Update app.py

app.py

@@ -14,233 +14,233 @@ NUM_ROUNDS = 3
 
 
 
-########################TinyLLM####################################
+# ########################TinyLLM####################################
+
+# import torch
+# import torch.nn as nn
+# from torch.nn import functional as F
+
+# # hyperparameters
+# batch_size = 64 # how many independent sequences will we process in parallel?
+# block_size = 256 # what is the maximum context length for predictions?
+# max_iters = 5000
+# eval_interval = 500
+# learning_rate = 3e-4
+# device = 'cuda' if torch.cuda.is_available() else 'cpu'
+# eval_iters = 200
+# n_embd = 384
+# n_head = 6
+# n_layer = 6
+# dropout = 0.2
+# # ------------
+
+# torch.manual_seed(1337)
+
+# # wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
+# with open('input.txt', 'r', encoding='utf-8') as f:
+#     text = f.read()
+
+# # here are all the unique characters that occur in this text
+# chars = sorted(list(set(text)))
+# vocab_size = len(chars)
+# # create a mapping from characters to integers
+# 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] # encoder: take a string, output a list of integers
+# decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+
+# # Train and test splits
+# data = torch.tensor(encode(text), dtype=torch.long)
+# n = int(0.9*len(data)) # first 90% will be train, rest val
+# train_data = data[:n]
+# val_data = data[n:]
+
+# # data loading
+# def get_batch(split):
+#     # generate a small batch of data of inputs x and targets y
+#     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):
+#     """ one head of self-attention """
+
+#     def __init__(self, head_size):
+#         super().__init__()
+#         self.key = nn.Linear(n_embd, head_size, bias=False)
+#         self.query = nn.Linear(n_embd, head_size, bias=False)
+#         self.value = nn.Linear(n_embd, head_size, bias=False)
+#         self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+
+#         self.dropout = nn.Dropout(dropout)
+
+#     def forward(self, x):
+#         # input of size (batch, time-step, channels)
+#         # output of size (batch, time-step, head size)
+#         B,T,C = x.shape
+#         k = self.key(x)   # (B,T,hs)
+#         q = self.query(x) # (B,T,hs)
+#         # compute attention scores ("affinities")
+#         wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
+#         wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
+#         wei = F.softmax(wei, dim=-1) # (B, T, T)
+#         wei = self.dropout(wei)
+#         # perform the weighted aggregation of the values
+#         v = self.value(x) # (B,T,hs)
+#         out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
+#         return out
+
+# class MultiHeadAttention(nn.Module):
+#     """ multiple heads of self-attention in parallel """
+
+#     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(head_size * num_heads, n_embd)
+#         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):
+#     """ a simple linear layer followed by a non-linearity """
+
+#     def __init__(self, n_embd):
+#         super().__init__()
+#         self.net = nn.Sequential(
+#             nn.Linear(n_embd, 4 * n_embd),
+#             nn.ReLU(),
+#             nn.Linear(4 * n_embd, n_embd),
+#             nn.Dropout(dropout),
+#         )
+
+#     def forward(self, x):
+#         return self.net(x)
+
+# class Block(nn.Module):
+#     """ Transformer block: communication followed by computation """
+
+#     def __init__(self, n_embd, n_head):
+#         # n_embd: embedding dimension, n_head: the number of heads we'd like
+#         super().__init__()
+#         head_size = n_embd // n_head
+#         self.sa = MultiHeadAttention(n_head, head_size)
+#         self.ffwd = FeedFoward(n_embd)
+#         self.ln1 = nn.LayerNorm(n_embd)
+#         self.ln2 = nn.LayerNorm(n_embd)
+
+#     def forward(self, x):
+#         x = x + self.sa(self.ln1(x))
+#         x = x + self.ffwd(self.ln2(x))
+#         return x
+
+# class GPTLanguageModel(nn.Module):
+
+#     def __init__(self):
+#         super().__init__()
+#         # each token directly reads off the logits for the next token from a lookup table
+#         self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
+#         self.position_embedding_table = nn.Embedding(block_size, n_embd)
+#         self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
+#         self.ln_f = nn.LayerNorm(n_embd) # final layer norm
+#         self.lm_head = nn.Linear(n_embd, vocab_size)
+
+#         # better init, not covered in the original GPT video, but important, will cover in followup video
+#         self.apply(self._init_weights)
+
+#     def _init_weights(self, module):
+#         if isinstance(module, nn.Linear):
+#             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+#             if module.bias is not None:
+#                 torch.nn.init.zeros_(module.bias)
+#         elif isinstance(module, nn.Embedding):
+#             torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+#     def forward(self, idx, targets=None):
+#         B, T = idx.shape
+
+#         # idx and targets are both (B,T) tensor of integers
+#         tok_emb = self.token_embedding_table(idx) # (B,T,C)
+#         pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
+#         x = tok_emb + pos_emb # (B,T,C)
+#         x = self.blocks(x) # (B,T,C)
+#         x = self.ln_f(x) # (B,T,C)
+#         logits = self.lm_head(x) # (B,T,vocab_size)
+
+#         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):
+#         # idx is (B, T) array of indices in the current context
+#         for _ in range(max_new_tokens):
+#             # crop idx to the last block_size tokens
+#             idx_cond = idx[:, -block_size:]
+#             # get the predictions
+#             logits, loss = self(idx_cond)
+#             # focus only on the last time step
+#             logits = logits[:, -1, :] # becomes (B, C)
+#             # apply softmax to get probabilities
+#             probs = F.softmax(logits, dim=-1) # (B, C)
+#             # sample from the distribution
+#             idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
+#             # append sampled index to the running sequence
+#             idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
+#         return idx
+
+# model = GPTLanguageModel()
+# m = model.to(device)
+# # print the number of parameters in the model
+# print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')
+
+# # create a PyTorch optimizer
+# optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-# hyperparameters
-batch_size = 64 # how many independent sequences will we process in parallel?
-block_size = 256 # what is the maximum context length for predictions?
-max_iters = 5000
-eval_interval = 500
-learning_rate = 3e-4
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-eval_iters = 200
-n_embd = 384
-n_head = 6
-n_layer = 6
-dropout = 0.2
-# ------------
-
-torch.manual_seed(1337)
-
-# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
-with open('input.txt', 'r', encoding='utf-8') as f:
-    text = f.read()
-
-# here are all the unique characters that occur in this text
-chars = sorted(list(set(text)))
-vocab_size = len(chars)
-# create a mapping from characters to integers
-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] # encoder: take a string, output a list of integers
-decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
-
-# Train and test splits
-data = torch.tensor(encode(text), dtype=torch.long)
-n = int(0.9*len(data)) # first 90% will be train, rest val
-train_data = data[:n]
-val_data = data[n:]
-
-# data loading
-def get_batch(split):
-    # generate a small batch of data of inputs x and targets y
-    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):
-    """ one head of self-attention """
-
-    def __init__(self, head_size):
-        super().__init__()
-        self.key = nn.Linear(n_embd, head_size, bias=False)
-        self.query = nn.Linear(n_embd, head_size, bias=False)
-        self.value = nn.Linear(n_embd, head_size, bias=False)
-        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
-
-        self.dropout = nn.Dropout(dropout)
-
-    def forward(self, x):
-        # input of size (batch, time-step, channels)
-        # output of size (batch, time-step, head size)
-        B,T,C = x.shape
-        k = self.key(x)   # (B,T,hs)
-        q = self.query(x) # (B,T,hs)
-        # compute attention scores ("affinities")
-        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
-        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
-        wei = F.softmax(wei, dim=-1) # (B, T, T)
-        wei = self.dropout(wei)
-        # perform the weighted aggregation of the values
-        v = self.value(x) # (B,T,hs)
-        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
-        return out
-
-class MultiHeadAttention(nn.Module):
-    """ multiple heads of self-attention in parallel """
-
-    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(head_size * num_heads, n_embd)
-        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):
-    """ a simple linear layer followed by a non-linearity """
-
-    def __init__(self, n_embd):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(n_embd, 4 * n_embd),
-            nn.ReLU(),
-            nn.Linear(4 * n_embd, n_embd),
-            nn.Dropout(dropout),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-class Block(nn.Module):
-    """ Transformer block: communication followed by computation """
-
-    def __init__(self, n_embd, n_head):
-        # n_embd: embedding dimension, n_head: the number of heads we'd like
-        super().__init__()
-        head_size = n_embd // n_head
-        self.sa = MultiHeadAttention(n_head, head_size)
-        self.ffwd = FeedFoward(n_embd)
-        self.ln1 = nn.LayerNorm(n_embd)
-        self.ln2 = nn.LayerNorm(n_embd)
-
-    def forward(self, x):
-        x = x + self.sa(self.ln1(x))
-        x = x + self.ffwd(self.ln2(x))
-        return x
-
-class GPTLanguageModel(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-        # each token directly reads off the logits for the next token from a lookup table
-        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
-        self.position_embedding_table = nn.Embedding(block_size, n_embd)
-        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
-        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
-        self.lm_head = nn.Linear(n_embd, vocab_size)
-
-        # better init, not covered in the original GPT video, but important, will cover in followup video
-        self.apply(self._init_weights)
-
-    def _init_weights(self, module):
-        if isinstance(module, nn.Linear):
-            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
-            if module.bias is not None:
-                torch.nn.init.zeros_(module.bias)
-        elif isinstance(module, nn.Embedding):
-            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
-
-    def forward(self, idx, targets=None):
-        B, T = idx.shape
-
-        # idx and targets are both (B,T) tensor of integers
-        tok_emb = self.token_embedding_table(idx) # (B,T,C)
-        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
-        x = tok_emb + pos_emb # (B,T,C)
-        x = self.blocks(x) # (B,T,C)
-        x = self.ln_f(x) # (B,T,C)
-        logits = self.lm_head(x) # (B,T,vocab_size)
-
-        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):
-        # idx is (B, T) array of indices in the current context
-        for _ in range(max_new_tokens):
-            # crop idx to the last block_size tokens
-            idx_cond = idx[:, -block_size:]
-            # get the predictions
-            logits, loss = self(idx_cond)
-            # focus only on the last time step
-            logits = logits[:, -1, :] # becomes (B, C)
-            # apply softmax to get probabilities
-            probs = F.softmax(logits, dim=-1) # (B, C)
-            # sample from the distribution
-            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
-            # append sampled index to the running sequence
-            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
-        return idx
-
-model = GPTLanguageModel()
-m = model.to(device)
-# print the number of parameters in the model
-print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')
-
-# create a PyTorch optimizer
-optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
-
-for iter in range(max_iters):
+# for iter in range(max_iters):
 
-    # every once in a while evaluate the loss on train and val sets
-    if iter % eval_interval == 0 or iter == max_iters - 1:
-        losses = estimate_loss()
-        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+#     # every once in a while evaluate the loss on train and val sets
+#     if iter % eval_interval == 0 or iter == max_iters - 1:
+#         losses = estimate_loss()
+#         print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
 
-    # sample a batch of data
-    xb, yb = get_batch('train')
+#     # sample a batch of data
+#     xb, yb = get_batch('train')
 
-    # evaluate the loss
-    logits, loss = model(xb, yb)
-    optimizer.zero_grad(set_to_none=True)
-    loss.backward()
-    optimizer.step()
+#     # evaluate the loss
+#     logits, loss = model(xb, yb)
+#     optimizer.zero_grad(set_to_none=True)
+#     loss.backward()
+#     optimizer.step()
 
-# generate from the model
-context = torch.zeros((1, 1), dtype=torch.long, device=device)
-print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))
-#open('more.txt', 'w').write(decode(m.generate(context, max_new_tokens=10000)[0].tolist()))
+# # generate from the model
+# context = torch.zeros((1, 1), dtype=torch.long, device=device)
+# print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))
+# #open('more.txt', 'w').write(decode(m.generate(context, max_new_tokens=10000)[0].tolist()))
 
 
 
@@ -252,7 +252,7 @@ print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))
 
 
 
-########################TinyLLM##################################
+# ########################TinyLLM##################################
 
 def load_data(dataset_name):
     raw_datasets = load_dataset(dataset_name)