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"StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "source": [ "!pip install tiktoken" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rif5UBx0oax7", "outputId": "10099131-7924-478f-8c2e-16dd194bdab1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting tiktoken\n", " Downloading tiktoken-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n", "Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.11/dist-packages (from tiktoken) (2024.11.6)\n", "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.11/dist-packages (from tiktoken) (2.32.3)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (3.4.1)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (2.3.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.26.0->tiktoken) (2024.12.14)\n", "Downloading tiktoken-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n", "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/1.2 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: tiktoken\n", "Successfully installed tiktoken-0.8.0\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_RTMeP0AhVKB", "outputId": "dece4dcf-25f6-41da-8c66-73ed3a4f12f9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", "Iteration: 3, Loss: 8.895743370056152\n", "Iteration: 4, Loss: 8.894034385681152\n", "Iteration: 5, Loss: 8.956920623779297\n", "Iteration: 6, Loss: 8.526345252990723\n", "Iteration: 7, Loss: 8.221501350402832\n", "Iteration: 8, Loss: 8.360747337341309\n", "Iteration: 9, Loss: 8.43831729888916\n", "Iteration: 10, Loss: 8.21973705291748\n", "Iteration: 11, Loss: 7.850347518920898\n", "Iteration: 12, Loss: 7.379423141479492\n", "Iteration: 13, Loss: 7.561660289764404\n", "Iteration: 14, Loss: 7.156119346618652\n", "Iteration: 15, Loss: 7.289281845092773\n", "Iteration: 16, Loss: 6.914283752441406\n", 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GPT2Tokenizer, AdamW\n", "import tiktoken\n", "\n", "@dataclass\n", "class Config:\n", " vocab_size: int = 50257\n", " max_seq_len: int = 2048\n", " dim: int = 768\n", " num_layers: int = 12\n", " num_heads: int = 12\n", " dropout: float = 0.1\n", "\n", "class MultiHeadAttention(nn.Module):\n", " def __init__(self, config):\n", " super().__init__()\n", " self.config = config\n", " self.n_head = config.num_heads\n", " self.n_embd = config.dim\n", "\n", " # Linear projections for Q, K, V\n", " self.c_attn = nn.Linear(config.dim, 3 * config.dim) # [n_embd, 3 * n_embd]\n", " self.c_proj = nn.Linear(config.dim, config.dim) # [n_embd, n_embd]\n", "\n", " self.attn_dropout = nn.Dropout(config.dropout)\n", " self.resid_dropout = nn.Dropout(config.dropout)\n", "\n", " def forward(self, x):\n", " B, T, C = x.size() # [B, T, n_embd]\n", "\n", " # Linear projection and split into Q, K, V\n", " q, k, v = self.c_attn(x).split(self.n_embd, dim=2) # [B, T, n_embd] each\n", "\n", " # Reshape for multi-head attention\n", " k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head]\n", " q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head]\n", " v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head]\n", "\n", " # Attention scores\n", " att = (q @ k.transpose(-2, -1)) * (1.0 / (k.size(-1) ** 0.5)) # [B, n_head, T, T]\n", " att = F.softmax(att, dim=-1) # [B, n_head, T, T]\n", " att = self.attn_dropout(att) # [B, n_head, T, T]\n", "\n", " # Weighted sum of values\n", " y = att @ v # [B, n_head, T, n_embd/n_head]\n", "\n", " # Reshape and project\n", " y = y.transpose(1, 2).contiguous().view(B, T, C) # [B, T, n_embd]\n", " y = self.c_proj(y) # [B, T, n_embd]\n", " y = self.resid_dropout(y) # [B, T, n_embd]\n", "\n", " return y\n", "\n", "class FeedForward(nn.Module):\n", " def __init__(self, config):\n", " super().__init__()\n", " self.c_fc = nn.Linear(config.dim, 4 * config.dim) # [n_embd, 4 * n_embd]\n", " self.c_proj = nn.Linear(4 * config.dim, config.dim) # [4 * n_embd, n_embd]\n", " self.dropout = nn.Dropout(config.dropout)\n", "\n", " def forward(self, x):\n", " x = self.c_fc(x) # [B, T, 4 * n_embd]\n", " x = F.gelu(x) # [B, T, 4 * n_embd]\n", " x = self.c_proj(x) # [B, T, n_embd]\n", " x = self.dropout(x) # [B, T, n_embd]\n", " return x\n", "\n", "class TransformerBlock(nn.Module):\n", " def __init__(self, config):\n", " super().__init__()\n", " self.ln_1 = nn.LayerNorm(config.dim) # [n_embd]\n", " self.attn = MultiHeadAttention(config)\n", " self.ln_2 = nn.LayerNorm(config.dim) # [n_embd]\n", " self.mlp = FeedForward(config)\n", "\n", " def forward(self, x):\n", " x = x + self.attn(self.ln_1(x)) # [B, T, n_embd]\n", " x = x + self.mlp(self.ln_2(x)) # [B, T, n_embd]\n", " return x\n", "\n", "def train(model, tokenizer, train_dataset, epochs=3, batch_size=4, learning_rate=5e-5):\n", " \"\"\"Trains the DecoderOnlyTransformer model.\"\"\"\n", "\n", " # Prepare optimizer and loss function\n", " optimizer = AdamW(model.parameters(), lr=learning_rate)\n", " loss_fn = nn.CrossEntropyLoss()\n", "\n", " # Training loop\n", " for epoch in range(epochs):\n", " for batch in train_dataset:\n", " # Tokenize input text\n", " input_ids = tokenizer(batch['text'], return_tensors=\"pt\", padding=True, truncation=True).input_ids\n", "\n", " # Move data to the same device as the model\n", " input_ids = input_ids.to(next(model.parameters()).device)\n", "\n", " # Forward pass\n", " logits = model(input_ids[:, :-1]) # Predict next token for each token in the sequence\n", "\n", " # Calculate loss\n", " loss = loss_fn(logits.reshape(-1, logits.size(-1)), input_ids[:, 1:].reshape(-1))\n", "\n", " # Backpropagation and optimization\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " print(f\"Epoch: {epoch + 1}, Loss: {loss.item()}\")\n", "\n", "class DecoderOnlyTransformer(nn.Module):\n", " def __init__(self, config):\n", " super().__init__()\n", " self.config = config\n", " self.wte = nn.Embedding(config.vocab_size, config.dim) # [vocab_size, n_embd]\n", " self.wpe = nn.Embedding(config.max_seq_len, config.dim) # [max_seq_len, n_embd]\n", " self.drop = nn.Dropout(config.dropout)\n", " self.blocks = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_layers)])\n", " self.ln_f = nn.LayerNorm(config.dim) # [n_embd]\n", " self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False) # [n_embd, vocab_size]\n", "\n", " self.apply(self._init_weights)\n", "\n", " def _init_weights(self, module):\n", " if isinstance(module, (nn.Linear, nn.Embedding)):\n", " module.weight.data.normal_(mean=0.0, std=0.02)\n", " if isinstance(module, nn.Linear) and module.bias is not None:\n", " module.bias.data.zero_()\n", " elif isinstance(module, nn.LayerNorm):\n", " module.bias.data.zero_()\n", " module.weight.data.fill_(1.0)\n", "\n", " def forward(self, idx):\n", " B, T = idx.size() # [B, T]\n", "\n", " # Positional embeddings\n", " pos = torch.arange(0, T, dtype=torch.long, device=idx.device).unsqueeze(0) # [1, T]\n", "\n", " # Token and position embeddings\n", " tok_emb = self.wte(idx) # [B, T, n_embd]\n", " pos_emb = self.wpe(pos) # [1, T, n_embd]\n", "\n", " # Combine embeddings and apply dropout\n", " x = self.drop(tok_emb + pos_emb) # [B, T, n_embd]\n", "\n", " # Transformer blocks\n", " for block in self.blocks:\n", " x = block(x) # [B, T, n_embd]\n", "\n", " # Final layer norm and linear projection\n", " x = self.ln_f(x) # [B, T, n_embd]\n", " logits = self.lm_head(x) # [B, T, vocab_size]\n", "\n", " return logits\n", "class DataLoaderLite:\n", " def __init__(self, B, T):\n", " self.B = B\n", " self.T = T\n", " # at init load tokens from disk and store them in memory\n", " with open('input.txt', 'r') as f:\n", " text = f.read()\n", " enc = tiktoken.get_encoding('gpt2')\n", " tokens = enc.encode(text)\n", " self.tokens = torch.tensor(tokens)\n", " print(f'loaded {len(self.tokens)} tokens')\n", " print(f'1 epoch = {len(self.tokens) // (B * T)} batches')\n", " # state\n", " self.current_position = 0\n", " def next_batch(self):\n", " B, T = self.B, self.T\n", " buf = self.tokens[self.current_position: self.current_position + B * T + 1]\n", " x = (buf[:-1]).view(B, T) # inputs\n", " y = (buf[1:]).view(B, T) # targets\n", " # advance the position in the tensor\n", " self.current_position += B*T\n", " # if loading the next batch would be out of bounds, reset\n", " if self.current_position + (B * T + 1) > len(self.tokens):\n", " self.current_position = 0\n", " return x, y\n", "\n", "\n", "\n", "if __name__ == '__main__':\n", " use_cuda = torch.cuda.is_available()\n", " device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n", " config = Config()\n", " model = DecoderOnlyTransformer(config)\n", " model.to(device)\n", "\n", " # Train the model\n", " train_loader = DataLoaderLite(B = 4, T = 128)\n", " # NEW CODE\n", " optimizer = torch.optim.AdamW(model.parameters(), lr = 3e-4)\n", " loss_fn = nn.CrossEntropyLoss() # Define loss_fn here\n", " for i in range(5000):\n", " x, y = train_loader.next_batch()\n", " x, y = x.to(device), y.to(device)\n", " optimizer.zero_grad()\n", " logits = model(x)\n", " loss = loss_fn(logits.reshape(-1, logits.size(-1)), y.reshape(-1)) # Calculate loss using logits and target\n", " loss.backward()\n", " optimizer.step()\n", "\n", " print(f\"Iteration: {i + 1}, Loss: {loss.item()}\") # Change to iteration\n", "\n", "\n", "\n", " print(\"Input shape:\", x.shape)\n", " print(\"Output shape:\", logits.shape)\n", "\n" ] }, { "cell_type": "code", "source": [ "prompt = \"First Citizen\"\n", "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n", "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n", "input_ids = input_ids.to(device)\n", "output = model(input_ids)\n", "predicted_ids = torch.argmax(output, dim=-1)\n", "predicted_text = tokenizer.decode(predicted_ids[0])\n", "print(predicted_text)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 299, "referenced_widgets": [ "939572ca9719407491d2d25477d00e50", "c1fc909e44e243c398e726e6dfefbb51", "5af45b792e7e46bdb86f28362c67d42c", "5edda65a563948f888f489788b9723e4", "5b5ef5bf93a241cbba520130c1ef9d2e", "70162ba1dec6412fbc87ccfa395ce63b", "79e61ff994984a90859d022529ae6762", "295aa750d30c4feb870f92e049ed3393", "86b2754ddbb74ba3a66f983fd33d792c", "45df0640f8374a1b87199e6147e2376d", "a66a11d5d89148768a408b806209c313", "6b8e252b3718465aabd5567df89add03", "80088b42bfe145dd9b8e2e77a826c6ba", "9c19f509206b45dfb1d2db7cb6093aee", "89047eb90e9e471696b03e52e4bb6e9e", "c4000576cada4db8abea834390f3ce90", "1ce7c4dd6a5448ca9d341658766683c5", "9913f6c80ea54114882c2c004bb1f807", "0f5f1afc02024e6d95b4df67270ec665", "d9d1ea3e19ff4b3ab259bee52ac6a0c0", "7ec63a2facbc414e99a14c652004ec0e", "2fbaba7d3bda4c7c998e99c1bb2b12e4", "0d888e3c4df2490e85f17efde3c1f87d", "515c68eb4502432e9c3dc60bbdcb3177", "c90a4eb3b4b342b4931927674870b59f", "103a2320218d44b49966dd690ea19145", "558263641465434283f2c3e0fa349540", "b9f2d69348bb4749912b93d1814b14b4", "67db87b54c9f47dfb2035fd16cf040dc", "8e6552f2daa1439ab3e33f085766185e", "91ded56060cc4f7cba826c513da3c659", "8466c3160290421f9be8fdccff632fa4", "cf7624e3803d411f8ddafc4c47600607", "8a668da7106148f9bfd92f30ad31c96c", "f5725297275a46cb849b29927b247ecb", "61d980e7010d4e2f8a4c98a9f1c6d029", "930dedf47cf449c68469d173377de202", "86324c0f8873433a968c1643f10793db", "5ec8bde9287444738044505c276d6060", "d5915f9366e74dedad7c5ca7b31633a7", "fac637a5d0af4b99a2aa23a0edb80db0", "25668c4e8ab940caa33c96c1b93fd51a", "7125e1a8b4984b0bb4e0fb684cae1273", "d2d393fc5b8b47419436656e16ba852d", "192e38e65d6c4ae9862b7db5649c4f6a", "5646c5ea72d24839bfb797b43567ad36", "56b41558f9144632a988a310baa83bef", "d5cf899178364efe929f06cfd7dd0b12", "7729a57c44e94788acae0e6d89e5c8a3", "24294ce4bb734d3d811029e606bcc248", "8b27c85b7c124fb4bebd6358e2269687", "296f34ec6c9c447cbf59017761d0c2a8", "352c02e497bf4ede8e212ea183f9da58", "cab02c9de3ec41baa2619e412c12ca1b", "8487aff79bbb4931a87df906b5ff51c8" ] }, "id": "BTfCm07YuTtK", "outputId": "e959956f-a9aa-48b6-abd0-f91d37e9a3bc" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00