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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "158eaa47",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"from torch.nn import functional as F\n",
"import math, time, os\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import tiktoken\n",
"# from torch.cuda.amp import autocast, GradScaler\n",
"from torch.amp.autocast_mode import autocast\n",
"from torch.amp.grad_scaler import GradScaler"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "97d9467e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/software/Documents/.rianstuff/chatbot/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Her campaign emailed a fundraising pitch Tuesday evening warning of the dangers of a Trump presidency and of complacency among Democrats.\n",
"{'text': \"Canonical, keeper of the Ubuntu Linux distribution, is a small company with big friends. The latest example: Dell, IBM and Intel each are taking new steps with Ubuntu. Here's the scoop.\"}\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"# dataset = load_dataset(\"wikimedia/wikipedia\", \"20231101.en\")\n",
"dataset = load_dataset(\"Bingsu/openwebtext_20p\")\n",
"# This gives you cleaned, plain text articles1\n",
"print(dataset['train'][100]['text'][:500]) # Print the first 500 characters of the first article\n",
"print(dataset['train'][600000])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "81b98c54",
"metadata": {},
"outputs": [],
"source": [
"# class TextDataset(Dataset):\n",
"# def __init__(self, hf_dataset, tokenizer, block_size):\n",
"# self.dataset = hf_dataset\n",
"# self.tokenizer = tokenizer\n",
"# self.block_size = block_size\n",
"\n",
"# def __len__(self):\n",
"# return len(self.dataset['train'])\n",
"\n",
"# # def __getitem__(self, idx):\n",
"# # tokens = self.tokenizer.encode(self.dataset['train'][idx]['text'])\n",
"# # if len(tokens) < self.block_size + 1:\n",
"# # tokens = F.pad(torch.tensor(tokens), (0, self.block_size + 1 - len(tokens)), value=0)\n",
"# # else:\n",
"# # tokens = torch.tensor(tokens[: self.block_size + 1])\n",
"# # x = tokens[: self.block_size]\n",
"# # y = tokens[1 : self.block_size + 1]\n",
"# # return x.long(), y.long()\n",
"# def __getitem__(self, idx):\n",
"# # choose a random index instead of using the passed idx\n",
"# rand_idx = torch.randint(0, len(self.dataset['train']), (1,)).item()\n",
"# tokens = self.tokenizer.encode(self.dataset['train'][rand_idx]['text'])\n",
"\n",
"# if len(tokens) < self.block_size + 1:\n",
"# tokens = F.pad(torch.tensor(tokens), (0, self.block_size + 1 - len(tokens)), value=0)\n",
"# else:\n",
"# tokens = torch.tensor(tokens[: self.block_size + 1])\n",
"\n",
"# x = tokens[: self.block_size]\n",
"# y = tokens[1 : self.block_size + 1]\n",
"# return x.long(), y.long()\n",
"# ... existing code ...\n",
"\n",
"class TextDataset(Dataset):\n",
" def __init__(self, hf_dataset, tokenizer, block_size):\n",
" self.dataset = hf_dataset\n",
" self.tokenizer = tokenizer\n",
" self.block_size = block_size\n",
"\n",
" def __len__(self):\n",
" return len(self.dataset['train'])\n",
"\n",
" def __getitem__(self, idx):\n",
" # Start with a random index sample\n",
" rand_idx = torch.randint(0, len(self.dataset['train']), (1,)).item()\n",
" text = self.dataset['train'][rand_idx]['text']\n",
" tokens = self.tokenizer.encode(text)\n",
"\n",
" # Keep appending more samples if too short\n",
" while len(tokens) < self.block_size + 1:\n",
" next_idx = torch.randint(0, len(self.dataset['train']), (1,)).item()\n",
" next_text = self.dataset['train'][next_idx]['text']\n",
" tokens.extend(self.tokenizer.encode(\" \" + next_text))\n",
" # Prevent runaway growth\n",
" if len(tokens) > self.block_size * 2:\n",
" break\n",
"\n",
" # Truncate to block_size + 1\n",
" tokens = torch.tensor(tokens[: self.block_size + 1])\n",
"\n",
" x = tokens[: self.block_size]\n",
" y = tokens[1 : self.block_size + 1]\n",
" return x.long(), y.long()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "599aa05a",
"metadata": {},
"outputs": [],
"source": [
"#hyperparameters\n",
"train_model =True\n",
"block_size = 256\n",
"n_layers = 8\n",
"n_heads = 8\n",
"dropout_p = 0.1\n",
"batch_size =8\n",
"learning_rate = 3e-4\n",
"n_embedding = 512\n",
"max_iters = 5000\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a69561e9",
"metadata": {},
"outputs": [],
"source": [
"tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
"\n",
"train_dataset = TextDataset(dataset, tokenizer, block_size=block_size)\n",
"train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=16)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ea5598ea",
"metadata": {},
"outputs": [],
"source": [
"class GPTModel(nn.Module):\n",
" def __init__(self, vocab_size, n_embedding, n_layers, n_heads, dropout_p, block_size):\n",
" super(GPTModel, self).__init__()\n",
" self.token_embedding = nn.Embedding(vocab_size, n_embedding)\n",
" self.position_embedding = nn.Embedding(block_size, n_embedding)\n",
" self.layers = nn.ModuleList([\n",
" nn.TransformerEncoderLayer(d_model=n_embedding, nhead=n_heads, dropout=dropout_p)\n",
" for _ in range(n_layers)\n",
" ])\n",
" self.ln_f = nn.LayerNorm(n_embedding)\n",
" self.head = nn.Linear(n_embedding, vocab_size)\n",
" self.dropout = nn.Dropout(dropout_p)\n",
" self.block_size = block_size\n",
"\n",
" def forward(self, x):\n",
" bsz, seq_len = x.size()\n",
" positions = torch.arange(0, seq_len, device=x.device).unsqueeze(0).expand(bsz, seq_len)\n",
" x = self.token_embedding(x) + self.position_embedding(positions)\n",
" x = self.dropout(x)\n",
"\n",
" for layer in self.layers:\n",
" x = layer(x)\n",
"\n",
" x = self.ln_f(x)\n",
" logits = self.head(x)\n",
" return logits"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6a1344ab",
"metadata": {},
"outputs": [],
"source": [
"# define objects\n",
"vocab_size = tokenizer.n_vocab\n",
"\n",
"model = GPTModel(vocab_size, n_embedding, n_layers, n_heads, dropout_p, block_size).to(device)\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
"loss_fn = nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a0982489",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/software/Documents/.rianstuff/chatbot/.venv/lib/python3.12/site-packages/torch/__init__.py:1617: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)\n",
" _C._set_float32_matmul_precision(precision)\n",
"Training: 100%|ββββββββββββββββββββββββββββββββββββ| 5000/5000 [06:27<00:00, 12.89it/s, loss=7.7995]\n"
]
}
],
"source": [
"\n",
"from tqdm import tqdm\n",
"\n",
"# training loop\n",
"torch.set_float32_matmul_precision('high')\n",
"scaler = GradScaler(device)\n",
"if train_model:\n",
" compiled_model = torch.compile(model)\n",
"\n",
" pbar = tqdm(range(max_iters), desc=\"Training\", ncols=100)\n",
" data_iter = iter(train_dataloader)\n",
"\n",
" for count in pbar:\n",
" xb, yb = next(data_iter)\n",
" # try:\n",
" # if(count%100==0):\n",
" # print(f\"Iteration {count}\")\n",
" # xb, yb = next(data_iter)\n",
" # print(f\"Batch shape: {xb.shape}, {yb.shape}\")\n",
" # print('y decoded: ', tokenizer.decode(yb[0].tolist()))\n",
" # print('y not decoded: ', yb[0].tolist())\n",
" # print('x decoded: ', tokenizer.decode(xb[0].tolist()))\n",
" # print('x not decoded: ', xb[0].tolist())\n",
" \n",
" # except StopIteration:\n",
" # break # dataloader exhausted before max_iters\n",
" \n",
" xb, yb = xb.to(device), yb.to(device)\n",
" # logits = compiled_model(xb)\n",
" # loss = loss_fn(logits.view(-1, vocab_size), yb.view(-1))\n",
"\n",
" # optimizer.zero_grad()\n",
" # loss.backward()\n",
" # optimizer.step()\n",
" with autocast(device, dtype=torch.float16):\n",
" logits = compiled_model(xb)\n",
" loss = loss_fn(logits.view(-1, vocab_size), yb.view(-1))\n",
"\n",
" # backward pass with gradient scaling\n",
" optimizer.zero_grad()\n",
" scaler.scale(loss).backward()\n",
" scaler.step(optimizer)\n",
" scaler.update()\n",
"\n",
" # update bar text dynamically\n",
" pbar.set_postfix({\"loss\": f\"{loss.item():.4f}\"})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6eb95580",
"metadata": {},
"outputs": [],
"source": [
"if train_model:\n",
" torch.save(model.state_dict(), \"checkpoints/gpt_model-1.pth\")\n",
"else:\n",
" model.load_state_dict(torch.load(\"checkpoints/gpt_model-1.pth\"))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "4371725d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model has 76.864593 million parameters.\n",
"this new company does οΏ½ week film the 5 theοΏ½ana be 2002 of there to that realWell runs suchοΏ½ to found, inex their a but just might saidοΏ½, later to? vision candidate resultd agon if give continue anti information Beast find beer the I over\n"
]
}
],
"source": [
"@torch.no_grad()\n",
"def generate_text(model, tokenizer, prompt, max_new_tokens, block_size, device):\n",
" model.eval()\n",
" # Encode the prompt text into token IDs\n",
" tokens = torch.tensor(tokenizer.encode(prompt), dtype=torch.long).unsqueeze(0).to(device)\n",
"\n",
" for _ in range(max_new_tokens):\n",
" # Only keep the last block_size tokens for context\n",
" input_tokens = tokens[:, -block_size:]\n",
"\n",
" # Get logits and take the last tokenβs distribution\n",
" logits = model(input_tokens)\n",
" logits = logits[:, -1, :] # (batch=1, vocab)\n",
" probs = F.softmax(logits, dim=-1)\n",
"\n",
" # Sample from the distribution\n",
" next_token = torch.multinomial(probs, num_samples=1)\n",
" tokens = torch.cat((tokens, next_token), dim=1)\n",
"\n",
" # Decode back into text\n",
" output_text = tokenizer.decode(tokens[0].tolist())\n",
" return output_text\n",
" \n",
"# print model parameters\n",
"print (f\"Model has {sum(p.numel() for p in model.parameters())/1000000} million parameters.\")\n",
"prompt = \"this new company does \"\n",
"print(generate_text(model, tokenizer, prompt, max_new_tokens=50, block_size=block_size, device=device))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56e9eb22",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "chatbot",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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