test.py

from ast import Mult
import torch
import torch.nn as nn
from torch.nn import functional as F
import random
block_size = 512

device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)
eval_iters = 100
batch_size = 32
max_iters = 20000000
learning_rate = 1e-4
n_embed = 384
n_layer = 12
n_head = 4
dropout = 0.2
#!wget https://raw.githubusercontent.com/BrightTheBackpack/llm/main/training.txt?token=GHSAT0AAAAAACURFFPTK5BTMULLUAQMY5CSZXDYTWA
with open('all_poems.txt', 'r', encoding = "utf8") as file:
    text = file.read()
chars = sorted(list(set(text)))
vocab_size = len(chars)
#decode and encode text into numbers stoi(string to int)
stoi = { ch:i for i, ch in enumerate(chars)}
itos = { i:ch for i, ch in enumerate(chars)}
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: ''.join([itos[i] for i in l])
data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9*len(data))
train_data = data[:n]
val_data = data[n:]
train_data[:block_size+1]

def get_batch(split):
  data = train_data if split == 'train' else val_data
  ix = torch.randint(len(data) - block_size, (batch_size,))
  x = torch.stack([data[i:i+block_size] for i in ix])
  y = torch.stack([data[i+1:i+block_size+1] for i in ix])
  x,y = x.to(device), y.to(device)
  return x, y

@torch.no_grad()
def estimate_loss():
  out = {}
  model.eval()
  for split in ['train' , 'val']:
    losses = torch.zeros(eval_iters)
    for k in range(eval_iters):
      x,y = get_batch(split)
      logits, loss = model(x,y)
      losses[k] = loss.item()
    out[split] = losses.mean()
  model.train()
  return out
class Head(nn.Module):
  def __init__(self, head_size):
    super().__init__()
    self.key = nn.Linear(n_embed,head_size, bias=False)
    self.query = nn.Linear(n_embed,head_size, bias=False)
    self.value = nn.Linear(n_embed,head_size, bias=False)
    self.dropout = nn.Dropout(dropout)
    self.register_buffer("tril", torch.tril(torch.ones(block_size,block_size)))
  def forward(self,x):
    B,T,C = x.shape
    k = self.key(x)
    q = self.query(x)
    wei = q @ k.transpose(-2,-1) * C**-0.5
    wei = wei.masked_fill(self.tril[:T, :T] ==0, float('-inf'))
    wei = F.softmax(wei, dim=-1)
    wei = self.dropout(wei)
    v = self.value(x)
    out = wei @ v
    return out

class MultiHeadAttention(nn.Module):
  def __init__(self, num_heads, head_size):
    super().__init__()
    self.heads = nn.ModuleList([Head(head_size) for  _ in range(num_heads)])
    self.proj = nn.Linear(n_embed, n_embed)
    self.dropout = nn.Dropout(dropout)
  def forward(self, x):
    out = torch.cat([h(x) for h in self.heads], dim = -1)
    out = self.dropout(self.proj(out))
    return out

class FeedFoward(nn.Module):
  def __init__(self, n_embed):
    super().__init__()
    self.net = nn.Sequential(
      nn.Linear(n_embed, 4*  n_embed),
      nn.ReLU(),
      nn.Linear(4 * n_embed, n_embed),
      nn.Dropout(dropout),

    )
  def forward(self, x):
    return self.net(x)
class Block(nn.Module):
  def __init__(self, n_embed, n_head):
    super().__init__()
    head_size = n_embed // n_head
    self.sa = MultiHeadAttention(n_head, head_size)

    self.ffwd = FeedFoward(n_embed)
    self.ln1 = nn.LayerNorm(n_embed)
    self.ln2 = nn.LayerNorm(n_embed)
  def forward(self,x):
    x = x + self.sa(self.ln1(x))
    x = x+   self.ffwd(self.ln2(x))
    return x
class BigramLanguageModel(nn.Module):
  def __init__(self):
    super().__init__()
    self.token_embedding_table = nn.Embedding(vocab_size, n_embed)
    self.position_embedding_table = nn.Embedding(block_size, n_embed)
    self.blocks = nn.Sequential(*[Block(n_embed, n_head=n_head) for _ in range(n_layer)])

    self.ln_f = nn.LayerNorm(n_embed)
    self.sa_head = MultiHeadAttention(4, n_embed//4)#MultiHeadAttention(4, n_embed//4)
    self.ffwd = FeedFoward(n_embed)
    self.lm_head = nn.Linear(n_embed, vocab_size)
  def forward(self, idx, targets=None):
    B,T = idx.shape
    tok_emb = self.token_embedding_table(idx) #B,T,C
    pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))[None,:,:] #1,T,C
    x = tok_emb + pos_emb
    x = self.blocks(x)
    x = self.ln_f(x)
    logits = self.lm_head(x)
    if targets is None:
      loss = None
    else:
      B,T,C = logits.shape
      logits = logits.view(B*T, C)
      targets = targets.view(B*T)
      loss = F.cross_entropy(logits,targets)
    return logits, loss
  def generate(self, idx, max_new_tokens):
    for _ in range(max_new_tokens):
      idx_cond = idx[:,-block_size:]
      logits, loss = self(idx_cond)
      logits = logits[:, -1, :]
      probs = F.softmax(logits, dim=-1)
      idx_next = torch.multinomial(probs, num_samples=1)
      idx = torch.cat((idx, idx_next), dim=1)
    return idx

device = "cuda" if torch.cuda.is_available() else "cpu"
model = BigramLanguageModel().to(device)
model.load_state_dict(torch.load('model.pth', map_location=device))

# Set the model to evaluation mode
model.eval()

# Example of how to generate text using the model
def generate_text(start_word, max_new_tokens=300):
    encoded_word = encode(start_word)
    idx = torch.tensor(encoded_word, dtype=torch.long, device=device).view(1, -1)
    generated_idx = model.generate(idx, max_new_tokens)
    return decode(generated_idx[0].tolist())

# Use the model to generate text
start_word = " "  # Replace with your chosen start word
generated_text = generate_text(start_word)
print(generated_text)