Delete model.py

model.py

@@ -1,168 +0,0 @@
-from transformers import PreTrainedModel
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from config import CustomAIConfig
-batch_size = 4
-block_size = 128
-max_iters = 10
-learning_rate = 3e-4
-eval_iters = 100
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-print(device)
-# n_embd = 384
-# n_head = 4
-# n_layer = 4
-dropout = 0.2
-# vocab_size=1000
-# model architecture
-class Head(nn.Module):
-    """ one head of self-attention """
-
-    def __init__(self, n_embd, 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, n_embd):
-        super().__init__()
-        self.heads = nn.ModuleList([Head(n_embd,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) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])
-        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,n_embd)
-        self.ffwd = FeedFoward(n_embd)
-        self.ln1 = nn.LayerNorm(n_embd)
-        self.ln2 = nn.LayerNorm(n_embd)
-
-
-    def forward(self, x):
-        y = self.sa(x)
-        x = self.ln1(x + y)
-        y = self.ffwd(x)
-        x = self.ln2(x + y)
-        return x
-
-class CustomAI(PreTrainedModel):
-    config_class = CustomAIConfig
-    def __init__(self, config):
-        super().__init__(config)
-        self.token_embedding_table = nn.Embedding(config.vocab_size, config.n_embd)
-        self.position_embedding_table = nn.Embedding(block_size, config.n_embd)
-        self.blocks = nn.Sequential(*[Block(config.n_embd, n_head=config.n_head) for _ in range(config.n_layer)])
-        self.ln_f = nn.LayerNorm(config.n_embd)  # final layer norm
-        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
-
-        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, index, targets=None):
-        B, T = index.shape
-
-
-        # idx and targets are both (B,T) tensor of integers
-        tok_emb = self.token_embedding_table(index) # (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, index, max_new_tokens):
-        # index is (B, T) array of indices in the current context
-        for _ in range(max_new_tokens):
-            # crop idx to the last block_size tokens
-            index_cond = index[:, -block_size:]
-            # get the predictions
-            logits, loss = self.forward(index_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
-            index_next = torch.multinomial(probs, num_samples=1) # (B, 1)
-            # append sampled index to the running sequence
-            index = torch.cat((index, index_next), dim=1) # (B, T+1)
-        return index
-config = CustomAIConfig(
-        vocab_size=1000,
-        n_embd=384,
-        n_head=4,
-        n_layer=4,
-        dropout=0.2,
-    # any other parameters you want to set
-)
-model = CustomAI(config)
-# model.load_state_dict(torch.load('/content/BharatAI.pth'))
-
-