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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from contextlib import nullcontext\n",
"from bigram_model import BigramLanguageModel\n",
"from tokenizer_utils import IntCharTokenizer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# !wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler\n",
"ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]\n",
"ctx = nullcontext() if device == 'cpu' else torch.amp.autocast(device_type=device, dtype=ptdtype)\n",
"scaler = torch.cuda.amp.GradScaler(enabled=(dtype == 'float16'))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from data_utils import *\n",
"model_args = dict(n_layer=n_layer, n_head=n_head, n_embd=n_embed, block_size=BLOCK_SIZE,\n",
" bias=False, vocab_size=None, dropout=dropout)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([128, 256, 65])\n",
"tensor(4.3690, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
]
}
],
"source": [
"from data_utils import *\n",
"xb, yb = get_random_batch('train')\n",
"xb = xb.to(device)\n",
"yb = yb.to(device)\n",
"\n",
"m = BigramLanguageModel(vocab_size=65, n_embed=n_embed, block_size=BLOCK_SIZE, num_heads=n_head, n_layers=n_layer).to(device)\n",
"logits, loss = m(xb, yb)\n",
"print(logits.shape)\n",
"print(loss)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def estimate_loss(model):\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_random_batch(split)\n",
" with ctx:\n",
" logits, loss = model(X, Y)\n",
" losses[k] = loss.item()\n",
" out[split] = losses.mean()\n",
" model.train()\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"char_tokenizer = load_int_char_tokenizer(load_text())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.788929 M parameters\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"step 0: train loss 4.3685, val loss 4.3640\n",
"step 500: train loss 1.9681, val loss 2.0837\n",
"step 1000: train loss 1.5377, val loss 1.7404\n",
"step 1500: train loss 1.3802, val loss 1.6101\n",
"step 2000: train loss 1.2855, val loss 1.5551\n",
"step 2500: train loss 1.2162, val loss 1.5157\n",
"step 3000: train loss 1.1617, val loss 1.5088\n",
"step 3500: train loss 1.1061, val loss 1.5088\n",
"step 4000: train loss 1.0555, val loss 1.5150\n",
"step 4500: train loss 1.0086, val loss 1.5385\n",
"step 4999: train loss 0.9583, val loss 1.5524\n"
]
}
],
"source": [
"print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')\n",
"\n",
"# create a PyTorch optimizer\n",
"optimizer = torch.optim.AdamW(m.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(m)\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_random_batch('train')\n",
"\n",
" # evaluate the loss\n",
" logits, loss = m(xb, yb)\n",
" optimizer.zero_grad(set_to_none=True)\n",
" loss.backward()\n",
" optimizer.step()\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"saving checkpoint to ./nano_gpt_ckpts\n"
]
}
],
"source": [
"checkpoint = {\n",
" 'model': m.state_dict(),\n",
" 'optimizer': optimizer.state_dict(),\n",
" 'model_args': model_args,\n",
" 'iter_num': max_iters,\n",
" 'best_val_loss': losses['val'],\n",
"\n",
"}\n",
"out_dir = \"./nano_gpt_ckpts\"\n",
"print(f\"saving checkpoint to {out_dir}\")\n",
"torch.save(checkpoint, os.path.join(out_dir, 'ckpt_5k_iters.pt'))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#m2 = BigramLanguageModel(vocab_size=65, n_embed=n_embed, block_size=BLOCK_SIZE, num_heads=n_head, n_layers=n_layer).to(device)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"GLOUCESTER: learn, like a nap. Prisoner will to my intents! with my brother! and this bloody makes off flows,--and haste tear'd your roe!--I should not be the other's.---I'ld do hear that be pupy with thear; sweet Montague,--thou as done not--So that they have nage must know,--never speak so many tears,--traightful ner-light,--with'd yet a ping tymp,--which time to stir; now still hurr'd,---water'd honour,--Pray's Coitlinius: the mountake's nobled daughter.' Sir, it is some thee on Rome is sin:--'proud him 'there;' none honest seen; forsweet must be pointed, hurls thee in men; a proud confines, foot, die, gin night, old Ratchard!--Go, good lord!--will'd you not piece, I dare not.' an't; swear by the dog, belike! mother!--How sir!-Spite! Jupiteous put o's!--God leave your lawful coward!'--for I'll dry down, you in death;'--near'---for very 'ven a day.---fa, by; 'twas his mother's disposed;--'I shall make no son,--hard him hear me,--do. Madam, or smother'd wife: and that you may part this denies.'--'--thrieks for Richmond dancerts, in free people's anointed,--O, hold: Curs, on a fiathful doom: every nurse, is I long now, never large.' quoth let return him; for an't plead the fie, his maids; he will not quarrel; 'twas this, but take within, as he learn, as and heat, it see; a gized evassages of season, imagish: yet, a very no other consulance, good den.--To fair cousin, stay! come, sir; and hath been, let it breather ring.' God; I am, trusper, I say: provided, pardone! a never lady; come in God. I'll fight with Montagues come. Why, 'twas bring you to be, if the pass off, and here, it dare, man cryield. Frow, your head A called with Gaunt; the cause. O, prettiest his pale thing, rust, and good. Thou adventure be more, Juliet, perishease: I'll take the queen, and his love.--give me note to de,--dyes help, Edward, and after Romeo!--Whence labour cann'd Warwick! was? whither? why hours! fairs! after was? stay come! your run? a happy kind!--O day, go be--hours, wrong!--ta w\n"
]
}
],
"source": [
"# generate from the model\n",
"context = torch.zeros((1, 1), dtype=torch.long, device=device)\n",
"#print(decode(m.generate(context, max_new_tokens=2000)[0].tolist()))\n",
"print(char_tokenizer.decode(m.generate(context, max_new_tokens=2000)[0].tolist()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m3 = BigramLanguageModel(vocab_size=65, n_embed=n_embed, block_size=BLOCK_SIZE, num_heads=n_head, n_layers=n_layer)\n",
"ckpt = torch.load(os.path.join(\"./nano_gpt_ckpts\", \"ckpt_5k_iters.pt\"))\n",
"m3.load_state_dict(ckpt['model'])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"But Dohor, aged by! At Antigonus. You see his court! For death; a talm every hand, here shall!--So,--O, I, title now point!--Who, this I sem blind--that tark;--come boy?---O pray, peace! May, two here, do not---that I troth:----to villain leave, where was the Gallent--if I look the house,--bold Jour---whether may I go,--Mine son,---as I amiled me pized,--or so fled; 'tis a famouse,--there littenants,--If an either lawful hant ther is gone.' Sicilence, if it wer done! I have twize its sourness. P\n"
]
}
],
"source": [
"context = torch.zeros((1, 1), dtype=torch.long)\n",
"print(char_tokenizer.decode(m3.generate(context, max_new_tokens=500)[0].tolist()))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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