16).unsqueeze(-1).to(device)\n",
        "  else:\n",
        "    raise ValueError('Unknown Mode')\n",
        "  \n",
        "  weights = torch.tensor([a['score'] for a in sent],dtype=torch.float32).unsqueeze(-1).to(device)\n",
        "  \n",
        "  rewards = labels * weights # (B, 1)\n",
        "\n",
        "  return rewards"
      ],
      "metadata": {
        "id": "St_giJWTZ19y"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def flatten(l):\n",
        "    return [item for sublist in l for item in sublist]\n",
        "print('Rewards in +ve mode')\n",
        "list(zip(inps, flatten(get_reward(inps, '+ve').tolist())))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmjSzmslbZZc",
        "outputId": "52846692-ffce-4659-f6cb-f0d9040b6f9d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rewards in +ve mode\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(\"I'm the king of the world!\", 0.9771729707717896),\n",
              " (\"I'll be back.\", 0.5481611490249634),\n",
              " ('The cake is a lie', 0.0),\n",
              " ('To be forgotten is worse than death', 0.0),\n",
              " ('All happy families are alike; each unhappy family is unhappy in its own way.',\n",
              "  0.0),\n",
              " (\"You don't need a reason to help people\", 0.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Rewards in -ve mode')\n",
        "list(zip(inps, flatten(get_reward(inps, '-ve').tolist())))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzUDgNwGcfAH",
        "outputId": "2b0ba9e3-41e9-4450-b5b0-d6cee2eb4a00"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rewards in -ve mode\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(\"I'm the king of the world!\", 0.0),\n",
              " (\"I'll be back.\", 0.0),\n",
              " ('The cake is a lie', 0.7581188678741455),\n",
              " ('To be forgotten is worse than death', 0.8209365606307983),\n",
              " ('All happy families are alike; each unhappy family is unhappy in its own way.',\n",
              "  0.0),\n",
              " (\"You don't need a reason to help people\", 0.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eval_interval_rlhf = 20\n",
        "max_iters_rlhf = 1000\n"
      ],
      "metadata": {
        "id": "37tezEm-CFXR"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.distributions import Categorical\n",
        "class RLHF(nn.Module):\n",
        "    def __init__(self, model):\n",
        "        super().__init__()\n",
        "        self.model = model\n",
        "\n",
        "    def forward(self, idx, targets=None):\n",
        "        return self.model(idx, targets)\n",
        "     \n",
        "    def generate(self, idx, max_new_tokens, block_size, ref_model=None):\n",
        "        # idx is (B, T) array of indices in the current context\n",
        "        log_probs = torch.tensor([]).to(device)\n",
        "        log_probs_ref = torch.tensor([]).to(device)\n",
        "        \n",
        "        for i in range(max_new_tokens):\n",
        "            # crop idx to the last block_size tokens\n",
        "            idx_cond = idx[:, -block_size:]\n",
        "\n",
        "            # get the predictions\n",
        "            logits, loss = self(idx_cond)\n",
        "\n",
        "            # focus only on the last time step\n",
        "            logits = logits[:, -1, :] # becomes (B, C)\n",
        "            \n",
        "            # logits define instance of Iategorical class\n",
        "            m = Categorical(logits=logits)\n",
        "            \n",
        "            # sample from the distribution\n",
        "            idx_next = m.sample()\n",
        "            \n",
        "            # get the log probability and append to running sequence\n",
        "            log_probs_idx_next = m.log_prob(idx_next)    \n",
        "            log_probs = torch.cat((log_probs, log_probs_idx_next.view(-1,1)), dim=1)\n",
        "            \n",
        "            if ref_model is not None:\n",
        "              # get log probability of sample idx_next under the reference model\n",
        "              logits_ref, _ = ref_model(idx_cond)\n",
        "              logits_ref = logits_ref[:, -1, :] # becomes (B, C)\n",
        "            \n",
        "              m_ref = Categorical(logits=logits_ref)\n",
        "              log_probs_ref_idx_next = m_ref.log_prob(idx_next)    \n",
        "              log_probs_ref = torch.cat((log_probs_ref, log_probs_ref_idx_next.view(-1,1)), dim=1)\n",
        "\n",
        "            # append sampled index to the running sequence\n",
        "            idx = torch.cat((idx, idx_next.view(-1,1)), dim=1) # (B, T+1)\n",
        "\n",
        "        return idx, log_probs, log_probs_ref"
      ],
      "metadata": {
        "id": "Rp4iqC2RWm6n"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import copy\n",
        "ref_model = copy.deepcopy(model)"
      ],
      "metadata": {
        "id": "4Z_-ygQYYhal"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "import numpy as np\n",
        "\n",
        "RLHFmodel = RLHF(model)\n",
        "RLHFmodel.to(device)\n",
        "\n",
        "ref_model.to(device)\n",
        "\n",
        "actor_optimizer = torch.optim.AdamW(RLHFmodel.parameters(), lr=1e-3)\n",
        "X, Y = get_batch('train') # fetch the very first batch\n",
        "X = torch.ones((X.shape[0], 1), dtype=torch.long).to(device) # for now there is no prompt\n",
        "X = X*enc.encode('The')[0] # start with ''The'\n",
        "t0  = time.time()\n",
        "max_new_tokens = block_size\n",
        "rews_all = []\n",
        "actor_loss_all = []\n",
        "mode = '+ve'\n",
        "ref_coef = 0.2\n",
        "e_coef = 0.1\n",
        "for iter in range(max_iters_rlhf):\n",
        "\n",
        "  states, log_probs, log_probs_ref = RLHFmodel.generate(\n",
        "      X, max_new_tokens, block_size, ref_model=ref_model)\n",
        "\n",
        "  states = states[:,-max_new_tokens:]\n",
        "  log_probs = log_probs[:,-max_new_tokens:] # (B, max_new_tokens)\n",
        "  if ref_model is not None:\n",
        "    log_probs_ref = log_probs_ref[:,-max_new_tokens:] # (B, max_new_tokens)\n",
        "  \n",
        "  rewards = get_reward([enc.decode(s.tolist()) for s in states], mode)\n",
        "  \n",
        "  pg = (rewards+ref_coef*log_probs_ref-e_coef*log_probs)* log_probs.squeeze()\n",
        "  \n",
        "  # log(1) = 0\n",
        "  # -log(1/N) = log(N)\n",
        "\n",
        "  # when ref_coef=e_coef this is equivalent to penalising for KL divergence\n",
        "  # pg = (rewards-ref_coef*(log_probs-log_probs_ref)* log_probs.squeeze() \n",
        "  \n",
        "  actor_loss = -pg.sum()\n",
        "\n",
        "  actor_optimizer.zero_grad(set_to_none=True)\n",
        "  actor_loss.backward()\n",
        "  actor_optimizer.step()\n",
        "\n",
        "  rews_all.append(rewards.mean().detach().cpu().numpy())\n",
        "  actor_loss_all.append(actor_loss.detach().cpu().numpy())\n",
        "\n",
        "  if iter % eval_interval_rlhf == 0:\n",
        "      t1 = time.time()\n",
        "      print('\\n')\n",
        "      print(f'iter: {iter}, time: {t1-t0}')\n",
        "      print(f'Actor loss: {np.mean(actor_loss_all[-eval_interval_rlhf:])}')\n",
        "      print(f'rets: {np.mean(rews_all[-eval_interval_rlhf:])}')\n",
        "\n",
        "      textRLHF = RLHFmodel.generate(X, 2*max_new_tokens, block_size, ref_model=None)[0]\n",
        "      for i in range(1):\n",
        "          text_i = textRLHF[i,:]\n",
        "          print(enc.decode(text_i.tolist()))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZ7HG0qAahgo",
        "outputId": "0c6b1d4a-c4a8-4c63-ae51-3f399f5c343b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "iter: 0, time: 2.0925092697143555\n",
            "Actor loss: -1174.6953125\n",
            "rets: 0.15557555854320526\n",
            "The nobles\n",
            "withoveseed\n",
            "Of our dimina to the narrow, of mine,\n",
            "For this earth brawling some fathers, thou shalt\n",
            "Con exceeds bow an ostows that were I\n",
            "actioning Roe that word;\n",
            "For what 'twas, likeers Lartft,\n",
            "And whose secret maid\n",
            "\n",
            "\n",
            "iter: 20, time: 43.471943855285645\n",
            "Actor loss: -1276.6146240234375\n",
            "rets: 0.08284272998571396\n",
            "The deceived!\n",
            "\n",
            "SICINs:\n",
            "The very faults of Norfolk is the cousin of sword;\n",
            "And, by me how so, seldom the last of that wounds\n",
            "Or much power home his clish'd utterhire, wine of that dame'd\n",
            "Of my life as the loss of death, be\n",
            "\n",
            "\n",
            "iter: 40, time: 83.07254672050476\n",
            "Actor loss: -1171.9931640625\n",
            "rets: 0.12373824417591095\n",
            "The bride, wives are\n",
            "Mis kneel to repent him for heaven and in debt.\n",
            "Look, thou fly, makes your beauty is his rash, I will not\n",
            "Say a side that taught their robbery.\n",
            "Mis up the demands Hastings.\n",
            "\n",
            "JULNot SAL:\n",
            "Your brother is drawn, and in\n",
            "\n",
            "\n",
            "iter: 60, time: 123.09890699386597\n",
            "Actor loss: -1200.064208984375\n",
            "rets: 0.1355905532836914\n",
            "The clouds in the blood'd age'd fearful's throne,\n",
            "To the priestred of me.\n",
            "\n",
            "HERMAMILLO:\n",
            "She are Imen, dear out your grace\n",
            "Whichose on your mother of the Duke of prince young\n",
            "Of blood of foul sword! I love upon your feet\n",
            "and ban\n",
            "\n",
            "\n",
            "iter: 80, time: 163.42521929740906\n",
            "Actor loss: -1015.2078247070312\n",
            "rets: 0.19954833388328552\n",
            "The loss ofitiveness.\n",
            "\n",
            "PAULINA:\n",
            "I am about the noble house of this presence,\n",
            " Rouro too words of England,\n",
            "And nothing, then all so, weeping yet\n",
            "Your grace comesstrance ouride the proudous thoughts!\n",
            "\n",
            "alt! Rome, pastUMNIA:\n",
            "\n",
            "\n",
            "iter: 100, time: 203.27433586120605\n",
            "Actor loss: -904.3199462890625\n",
            "rets: 0.2031252384185791\n",
            "The brother.\n",
            "\n",
            "MMeasure about the loved,\n",
            "Our letters of your worthy Gaunt lady:\n",
            "But four a town is worth'd by him,\n",
            "To rigence yield hand, there I am anyhee,\n",
            "I' love that steed our brother\n",
            "Than in bount about to joy,\n",
            "And\n",
            "\n",
            "\n",
            "iter: 120, time: 243.294597864151\n",
            "Actor loss: -1021.115234375\n",
            "rets: 0.16891352832317352\n",
            "The thrustied\n",
            "Against his majesty to- idol and entence?\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "Is that was too late that when love.\n",
            "\n",
            "JOHN OF GAUNT:\n",
            "Bend, fear of the dukedom.\n",
            "\n",
            "First Lord:\n",
            "I along, p stopp The\n",
            "\n",
            "\n",
            "iter: 140, time: 283.161762714386\n",
            "Actor loss: -1081.0517578125\n",
            "rets: 0.16179659962654114\n",
            "The set-antied live,\n",
            "So bed change with the sovereign.\n",
            "\n",
            "CAMILLIUS:\n",
            "At me, sir. arise, welcome my lords.\n",
            "\n",
            "First Citizen:\n",
            "What someever,\n",
            "I know it again so little, that I have\n",
            "say that done sweat prisoner to Hermione,\n",
            "\n",
            "\n",
            "iter: 160, time: 323.04486894607544\n",
            "Actor loss: -994.4212036132812\n",
            "rets: 0.18603841960430145\n",
            "The get hath a true,\n",
            "If our land, like every laws;\n",
            "Some common pomp--\n",
            "\n",
            "NOROLYCUS:\n",
            "For fit.\n",
            "\n",
            "NORTHUMBERLAND:\n",
            "'Tis a man that I think one mine too:\n",
            "Her consent breathing night.\n",
            "\n",
            "QUEEN MARG\n",
            "\n",
            "\n",
            "iter: 180, time: 362.9912130832672\n",
            "Actor loss: -1087.4278564453125\n",
            "rets: 0.16612616181373596\n",
            "The seven; that else,\n",
            "If very Lancaster, prisoners hope, I'll sit,\n",
            "By our honouredraidign,\n",
            "Whilst endiniusial earued for that and meet,\n",
            "Who took mean you what thou fool nort\n",
            "More than they-rate moleer therefore from thygood course:\n",
            "\n",
            "\n",
            "\n",
            "iter: 200, time: 402.95860266685486\n",
            "Actor loss: -878.0130004882812\n",
            "rets: 0.22536030411720276\n",
            "The day,\n",
            "Had she in theEWway of all!\n",
            "DUCHESS OF lord, I will not sound in theirAL.\n",
            "\n",
            "RICHARD:\n",
            "\n",
            "Purs III:\n",
            "Your Mow me, pale!\n",
            "\n",
            "LUCIO:\n",
            "\n",
            "ClABETH:\n",
            "O me,\n",
            "\n",
            "\n",
            "iter: 220, time: 442.6643555164337\n",
            "Actor loss: -704.8284301757812\n",
            "rets: 0.2918173372745514\n",
            "Thebleness'd\n",
            " liberties us. Thouce statue,\n",
            "The good you had a jest of a name?\n",
            "\n",
            "TYBOW:\n",
            "This I love,\n",
            "So you in oneseech you, as but\n",
            " bloody fre gentleman's well-day? marry much tortures,\n",
            "Of your grace,\n",
            "\n",
            "\n",
            "iter: 240, time: 482.6602349281311\n",
            "Actor loss: -1022.29150390625\n",
            "rets: 0.1844368577003479\n",
            "The sons?\n",
            "Lord ROSS cloy that our maid, where there with honour\n",
            "That I sleep to the soldiers.\n",
            "\n",
            "RUKE VINCENTIO:\n",
            "J the boy; but, to giving my go.\n",
            "\n",
            "HASTINGS:\n",
            "The better of too great little vanity.\n",
            "\n",
            "Second\n",
            "\n",
            "\n",
            "iter: 260, time: 522.2385444641113\n",
            "Actor loss: -835.5398559570312\n",
            "rets: 0.2348378598690033\n",
            "The absence of you must the\n",
            "the heard you have full this 'em.\n",
            "\n",
            "QUEEN MARGARET:\n",
            " punishment is love, g hand of you or\n",
            "More than thidalines.\n",
            "\n",
            "PARIS:\n",
            "Is aATES affails morely then, I have\n",
            "k Thomas mercy.\n",
            "\n",
            "\n",
            "iter: 280, time: 562.4093163013458\n",
            "Actor loss: -739.087890625\n",
            "rets: 0.2898559868335724\n",
            "The suspicious: nor shall come a cause.\n",
            "\n",
            "BENVOLUMNIA:\n",
            "I give a king.\n",
            "\n",
            "POLIXENES:\n",
            "' they are kind,Pray follows as I look,\n",
            "Is last.\n",
            "\n",
            "CAMILLd.\n",
            "\n",
            "KING EDWARD IV:\n",
            "Talk,\n",
            "\n",
            "\n",
            "iter: 300, time: 601.8023648262024\n",
            "Actor loss: -852.7369384765625\n",
            "rets: 0.20052771270275116\n",
            "The soldiers.\n",
            "b moved how and things, give me not despise.\n",
            "\n",
            "ISABELLA:\n",
            "Ay, sir, further, were my lord;\n",
            "Behee the child', for King your high temples.\n",
            "Come, let us this prince!\n",
            "I must make him too younger whither of his\n",
            "\n",
            "\n",
            "iter: 320, time: 641.1348361968994\n",
            "Actor loss: -746.7435913085938\n",
            "rets: 0.2848814129829407\n",
            "The nobles of your hand?\n",
            "\n",
            "deepT:\n",
            "And these inferior shall writ thee here? What that shall I were we better and mine\n",
            "Now do for gentle learning to ours.\n",
            "\n",
            "SUNLORIZEL:\n",
            "Be patient time to their eyes to up thy brother of rose:\n",
            "Tell him,\n",
            "\n",
            "\n",
            "iter: 340, time: 680.8197565078735\n",
            "Actor loss: -876.5494995117188\n",
            "rets: 0.2481001913547516\n",
            "The mile live?\n",
            "If shall I will dwell round what love.\n",
            "Marry, take what I sit: give me?\n",
            "\n",
            "MENENIUS:\n",
            "And Warwick's mad?\n",
            "O shrewakret please you here;\n",
            "And all that I'll not.\n",
            "3 KING EDWARD IV:\n",
            "E\n",
            "\n",
            "\n",
            "iter: 360, time: 720.4318358898163\n",
            "Actor loss: -697.50244140625\n",
            "rets: 0.3108132779598236\n",
            "The king will bear the royal, like a ones,\n",
            "And gains day.\n",
            "\n",
            "CAMILLO:\n",
            "I dare this, I thank them, on a made, or else\n",
            "Is stay at the morning are think about, by eyes\n",
            "inish moreinateers.' Thisush's.\n",
            "\n",
            "KING ED\n",
            "\n",
            "\n",
            "iter: 380, time: 760.4552597999573\n",
            "Actor loss: -825.5812377929688\n",
            "rets: 0.2534932792186737\n",
            "The stroke of Norfolk.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "The statue stay, I they are withdraw to your king,\n",
            "They hold your majestyire to the found it that\n",
            "for lead the villain to stir, all love, till hear many\n",
            "will what I you have fall'd with you.\n",
            "\n",
            "\n",
            "\n",
            "iter: 400, time: 799.7820234298706\n",
            "Actor loss: -680.2034301757812\n",
            "rets: 0.306072473526001\n",
            "The must fight and their friends that,\n",
            "It and letcentio. I love, come, good lords,\n",
            " Hark, what furthercing him them.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "You were YourTo give thee--\n",
            "It is Thomas in Henry forsweness\n",
            "That many these days\n",
            "\n",
            "\n",
            "iter: 420, time: 839.0483448505402\n",
            "Actor loss: -846.8916015625\n",
            "rets: 0.25812074542045593\n",
            "The other of Warwick,\n",
            "With though it honours, for the See of set\n",
            "As thou stands by this R weary had won,\n",
            "Let his heads and glst delight.\n",
            "\n",
            "KING HENVOLIO:\n",
            "I thank thou not ables--My lord; I pray more, the\n",
            "Sh design,\n",
            "\n",
            "\n",
            "iter: 440, time: 878.8783130645752\n",
            "Actor loss: -863.8115234375\n",
            "rets: 0.2483484447002411\n",
            "The grace are be now,\n",
            "And encounters now going married.\n",
            "\n",
            "AONTESC:\n",
            "AndSo one we king, for heaven thought;\n",
            "SomeENT give that a honesty neighbour following thine.\n",
            "\n",
            "HASTINGS:\n",
            "If such map with thou hemaniful.\n",
            "\n",
            "LADY\n",
            "\n",
            "\n",
            "iter: 460, time: 918.7519881725311\n",
            "Actor loss: -773.82421875\n",
            "rets: 0.3029620051383972\n",
            "The victory body's wish you well Warwick.\n",
            "\n",
            "First Servant::\n",
            "My lord, noble nobleself for my holpge!\n",
            "Served, bloody is this grave; but my study\n",
            " toweratingond degree unto be drawn appear,\n",
            "The better.\n",
            "\n",
            "KING EDWARD IV:\n",
            "I reason\n",
            "\n",
            "\n",
            "iter: 480, time: 957.8695306777954\n",
            "Actor loss: -790.4373168945312\n",
            "rets: 0.2682585120201111\n",
            "The point of this. You sun,\n",
            "How fares the vault I, is; itad, seem sm indirectutio:\n",
            "Heio,\n",
            "I will one to thy sake to make your tongue.\n",
            "\n",
            "HENRY BOLARET:\n",
            "God you, dead: tell him came?\n",
            "\n",
            "S\n",
            "\n",
            "\n",
            "iter: 500, time: 997.3224573135376\n",
            "Actor loss: -603.6134033203125\n",
            "rets: 0.3471847474575043\n",
            "The ground is best with all subjects\n",
            "inn shed, I come itself, such content coats,--\n",
            "Or, come, a present in things,\n",
            "And by the morning.\n",
            "\n",
            "KING EDWARD IV:\n",
            "I thank thee, and a word as they have you joy,\n",
            "have you, to show.\n",
            "\n",
            "\n",
            "\n",
            "iter: 520, time: 1036.6428000926971\n",
            "Actor loss: -643.1966552734375\n",
            "rets: 0.3397030532360077\n",
            "The house of mine hand's anger\n",
            "In an day, my gracious lord.\n",
            "Come it, so did I hunt to breed friends,\n",
            "Who unh prominous faults, then of Rome time\n",
            "Shall reg us, with the tombity witness\n",
            "In sorrow, here: here are more bed\n",
            "Be frowns love\n",
            "\n",
            "\n",
            "iter: 540, time: 1076.3727550506592\n",
            "Actor loss: -708.031005859375\n",
            "rets: 0.3134646415710449\n",
            "The edge!\n",
            "\n",
            "\n",
            "LUCIO:\n",
            "Ah, acting you untime,\n",
            "That I say 'twas: pray they are it\n",
            "In wo; to carryeech you richer;\n",
            "Like beasts what fellow? Pray, dear you shall fly you\n",
            "Yourself to have no such a and true\n",
            "\n",
            "\n",
            "iter: 560, time: 1115.688039779663\n",
            "Actor loss: -682.0416259765625\n",
            "rets: 0.3094523847103119\n",
            "The place, deputy\n",
            "Of England.\n",
            "\n",
            "DUKE OF AUMLE:\n",
            "Where you what stands it? Pray? I'll give you\n",
            "Even with the churchous daughter of us!\n",
            "\n",
            "NORTHUMBERLAND:\n",
            "Nay, come to our wlockish'd hand,\n",
            "The\n",
            "\n",
            "\n",
            "iter: 580, time: 1155.1704773902893\n",
            "Actor loss: -584.12646484375\n",
            "rets: 0.36164161562919617\n",
            "The price;\n",
            "And so shall I loveing ignorant have\n",
            "To husband. send myself are\n",
            "Sinceives? THR modestyible,\n",
            "On neither in the fire have done.\n",
            "\n",
            "BUCKINGHAM:\n",
            "Let you your love. I\n",
            "\n",
            "ISABELLA:\n",
            "Good brother, good mad\n",
            "\n",
            "\n",
            "iter: 600, time: 1194.873331785202\n",
            "Actor loss: -517.8790283203125\n",
            "rets: 0.39595964550971985\n",
            "The king.\n",
            "\n",
            "HENRY PER:\n",
            "Well, the in York, love.\n",
            "\n",
            "VOLUMNIA:\n",
            "I'll cutign defend my lord.\n",
            "\n",
            "MENENIUS:\n",
            "Go, be prosperous so:\n",
            "O will-morrow in the father's love.\n",
            "\n",
            "ROMEO:\n",
            "\n",
            "\n",
            "iter: 620, time: 1234.6517779827118\n",
            "Actor loss: -632.740966796875\n",
            "rets: 0.3639276921749115\n",
            "The nature man would respect drunk:\n",
            "business my mind receives and fear of his way\n",
            "As to their proceedings.\n",
            "\n",
            "VOLUMNIA:\n",
            "I would I love for this cap, a tear'd,\n",
            "But, love him should be not that hath\n",
            "ighthART you undertake, to go.\n",
            "\n",
            "CLA\n",
            "\n",
            "\n",
            "iter: 640, time: 1273.8108813762665\n",
            "Actor loss: -603.2329711914062\n",
            "rets: 0.37746334075927734\n",
            "The earth,\n",
            "to not worse set his knife, a Clarence apprehend,\n",
            "To be me; while him and Romeo so.\n",
            "\n",
            "DUKE OF YORK:\n",
            "I doeech thee the pleasureier:\n",
            "Didishedief make Sweet loved him my knowledge!\n",
            "\n",
            "DUKE OF YORK:\n",
            "\n",
            "\n",
            "\n",
            "iter: 660, time: 1313.7660591602325\n",
            "Actor loss: -787.6739501953125\n",
            "rets: 0.288984090089798\n",
            "The second,\n",
            "First Senator itself, when I will come there have his\n",
            "With the princely like clouds.\n",
            "\n",
            "DUKE OF YORK:\n",
            "CAPULETis, we dream I love a doth thy retire.\n",
            "\n",
            "RICHHENRY BWiltIO:\n",
            "O, I come to\n",
            "\n",
            "\n",
            "iter: 680, time: 1353.1964864730835\n",
            "Actor loss: -592.0076293945312\n",
            "rets: 0.36385422945022583\n",
            "The point and least'd in casting for our king;\n",
            "I shall have where he, too enrich,--\n",
            "Withape: what is 'not there by the state; and all love,\n",
            "I say he may sit.\n",
            "\n",
            "KING RICHARD III:\n",
            "If she is your longbs. He out\n",
            "\n",
            "\n",
            "\n",
            "iter: 700, time: 1392.60701918602\n",
            "Actor loss: -641.7145385742188\n",
            "rets: 0.3566742539405823\n",
            "The course?\n",
            "\n",
            "MENENIUS:\n",
            "Your gracious lord.\n",
            "3 KING HENRY VI.\n",
            "\n",
            "First Senator:\n",
            "O, lady'st, he is that you?\n",
            "\n",
            "GLOUCESTER:\n",
            "Well, Romeo, where is my lord, I\n",
            "ilt desire you. spirit\n",
            "\n",
            "\n",
            "iter: 720, time: 1432.1645839214325\n",
            "Actor loss: -631.0171508789062\n",
            "rets: 0.33802512288093567\n",
            "The sweet tears.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "I know if woe?\n",
            "\n",
            "DU informIA:\n",
            "My gracious brother, as protest;\n",
            "A slave clouds, I hath i'ld. O bird.\n",
            "\n",
            "NORTHUMBERLAND:\n",
            "Be length's child mercy,\n",
            "\n",
            "\n",
            "iter: 740, time: 1472.0193173885345\n",
            "Actor loss: -770.1849365234375\n",
            "rets: 0.306596577167511\n",
            "The provost: I thank you, be little man.\n",
            "\n",
            "HORTENSIO:\n",
            "Thou hast my son, speak.\n",
            "\n",
            "DUKE OF YORK:\n",
            "No, it is a happy\n",
            "ears my nime so writ;\n",
            "And nowcreator entertainment to behold.\n",
            "\n",
            "KING EDWARD IV\n",
            "\n",
            "\n",
            "iter: 760, time: 1511.5322704315186\n",
            "Actor loss: -654.1215209960938\n",
            "rets: 0.34393274784088135\n",
            "Theou is A spirit.\n",
            "\n",
            "KING EDWARD IV:\n",
            "Come, I do know it so, most greatness,\n",
            "Tread, I will proud scene him. My a throne\n",
            "Well, then, sweet for a debt on war,\n",
            "Thus Barnards and himself to happy one hark.\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "iter: 780, time: 1550.8467333316803\n",
            "Actor loss: -635.3596801757812\n",
            "rets: 0.32344189286231995\n",
            "The bed of the face?\n",
            "\n",
            "HENRY BOLINGBROKE:\n",
            "HereORDoler thou odd!\n",
            "Of it not too, prefer.\n",
            "\n",
            "ARD III:\n",
            "I come,\n",
            "Upon them.\n",
            "\n",
            "HERGDUCHESS OF AUM:\n",
            "\n",
            "NORTHUMBERLAND\n",
            "\n",
            "\n",
            "iter: 800, time: 1590.659930229187\n",
            "Actor loss: -659.7162475585938\n",
            "rets: 0.3370627164840698\n",
            "The noble\n",
            " Pleaseing, for grace him as with the excellent of their\n",
            "To frame out onte.\n",
            "\n",
            "MERBIONDELL:\n",
            "I know for the orderly spirit to know\n",
            "WhomENTIO.\n",
            "Why'll may have free\n",
            "Of your stronger law of smiles of another give:\n",
            "O\n",
            "\n",
            "\n",
            "iter: 820, time: 1629.6801323890686\n",
            "Actor loss: -540.1078491210938\n",
            "rets: 0.3763672113418579\n",
            "The common of thee.\n",
            "\n",
            "QUEEN ELIZABETH:\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "God hold me well I am this noble soul we.\n",
            "\n",
            "KING EDWARD IV:\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "Come, I'll blELL you, subjects enough\n",
            "\n",
            "\n",
            "iter: 840, time: 1669.0213844776154\n",
            "Actor loss: -715.7509765625\n",
            "rets: 0.3233412206172943\n",
            "The sleep,\n",
            "And being, doublere our forbear, loving friends,\n",
            "A prett hath thou grow patient;\n",
            "Whom thou hastest? scalares,--\n",
            "\n",
            "PAULINA:\n",
            "Will he did your pleasure would please him down.\n",
            "\n",
            "LUCIO:\n",
            " marry much, good lord,\n",
            "\n",
            "\n",
            "iter: 860, time: 1708.6020300388336\n",
            "Actor loss: -698.3497924804688\n",
            "rets: 0.3224356472492218\n",
            "The trick can shed. Who'\n",
            "KATHARENCE:\n",
            "Darest thou did prove I never usur heart, our unroble spent;\n",
            "We hope, to rise, love you shall kill enver.\n",
            " You will them more, I will not speak thee.\n",
            "\n",
            "KING EDWARD IV:\n",
            "A\n",
            "\n",
            "\n",
            "iter: 880, time: 1748.3153533935547\n",
            "Actor loss: -658.4968872070312\n",
            "rets: 0.31735074520111084\n",
            "The unt in my lner.\n",
            "\n",
            "ComeUMNIA:\n",
            "He cannot good queen of his\n",
            "Than it in our exnely days, 'em,\n",
            "As not too.\n",
            "\n",
            "MERCALUS:\n",
            "'Tis you have peril'd.\n",
            "There's rose out of the matter snow\n",
            "\n",
            "\n",
            "iter: 900, time: 1787.5656242370605\n",
            "Actor loss: -619.747314453125\n",
            "rets: 0.3222334086894989\n",
            "The heads to do thou\n",
            "To free!Glad the gates is do?\n",
            "SICINCE of Norfolk to the world to London and mother.\n",
            "Alearch.\n",
            "\n",
            "Second Citizen:\n",
            "That she of the old bright:\n",
            "The gates of the house?\n",
            "\n",
            "GLOUCESTER:\n",
            "All\n",
            "\n",
            "\n",
            "iter: 920, time: 1827.115246772766\n",
            "Actor loss: -655.8629760742188\n",
            "rets: 0.3531707227230072\n",
            "The gageeth man.\n",
            "\n",
            "WARD IV:\n",
            "And therefore, wife!\n",
            "\n",
            "ELBRAY, a slaveies fear is\n",
            "Do I playraw you to-likely:\n",
            "I have I love else thy breast?\n",
            "Hear what is made me: thou shalt stand in 'tis happy tallprovided\n",
            "\n",
            "\n",
            "iter: 940, time: 1866.3652894496918\n",
            "Actor loss: -479.79656982421875\n",
            "rets: 0.40305882692337036\n",
            "The worth, that my gracious lords.\n",
            "\n",
            "May sheANUS:\n",
            "Your love, like that will prove.\n",
            "\n",
            "PARIS:\n",
            "Why, for you have pin, luck was born,\n",
            "To bring you the common, what an blood of the\n",
            "Even are starkly the gods: I will have it\n",
            "\n",
            "\n",
            "iter: 960, time: 1905.8332304954529\n",
            "Actor loss: -573.760986328125\n",
            "rets: 0.36464911699295044\n",
            "The sorrow, yet they will gates.\n",
            "\n",
            "RIARINA:\n",
            "She! This is Lord Hastings\n",
            "Shall be a well up heaven,\n",
            "If this sin in himself.\n",
            "\n",
            "KING EDWARD IV:\n",
            "\n",
            "CLARENCE:\n",
            "My Lord?\n",
            "\n",
            "AUFIDIUS:\n",
            "What pain\n",
            "\n",
            "\n",
            "iter: 980, time: 1945.2791361808777\n",
            "Actor loss: -630.5237426757812\n",
            "rets: 0.34373268485069275\n",
            "The law, I love his\n",
            "To win them: I seem cannot be found; 'em me no.\n",
            "\n",
            "ISABHCFICHARDINE:\n",
            "Go, peace, misay, than we must you.\n",
            "\n",
            "PARians!\n",
            "A:\n",
            "\n",
            "KING RICHARD III:\n",
            "I thank\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# generate from the model\n",
        "context = torch.zeros((1, 1), dtype=torch.long, device=device)\n",
        "print(enc.decode(m.generate(context, max_new_tokens=200)[0].tolist()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mjv63OeQ0bUI",
        "outputId": "af51f8a4-a8fc-4861-a005-22ae9eb93695"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "!\n",
            "You be right,\n",
            "And triumph; am so, my good\n",
            "to of general; if thou husband\n",
            "As never sunder best against my blood.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "Then like a Romansers, art speed at the queen,\n",
            "Fromagenetings, my tears. But\n",
            "\n",
            "ulet:\n",
            "Which was what I break scarce; where then.\n",
            "For this we'll bear the dewance of man!\n",
            "\n",
            "POLIXENES:\n",
            "I'll swear soon I will say out a happyker you.\n",
            "Hear thee.\n",
            "\n",
            "DUKE VINCENTIO:\n",
            "But since our life, Lord, Titus me,\n",
            "For I love this wed is the battle your man.\n",
            "\n",
            "CATESBY:\n",
            "Farewell you, why.\n",
            "\n",
            "CLAUDIO:\n",
            " if you hear that live Not gone, sent, let our headsarer\n",
            "wixt the XIant cannot talkings\n",
            "Should is cl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. We trained a GPT model to reproduce Shakespeare\n",
        "2. We built a reward model by repurposing a Huggingface sentiment classifier\n",
        "3. We fine tuned the GPT model using reinforcement learning. \n",
        "4. The model over-optimised the reward so we penalised it for moving too far from a reference model\n",
        "5. We found it to be far too repetitive and so we added in an entropy bonus to encourage diverse outputs\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "6Na4mH46sFpx"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PJrQpMzU9gWJ"
      },
      "execution_count": 17,
      "outputs": []
    }
  ]
}