byte_latent_transformer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## **Lightweight Implementation of Byte Latent Transformer**\n",
        "Here we implement a charcter-level BLT (Byte Latent Transformer) model from scratch under 500 lines of code. The Byte Latent Transformer architecture is a tokenizer-free architecture that learns from raw byte data, recently introduced in this [paper](https://dl.fbaipublicfiles.com/blt/BLT__Patches_Scale_Better_Than_Tokens.pdf) by meta.\n",
        "\n",
        "BLT processes raw bytes instead of tokens, using dynamic patches based on entropy. The architecture consists of lightweight local encoder, large latent transformer, and lightweight local decoder. This implementation tries to follow the paper as close as possible with simplified versions of each module in the architecture. Below shows illustrations from the paper.\n",
        "\n",
        "\n",
        "<img src=\"assets/blt.jpeg\" />\n",
        "<img src=\"assets/blt2.png\" />"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ktJG5cgKlhPh",
        "outputId": "9f63664c-02ab-436b-b33e-a68cbab5f885"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using device: cuda\n",
            "\n",
            "Hyperparameters:\n",
            "n_embd: 128\n",
            "n_head: 2\n",
            "n_layers_encoder: 2\n",
            "n_layers_latent: 4\n",
            "n_layers_decoder: 2\n",
            "dropout: 0.1\n",
            "vocab_size: 256\n",
            "patch_size: 4\n",
            "Total bytes: 1,115,394\n",
            "Block size: 1024 bytes\n",
            "Batch size: 8 sequences\n",
            "Number of iterations: 2000\n",
            "\n",
            "Training...\n",
            "step 1/2000 | loss 6.0808 | 85166.91 bytes/s | bits-per-byte 8.7727 | 2048 patches\n",
            "step 2/2000 | loss 5.0191 | 85532.09 bytes/s | bits-per-byte 8.0069 | 4096 patches\n",
            "step 3/2000 | loss 4.3742 | 94362.91 bytes/s | bits-per-byte 7.4414 | 6144 patches\n",
            "step 4/2000 | loss 4.1207 | 101116.05 bytes/s | bits-per-byte 7.0673 | 8192 patches\n",
            "step 5/2000 | loss 3.8360 | 106534.23 bytes/s | bits-per-byte 6.7607 | 10240 patches\n",
            "step 6/2000 | loss 3.7044 | 109588.32 bytes/s | bits-per-byte 6.5246 | 12288 patches\n",
            "step 7/2000 | loss 3.6626 | 107635.68 bytes/s | bits-per-byte 6.3474 | 14336 patches\n",
            "step 8/2000 | loss 3.5600 | 108066.74 bytes/s | bits-per-byte 6.1960 | 16384 patches\n",
            "step 9/2000 | loss 3.5207 | 108156.17 bytes/s | bits-per-byte 6.0719 | 18432 patches\n",
            "step 10/2000 | loss 3.5323 | 110291.28 bytes/s | bits-per-byte 5.9743 | 20480 patches\n",
            "step 11/2000 | loss 3.5921 | 112184.91 bytes/s | bits-per-byte 5.9023 | 22528 patches\n",
            "step 12/2000 | loss 3.5153 | 113690.24 bytes/s | bits-per-byte 5.8331 | 24576 patches\n",
            "step 13/2000 | loss 3.5563 | 114835.45 bytes/s | bits-per-byte 5.7790 | 26624 patches\n",
            "step 14/2000 | loss 3.5414 | 114626.84 bytes/s | bits-per-byte 5.7312 | 28672 patches\n",
            "step 15/2000 | loss 3.5630 | 115445.64 bytes/s | bits-per-byte 5.6918 | 30720 patches\n",
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            "step 1945/2000 | loss 0.3365 | 142461.62 bytes/s | bits-per-byte 1.5372 | 3983360 patches\n",
            "step 1946/2000 | loss 0.3111 | 142463.81 bytes/s | bits-per-byte 1.5367 | 3985408 patches\n",
            "step 1947/2000 | loss 0.3376 | 142461.13 bytes/s | bits-per-byte 1.5361 | 3987456 patches\n",
            "step 1948/2000 | loss 0.3512 | 142461.90 bytes/s | bits-per-byte 1.5356 | 3989504 patches\n",
            "step 1949/2000 | loss 0.3235 | 142462.64 bytes/s | bits-per-byte 1.5350 | 3991552 patches\n",
            "step 1950/2000 | loss 0.3347 | 142462.56 bytes/s | bits-per-byte 1.5345 | 3993600 patches\n",
            "step 1951/2000 | loss 0.3512 | 142463.48 bytes/s | bits-per-byte 1.5340 | 3995648 patches\n",
            "step 1952/2000 | loss 0.3124 | 142464.26 bytes/s | bits-per-byte 1.5334 | 3997696 patches\n",
            "step 1953/2000 | loss 0.3471 | 142465.55 bytes/s | bits-per-byte 1.5329 | 3999744 patches\n",
            "step 1954/2000 | loss 0.3341 | 142466.85 bytes/s | bits-per-byte 1.5324 | 4001792 patches\n",
            "step 1955/2000 | loss 0.3278 | 142468.09 bytes/s | bits-per-byte 1.5318 | 4003840 patches\n",
            "step 1956/2000 | loss 0.3435 | 142468.64 bytes/s | bits-per-byte 1.5313 | 4005888 patches\n",
            "step 1957/2000 | loss 0.3167 | 142470.27 bytes/s | bits-per-byte 1.5307 | 4007936 patches\n",
            "step 1958/2000 | loss 0.3163 | 142468.13 bytes/s | bits-per-byte 1.5302 | 4009984 patches\n",
            "step 1959/2000 | loss 0.3444 | 142468.26 bytes/s | bits-per-byte 1.5297 | 4012032 patches\n",
            "step 1960/2000 | loss 0.3470 | 142470.50 bytes/s | bits-per-byte 1.5291 | 4014080 patches\n",
            "step 1961/2000 | loss 0.3450 | 142473.20 bytes/s | bits-per-byte 1.5286 | 4016128 patches\n",
            "step 1962/2000 | loss 0.3411 | 142475.84 bytes/s | bits-per-byte 1.5281 | 4018176 patches\n",
            "step 1963/2000 | loss 0.3307 | 142473.84 bytes/s | bits-per-byte 1.5275 | 4020224 patches\n",
            "step 1964/2000 | loss 0.3669 | 142475.68 bytes/s | bits-per-byte 1.5270 | 4022272 patches\n",
            "step 1965/2000 | loss 0.3314 | 142477.72 bytes/s | bits-per-byte 1.5265 | 4024320 patches\n",
            "step 1966/2000 | loss 0.3298 | 142478.33 bytes/s | bits-per-byte 1.5260 | 4026368 patches\n",
            "step 1967/2000 | loss 0.3575 | 142479.98 bytes/s | bits-per-byte 1.5255 | 4028416 patches\n",
            "step 1968/2000 | loss 0.3406 | 142477.83 bytes/s | bits-per-byte 1.5249 | 4030464 patches\n",
            "step 1969/2000 | loss 0.3069 | 142478.27 bytes/s | bits-per-byte 1.5244 | 4032512 patches\n",
            "step 1970/2000 | loss 0.3617 | 142479.86 bytes/s | bits-per-byte 1.5239 | 4034560 patches\n",
            "step 1971/2000 | loss 0.3487 | 142482.70 bytes/s | bits-per-byte 1.5234 | 4036608 patches\n",
            "step 1972/2000 | loss 0.3367 | 142485.11 bytes/s | bits-per-byte 1.5228 | 4038656 patches\n",
            "step 1973/2000 | loss 0.3197 | 142484.31 bytes/s | bits-per-byte 1.5223 | 4040704 patches\n",
            "step 1974/2000 | loss 0.3396 | 142485.31 bytes/s | bits-per-byte 1.5218 | 4042752 patches\n",
            "step 1975/2000 | loss 0.3162 | 142488.15 bytes/s | bits-per-byte 1.5212 | 4044800 patches\n",
            "step 1976/2000 | loss 0.3111 | 142491.10 bytes/s | bits-per-byte 1.5207 | 4046848 patches\n",
            "step 1977/2000 | loss 0.3132 | 142491.18 bytes/s | bits-per-byte 1.5201 | 4048896 patches\n",
            "step 1978/2000 | loss 0.3348 | 142490.70 bytes/s | bits-per-byte 1.5196 | 4050944 patches\n",
            "step 1979/2000 | loss 0.3446 | 142485.71 bytes/s | bits-per-byte 1.5191 | 4052992 patches\n",
            "step 1980/2000 | loss 0.3440 | 142477.79 bytes/s | bits-per-byte 1.5186 | 4055040 patches\n",
            "step 1981/2000 | loss 0.3695 | 142476.47 bytes/s | bits-per-byte 1.5181 | 4057088 patches\n",
            "step 1982/2000 | loss 0.3384 | 142477.31 bytes/s | bits-per-byte 1.5176 | 4059136 patches\n",
            "step 1983/2000 | loss 0.3435 | 142477.12 bytes/s | bits-per-byte 1.5171 | 4061184 patches\n",
            "step 1984/2000 | loss 0.3154 | 142473.87 bytes/s | bits-per-byte 1.5165 | 4063232 patches\n",
            "step 1985/2000 | loss 0.3401 | 142475.03 bytes/s | bits-per-byte 1.5160 | 4065280 patches\n",
            "step 1986/2000 | loss 0.3263 | 142473.63 bytes/s | bits-per-byte 1.5155 | 4067328 patches\n",
            "step 1987/2000 | loss 0.3325 | 142474.71 bytes/s | bits-per-byte 1.5150 | 4069376 patches\n",
            "step 1988/2000 | loss 0.3090 | 142474.94 bytes/s | bits-per-byte 1.5144 | 4071424 patches\n",
            "step 1989/2000 | loss 0.3467 | 142474.53 bytes/s | bits-per-byte 1.5139 | 4073472 patches\n",
            "step 1990/2000 | loss 0.3339 | 142470.48 bytes/s | bits-per-byte 1.5134 | 4075520 patches\n",
            "step 1991/2000 | loss 0.3257 | 142470.53 bytes/s | bits-per-byte 1.5129 | 4077568 patches\n",
            "step 1992/2000 | loss 0.3262 | 142465.39 bytes/s | bits-per-byte 1.5123 | 4079616 patches\n",
            "step 1993/2000 | loss 0.3221 | 142451.44 bytes/s | bits-per-byte 1.5118 | 4081664 patches\n",
            "step 1994/2000 | loss 0.3174 | 142452.16 bytes/s | bits-per-byte 1.5113 | 4083712 patches\n",
            "step 1995/2000 | loss 0.3461 | 142448.02 bytes/s | bits-per-byte 1.5108 | 4085760 patches\n",
            "step 1996/2000 | loss 0.3312 | 142446.31 bytes/s | bits-per-byte 1.5103 | 4087808 patches\n",
            "step 1997/2000 | loss 0.3300 | 142447.51 bytes/s | bits-per-byte 1.5097 | 4089856 patches\n",
            "step 1998/2000 | loss 0.3347 | 142447.88 bytes/s | bits-per-byte 1.5092 | 4091904 patches\n",
            "step 1999/2000 | loss 0.3338 | 142448.81 bytes/s | bits-per-byte 1.5087 | 4093952 patches\n",
            "step 2000/2000 | loss 0.3129 | 142449.78 bytes/s | bits-per-byte 1.5082 | 4096000 patches\n",
            "\n",
            "Training completed in 115.02 seconds.\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Once upon a at hap\\aoply arcacoaad aroraaking aemmy arname.\n",
            "Commaned hand ararnedoucbodigreesetake her;\n",
            "Pardon me, imavarcelion.\n",
            "Look?\n",
            "\u0005ed waririt is lice,\n",
            "SaysShe evils!\n",
            "You conequal and are\n",
            "Play of maid itwere\n",
            "Proman old aries\n",
            "Cined plocksain of charge. Pray haach;\n",
            "GLouncemabboom\n",
            "Sree\n",
            "Shed a acoal, well, am ELIO:\n",
            "Grayemary wiaHEven yougee hapbed \u0006ring me you hashrou\n",
            "Sgher apuppings had thunder:\n",
            "Clarence restible; into refer, PELI'LDAne doboUpey Lord Nortumbl wing;\n",
            "Plausiege.\n",
            "HO, each and MESExec again\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import math\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Hyperparameters\n",
        "batch_size = 8\n",
        "block_size = 1024\n",
        "patch_size = 4  # Number of bytes per patch\n",
        "num_iters = 2000  # Number of training iterations\n",
        "learning_rate = 1e-3\n",
        "n_embd = 128      # Embedding dimension\n",
        "n_head = 2        # Number of attention heads\n",
        "n_layers_encoder = 2  # lE << lG # Number of layers in the encoder\n",
        "n_layers_latent = 4   # lG # Number of layers in the latent transformer\n",
        "n_layers_decoder = 2  # lD << lG # Number of layers in the decoder\n",
        "dropout = 0.1\n",
        "vocab_size = 256  # Number of unique bytes\n",
        "hash_sizes = range(3, 6)  # n-gram sizes\n",
        "hash_table_size = 50000  # Size of each hash table\n",
        "\n",
        "class LayerNorm(nn.Module):\n",
        "    \"\"\"Custom Layer Normalization.\"\"\"\n",
        "    def __init__(self, ndim, bias=True):\n",
        "        super().__init__()\n",
        "        self.weight = nn.Parameter(torch.ones(ndim))\n",
        "        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None\n",
        "\n",
        "    def forward(self, x):\n",
        "        return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)\n",
        "\n",
        "class Block(nn.Module):\n",
        "    \"\"\"Transformer Block with LayerNorm, Attention, and MLP.\"\"\"\n",
        "    def __init__(self, n_embd, n_head, dropout):\n",
        "        super().__init__()\n",
        "        self.ln1 = LayerNorm(n_embd)\n",
        "        self.attn = nn.MultiheadAttention(embed_dim=n_embd, num_heads=n_head, dropout=dropout, batch_first=True)\n",
        "        self.ln2 = LayerNorm(n_embd)\n",
        "        self.mlp = nn.Sequential(\n",
        "            nn.Linear(n_embd, 4 * n_embd),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(4 * n_embd, n_embd),\n",
        "            nn.Dropout(dropout)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        attn_out, _ = self.attn(self.ln1(x), self.ln1(x), self.ln1(x))\n",
        "        x = x + attn_out\n",
        "        x = x + self.mlp(self.ln2(x))\n",
        "        return x\n",
        "\n",
        "class CrossAttentionLayer(nn.Module):\n",
        "    \"\"\"Cross Attention Layer for Encoder and Decoder.\"\"\"\n",
        "    def __init__(self, query_dim, key_dim, n_head, dropout):\n",
        "        super().__init__()\n",
        "        self.ln_q = LayerNorm(query_dim)\n",
        "        self.ln_kv = LayerNorm(key_dim)\n",
        "        self.attn = nn.MultiheadAttention(embed_dim=query_dim, num_heads=n_head, dropout=dropout, batch_first=True)\n",
        "        self.proj = nn.Linear(query_dim, query_dim)\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "\n",
        "    def forward(self, query, key, value):\n",
        "        query = self.ln_q(query)\n",
        "        key = self.ln_kv(key)\n",
        "        value = self.ln_kv(value)\n",
        "        attn_out, _ = self.attn(query, key, value)\n",
        "        attn_out = self.proj(attn_out)\n",
        "        attn_out = self.dropout(attn_out)\n",
        "        return query + attn_out\n",
        "\n",
        "class HashNGramEmbedding(nn.Module):\n",
        "    \"\"\"Hash n-gram Embeddings.\"\"\"\n",
        "    def __init__(self, hash_sizes, hash_table_size, n_embd):\n",
        "        super().__init__()\n",
        "        self.hash_sizes = hash_sizes\n",
        "        self.hash_table_size = hash_table_size\n",
        "        self.n_embd = n_embd\n",
        "        self.hash_embeddings = nn.ModuleDict({\n",
        "            f\"hash_{n}\": nn.Embedding(hash_table_size, n_embd) for n in hash_sizes\n",
        "        })\n",
        "\n",
        "    def forward(self, x):\n",
        "        B, T = x.shape\n",
        "        embeddings = torch.zeros(B, T, self.n_embd, device=x.device)\n",
        "        for n in self.hash_sizes:\n",
        "            if T < n:\n",
        "                continue\n",
        "            # Extract n-grams\n",
        "            ngrams = x.unfold(1, n, 1)  # [B, T - n +1, n]\n",
        "            # Compute hash\n",
        "            hashes = self.roll_poly_hash(ngrams)\n",
        "            hashes = hashes % self.hash_table_size\n",
        "            # Lookup embeddings\n",
        "            hash_emb = self.hash_embeddings[f\"hash_{n}\"](hashes)  # [B, T - n +1, n_embd]\n",
        "            # Scatter add\n",
        "            embeddings[:, n-1:T, :] += hash_emb\n",
        "        # Normalize\n",
        "        embeddings = embeddings / len(self.hash_sizes)\n",
        "        return embeddings  # [B, T, n_embd]\n",
        "\n",
        "    def roll_poly_hash(self, ngrams):\n",
        "        \"\"\"Simple polynomial rolling hash.\"\"\"\n",
        "        base = 257\n",
        "        hash_val = torch.zeros(ngrams.size(0), ngrams.size(1), device=ngrams.device, dtype=torch.long)\n",
        "        for i in range(ngrams.size(2)):\n",
        "            hash_val = (hash_val * base + ngrams[:, :, i].long()) % (2**32)\n",
        "        return hash_val\n",
        "\n",
        "class LocalEncoder(nn.Module):\n",
        "    \"\"\"Local Encoder that encodes input bytes into patch representations.\"\"\"\n",
        "    def __init__(self, vocab_size, n_embd, patch_size, hash_sizes, hash_table_size, n_head, dropout, lE):\n",
        "        super().__init__()\n",
        "        self.patch_size = patch_size\n",
        "        self.n_embd = n_embd\n",
        "        self.byte_embedding = nn.Embedding(vocab_size, n_embd)\n",
        "        self.hash_ngram = HashNGramEmbedding(hash_sizes, hash_table_size, n_embd)\n",
        "        self.transformer_blocks = nn.ModuleList([Block(n_embd, n_head, dropout) for _ in range(lE)])\n",
        "        self.cross_attn = CrossAttentionLayer(n_embd, n_embd, n_head, dropout)\n",
        "        self.ln = LayerNorm(n_embd)\n",
        "\n",
        "    def forward(self, x):\n",
        "        B, T = x.shape\n",
        "        # Byte Embedding\n",
        "        x_emb = self.byte_embedding(x)  # [B, T, C]\n",
        "        # Hash n-gram Embedding\n",
        "        hash_emb = self.hash_ngram(x)  # [B, T, C]\n",
        "        x_emb = x_emb + hash_emb  # [B, T, C]\n",
        "        # Transformer Layers\n",
        "        for block in self.transformer_blocks:\n",
        "            x_emb = block(x_emb)\n",
        "        # Cross-Attention to form patches\n",
        "        # Assume patches are non-overlapping\n",
        "        # Pad if necessary\n",
        "        if T % self.patch_size != 0:\n",
        "            pad_len = self.patch_size - (T % self.patch_size)\n",
        "            pad = torch.zeros((B, pad_len), dtype=x.dtype, device=x.device).long()\n",
        "            pad_emb = self.byte_embedding(pad)  # [B, pad_len, C]\n",
        "            pad_emb += self.hash_ngram(pad)  # Incorporate hash embeddings\n",
        "            x_emb = torch.cat([x_emb, pad_emb], dim=1)  # [B, T + pad_len, C]\n",
        "            T += pad_len\n",
        "        # Reshape and pool to create patch representations\n",
        "        patches = x_emb.view(B, T // self.patch_size, self.patch_size, self.n_embd).mean(dim=2)  # [B, N_patches, C]\n",
        "        patches = self.cross_attn(patches, x_emb, x_emb)  # [B, N_patches, C]\n",
        "        patches = self.ln(patches)\n",
        "        return patches  # [B, N_patches, C]\n",
        "\n",
        "class LatentTransformer(nn.Module):\n",
        "    \"\"\"Latent Transformer over patch representations.\"\"\"\n",
        "    def __init__(self, n_embd, n_head, n_layers, dropout):\n",
        "        super().__init__()\n",
        "        self.blocks = nn.ModuleList([Block(n_embd, n_head, dropout) for _ in range(n_layers)])\n",
        "        self.ln_f = LayerNorm(n_embd)\n",
        "\n",
        "    def forward(self, x):\n",
        "        for block in self.blocks:\n",
        "            x = block(x)\n",
        "        return self.ln_f(x)\n",
        "\n",
        "class LocalDecoder(nn.Module):\n",
        "    \"\"\"Local Decoder to decode the next patch of bytes.\"\"\"\n",
        "    def __init__(self, vocab_size, n_embd, patch_size, hash_sizes, hash_table_size, n_head, dropout, lD):\n",
        "        super().__init__()\n",
        "        self.patch_size = patch_size\n",
        "        self.vocab_size = vocab_size\n",
        "        self.hash_ngram = HashNGramEmbedding(hash_sizes, hash_table_size, n_embd)\n",
        "        self.cross_attn = CrossAttentionLayer(n_embd, n_embd, n_head, dropout)\n",
        "        self.transformer_blocks = nn.ModuleList([Block(n_embd, n_head, dropout) for _ in range(lD)])\n",
        "        self.byte_proj = nn.Linear(n_embd, patch_size * vocab_size)\n",
        "        self.ln = LayerNorm(vocab_size)  # Corrected to vocab_size\n",
        "\n",
        "    def forward(self, x, patches):\n",
        "        # Cross-Attention: bytes query patches\n",
        "        x = self.cross_attn(x, patches, patches)  # [B, T_patches, C]\n",
        "        # Transformer Blocks\n",
        "        for block in self.transformer_blocks:\n",
        "            x = block(x)\n",
        "        # Project to bytes\n",
        "        x = self.byte_proj(x)  # [B, T_patches, patch_size * vocab_size]\n",
        "        # Reshape to [B, T, vocab_size]\n",
        "        B, T_patches, _ = x.shape\n",
        "        x = x.view(B, T_patches * self.patch_size, self.vocab_size)  # [B, T, vocab_size]\n",
        "        x = self.ln(x)  # [B, T, vocab_size]\n",
        "        return x  # [B, T, vocab_size]\n",
        "\n",
        "class ByteLatentTransformer(nn.Module):\n",
        "    \"\"\"Byte Latent Transformer combining encoder, transformer, and decoder.\"\"\"\n",
        "    def __init__(self, vocab_size, n_embd, n_head, n_layers_encoder, n_layers_latent, n_layers_decoder, dropout, patch_size, hash_sizes, hash_table_size, block_size):\n",
        "        super().__init__()\n",
        "        self.local_encoder = LocalEncoder(vocab_size, n_embd, patch_size, hash_sizes, hash_table_size, n_head, dropout, n_layers_encoder)\n",
        "        self.latent_transformer = LatentTransformer(n_embd, n_head, n_layers_latent, dropout)\n",
        "        self.local_decoder = LocalDecoder(vocab_size, n_embd, patch_size, hash_sizes, hash_table_size, n_head, dropout, n_layers_decoder)\n",
        "        self.pos_embedding = nn.Parameter(torch.zeros(1, block_size // patch_size, n_embd))\n",
        "        self.apply(self._init_weights)\n",
        "\n",
        "    def _init_weights(self, module):\n",
        "        if isinstance(module, nn.Linear):\n",
        "            nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
        "            if module.bias is not None:\n",
        "                nn.init.zeros_(module.bias)\n",
        "        elif isinstance(module, nn.Embedding):\n",
        "            nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
        "\n",
        "    def forward(self, x, targets=None):\n",
        "        # Encode bytes to patches\n",
        "        patches = self.local_encoder(x)  # [B, N_patches, C]\n",
        "        # Add positional embeddings\n",
        "        patches = patches + self.pos_embedding[:, :patches.size(1), :]  # [B, N_patches, C]\n",
        "        # Transform patches\n",
        "        transformed_patches = self.latent_transformer(patches)  # [B, N_patches, C]\n",
        "        # Decode patches to bytes\n",
        "        logits = self.local_decoder(transformed_patches, patches)  # [B, T, vocab_size]\n",
        "        if targets is not None:\n",
        "            B, T, _ = logits.shape\n",
        "            loss = F.cross_entropy(logits.view(B * T, -1), targets.view(-1))\n",
        "        else:\n",
        "            loss = None\n",
        "        return logits, loss\n",
        "\n",
        "class PrepData:\n",
        "    \"\"\"Dataset handling byte-level text data.\"\"\"\n",
        "    def __init__(self, text, block_size, patch_size):\n",
        "        self.data = torch.frombuffer(text.encode('utf-8'), dtype=torch.uint8)\n",
        "        self.block_size = block_size\n",
        "        self.patch_size = patch_size\n",
        "        assert self.block_size % self.patch_size == 0, \"block_size must be divisible by patch_size.\"\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data) - self.block_size - self.patch_size + 1\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        chunk = self.data[idx:idx + self.block_size + self.patch_size]\n",
        "        x = chunk[:self.block_size].long()\n",
        "        y = chunk[1:self.block_size + 1].long()  # Changed to next byte\n",
        "        return x, y\n",
        "\n",
        "def bits_per_byte(loss):\n",
        "    \"\"\"Calculate Bits-Per-Byte (BPB).\"\"\"\n",
        "    return loss / math.log(2)\n",
        "\n",
        "def train_model():\n",
        "    # Setup device\n",
        "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "    print(f'Using device: {device}')\n",
        "\n",
        "    # Load data\n",
        "    try:\n",
        "        with open('data/input.txt', 'r', encoding='utf-8') as f:\n",
        "            text = f.read()\n",
        "    except FileNotFoundError:\n",
        "        print(\"Error: 'input.txt' not found. Please ensure the file exists in the 'data' directory.\")\n",
        "        return None, None\n",
        "\n",
        "    # Data statistics\n",
        "    raw_bytes = text.encode('utf-8')\n",
        "    n_bytes = len(raw_bytes)\n",
        "\n",
        "    # Create dataset and model\n",
        "    dataset = PrepData(text, block_size, patch_size)\n",
        "    model = ByteLatentTransformer(\n",
        "        vocab_size=vocab_size,\n",
        "        n_embd=n_embd,\n",
        "        n_head=n_head,\n",
        "        n_layers_encoder=n_layers_encoder,\n",
        "        n_layers_latent=n_layers_latent,\n",
        "        n_layers_decoder=n_layers_decoder,\n",
        "        dropout=dropout,\n",
        "        patch_size=patch_size,\n",
        "        hash_sizes=hash_sizes,\n",
        "        hash_table_size=hash_table_size,\n",
        "        block_size=block_size\n",
        "    ).to(device)\n",
        "\n",
        "    # Init positional embeddings\n",
        "    nn.init.normal_(model.pos_embedding, mean=0.0, std=0.02)\n",
        "\n",
        "    # Print hyperparameters\n",
        "    print(\"\\nHyperparameters:\")\n",
        "    print(f'n_embd: {n_embd}')\n",
        "    print(f'n_head: {n_head}')\n",
        "    print(f'n_layers_encoder: {n_layers_encoder}')\n",
        "    print(f'n_layers_latent: {n_layers_latent}')\n",
        "    print(f'n_layers_decoder: {n_layers_decoder}')\n",
        "    print(f'dropout: {dropout}')\n",
        "    print(f'vocab_size: {vocab_size}')\n",
        "    print(f'patch_size: {patch_size}')\n",
        "    print(f'Total bytes: {n_bytes:,}')\n",
        "    print(f'Block size: {block_size} bytes')\n",
        "    print(f'Batch size: {batch_size} sequences')\n",
        "    print(f'Number of iterations: {num_iters}')\n",
        "\n",
        "    # Init optimizer\n",
        "    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
        "\n",
        "    # Init metric lists\n",
        "    loss_list = []\n",
        "    bpb_list = []\n",
        "    bytes_per_sec_list = []\n",
        "    patches_list = []\n",
        "\n",
        "    # Training loop\n",
        "    iter_num = 0\n",
        "    start_time = time.time()\n",
        "    total_loss = 0.0\n",
        "    print('\\nTraining...')\n",
        "    while iter_num < num_iters:\n",
        "        # Sample a batch\n",
        "        ix = torch.randint(len(dataset), (batch_size,))\n",
        "        x = torch.stack([dataset[i][0] for i in ix]).to(device)\n",
        "        y = torch.stack([dataset[i][1] for i in ix]).to(device)\n",
        "\n",
        "        # Ensure all indices are within [0, 255]\n",
        "        assert torch.all((x >= 0) & (x < vocab_size)), \"Byte indices out of range!\"\n",
        "        assert torch.all((y >= 0) & (y < vocab_size)), \"Byte indices out of range!\"\n",
        "\n",
        "        # Forward pass\n",
        "        optimizer.zero_grad(set_to_none=True)\n",
        "        logits, loss = model(x, y)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss += loss.item()\n",
        "\n",
        "        # Collect metrics\n",
        "        loss_list.append(loss.item())\n",
        "        current_bpb = bits_per_byte(total_loss / (iter_num + 1))\n",
        "        bpb_list.append(current_bpb)\n",
        "\n",
        "        elapsed = time.time() - start_time\n",
        "        total_bytes = (iter_num + 1) * batch_size * block_size\n",
        "        bytes_per_sec = total_bytes / elapsed if elapsed > 0 else 0\n",
        "        bytes_per_sec_list.append(bytes_per_sec)\n",
        "\n",
        "        est_patches = total_bytes // patch_size\n",
        "        patches_list.append(est_patches)\n",
        "\n",
        "        # Reporting for every iteration]\n",
        "        print(f'step {iter_num +1}/{num_iters} | loss {loss.item():.4f} | '\n",
        "              f'{bytes_per_sec:.2f} bytes/s | bits-per-byte {current_bpb:.4f} | {est_patches} patches')\n",
        "\n",
        "        iter_num += 1\n",
        "\n",
        "    avg_time = time.time() - start_time\n",
        "    print(f'\\nTraining completed in {avg_time:.2f} seconds.')\n",
        "\n",
        "    # Save the trained model\n",
        "    torch.save(model.state_dict(), \"blt_model.pt\")\n",
        "\n",
        "    # Downsample data for plotting if num_iters is large\n",
        "    plot_every = max(1, num_iters // 500)  # Adjust the divisor based on desired number of points\n",
        "    iterations = list(range(1, num_iters + 1, plot_every))\n",
        "    loss_plot = loss_list[::plot_every]\n",
        "    bpb_plot = bpb_list[::plot_every]\n",
        "    bytes_sec_plot = bytes_per_sec_list[::plot_every]\n",
        "    patches_plot = patches_list[::plot_every]\n",
        "\n",
        "    # Plot Loss\n",
        "    plt.figure(figsize=(16, 6))\n",
        "    plt.plot(iterations, loss_plot, label='Loss', color='blue', linewidth=2)\n",
        "    plt.xlabel('Iterations', fontsize=14)\n",
        "    plt.ylabel('Loss', fontsize=14)\n",
        "    plt.title('Loss / Iterations', fontsize=16)\n",
        "    plt.legend(fontsize=12)\n",
        "    plt.xticks(fontsize=12)\n",
        "    plt.yticks(fontsize=12)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Plot Bits-Per-Byte\n",
        "    plt.figure(figsize=(16, 6))\n",
        "    plt.plot(iterations, bpb_plot, label='Bits-Per-Byte', color='orange', linewidth=2)\n",
        "    plt.xlabel('Iterations', fontsize=14)\n",
        "    plt.ylabel('Bits-Per-Byte', fontsize=14)\n",
        "    plt.title('Bits-Per-Byte / Iterations', fontsize=16)\n",
        "    plt.legend(fontsize=12)\n",
        "    plt.xticks(fontsize=12)\n",
        "    plt.yticks(fontsize=12)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Plot Bytes per Second\n",
        "    plt.figure(figsize=(16, 6))\n",
        "    plt.plot(iterations, bytes_sec_plot, label='Bytes/Sec', color='green', linewidth=2)\n",
        "    plt.xlabel('Iterations', fontsize=14)\n",
        "    plt.ylabel('Bytes per Second', fontsize=14)\n",
        "    plt.title('Training Speed / Iterations', fontsize=16)\n",
        "    plt.legend(fontsize=12)\n",
        "    plt.xticks(fontsize=12)\n",
        "    plt.yticks(fontsize=12)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return model, dataset\n",
        "\n",
        "def generate(model, prompt, max_new_bytes=500, temperature=1.0):\n",
        "    \"\"\"Generate text from the model given a prompt.\"\"\"\n",
        "    model.eval()\n",
        "    device = next(model.parameters()).device\n",
        "    try:\n",
        "        x = torch.frombuffer(prompt.encode('utf-8'), dtype=torch.uint8).long().unsqueeze(0).to(device)\n",
        "    except UnicodeEncodeError:\n",
        "        print(\"Error: Prompt contains characters that cannot be encoded in UTF-8.\")\n",
        "        return \"\"\n",
        "\n",
        "    generated_bytes = []\n",
        "    with torch.no_grad():\n",
        "        for _ in range(max_new_bytes):\n",
        "            logits, _ = model(x)\n",
        "            logits = logits[:, -1, :] / temperature\n",
        "            probs = F.softmax(logits, dim=-1)\n",
        "            next_byte = torch.multinomial(probs, num_samples=1)\n",
        "            generated_bytes.append(next_byte.item())\n",
        "            x = torch.cat([x, next_byte.view(1, 1).to(device)], dim=1)\n",
        "\n",
        "    try:\n",
        "        generated_text = bytes(generated_bytes).decode('utf-8')\n",
        "    except UnicodeDecodeError:\n",
        "        # Handle incomplete UTF-8 sequence\n",
        "        generated_text = bytes(generated_bytes[:-1]).decode('utf-8', errors='ignore')\n",
        "    return generated_text\n",
        "\n",
        "# Train model\n",
        "model, dataset = train_model()\n",
        "\n",
        "if model is not None:\n",
        "    # Sample from trained model\n",
        "    prompt = \"Once upon a\"\n",
        "    generated = generate(model, prompt)\n",
        "    print(f\"{prompt} {generated}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zS68cVDn03u",
        "outputId": "0ae5a564-6f1f-4421-a04b-11db7ed63edb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Once upon a ccor genure touggence woure tongue\n",
            "of\n",
            "our before there tne\n",
            "sour high mine wore a toe Perrgoery sorure, it bevsert to ibt rather playour\n",
            "Mangled sterbline rough she fault,\n",
            "Wherefore\n",
            "thing coured by what impose thired habin tery thereof steepAin topbut that libertsy+;\n",
            "For romy tent,\n",
            "\u001eThis morquin\n",
            "Shemenocha not come\n",
            "Come whites\n",
            "COousip her stit ahing thumest,\n",
            "Such words uponim'st purpose\n",
            "To dechest,\n",
            "Wintion!\n",
            "\n",
            "ANTOn, thus sopheichotorious exchase the garmentwoinoble therior turn grief\n",
            "Aster be nilo5r\beo isy hand wordu itance anger, thet sure is there,\n",
            "Coruincoronusting our brother's distrous wontent'st our tale\n",
            "Then &coqueter trut thereby broke birth;\n",
            "The petion anger\n",
            "Wine with treraares\n",
            "Touching stringling Corough or mine outward here cicher tonger:\n",
            "Such parets.\n",
            "\n",
            "ANorw'st therefore betweeere our domething that\n",
            "This, to \u0013ore wereth pure the desires satis for my ncares that aide in corripes over-worcking,\n",
            "Speagreeling,\n",
            "Spart\n",
            "irong affron:\n",
            "Defory joconveyong,\n",
            "Fore worthKheal's di\n"
          ]
        }
      ],
      "source": [
        "if model is not None:\n",
        "    # Sample from trained model\n",
        "    prompt = \"Once upon a\"\n",
        "    generated = generate(model, prompt, max_new_bytes=1000)\n",
        "    print(f\"\\n{prompt} {generated}\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}