Upload model and tokenizer

modeling_gpt.py

@@ -0,0 +1,200 @@
+# Model/model.py
+import torch
+import torch.nn as nn
+import inspect
+from huggingface_hub import PyTorchModelHubMixin
+
+# Define hyperparameters and constants
+BATCH_SIZE = 16
+BLOCK_SIZE = 1024
+MAX_ITERS = 5
+EVAL_INTERVAL = 500
+LEARNING_RATE = 6e-4
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+EVAL_ITERS = 200
+N_EMBD = 768
+N_HEAD = 12
+N_LAYER = 12
+DROPOUT = 0.2
+MODEL_PATH = "Naive_gpt\model_weights_llama"  # Where to save weights
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        assert N_EMBD % N_HEAD == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(N_EMBD, 3 * N_EMBD)
+        # output projection
+        self.c_proj = nn.Linear(N_EMBD, N_EMBD)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+        # regularization
+        self.n_head = N_HEAD
+        self.n_embd = N_EMBD
+
+    def forward(self, x):
+        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs
+        # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C //
+                   self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C //
+                   self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C //
+                   self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+        y = nn.functional.scaled_dot_product_attention(
+            q, k, v, is_causal=True)  # flash attention
+        # re-assemble all head outputs side by side
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        # output projection
+        y = self.c_proj(y)
+        return y
+    
+
+class FeedFoward(nn.Module):   #yeh MLP hai karpathy wala -> Feed forward hai sebastian wala
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(N_EMBD, 4 * N_EMBD)
+        self.gelu = nn.GELU(approximate='tanh')
+        self.c_proj = nn.Linear(4 * N_EMBD, N_EMBD)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        return x
+    """ a simple linear layer followed by a non-linearity """
+
+class Block(nn.Module):
+    """ Transformer block: communication followed by computation """
+
+    def __init__(self, n_embd, n_head):
+        super().__init__()
+        head_size = N_EMBD // n_head
+        self.sa = CausalSelfAttention()
+        self.ffwd = FeedFoward()
+        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, PyTorchModelHubMixin):
+
+    def __init__(self, vocab_size=20000, block_size=1024, n_embd=768, n_head=12, n_layer=12):
+        super().__init__()
+        print("This is vocab size:", vocab_size)
+        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)
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+
+        self.token_embedding_table.weight = self.lm_head.weight
+
+        self.apply(self._init_weights)
+        self.config = {"BLOCK_SIZE": block_size, "N_EMBD": n_embd, "N_HEAD":n_head, "N_LAYER": n_layer}
+        
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+                std = 0.02
+                if hasattr(module, 'NANOGPT_SCALE_INIT'):
+                    std *= (2 * N_LAYER) ** -0.5
+                torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+                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
+        assert T <= BLOCK_SIZE, f"Cannot forward sequence of length {T}, block size is only {BLOCK_SIZE}"
+
+
+        tok_emb = self.token_embedding_table(idx)
+        pos_emb = self.position_embedding_table(torch.arange(0, T, dtype=torch.long, device=idx.device))
+        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:
+            loss = nn.functional.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens, temperature=1.0):
+        """
+        Generate tokens using the language model.
+        Args:
+            idx: Input token indices
+            max_new_tokens: Number of tokens to generate
+            temperature: Controls randomness in generation
+                        - temperature > 1.0 increases randomness
+                        - temperature < 1.0 decreases randomness
+                        - temperature = 0 makes it deterministic (always picks highest probability)
+        """
+        for _ in range(max_new_tokens):
+            # Truncate the sequence to the last BLOCK_SIZE tokens
+            idx_cond = idx[:, -BLOCK_SIZE:]
+            # Get logits from the model
+            logits, _ = self(idx_cond)
+            # Focus only on the last time step
+            logits = logits[:, -1, :]
+
+            if temperature == 0.0:
+                # For temperature = 0, simply take the argmax
+                idx_next = torch.argmax(logits, dim=-1, keepdim=True)
+            else:
+                # Apply temperature scaling
+                logits = logits / temperature
+                # Convert to probabilities
+                probs = torch.softmax(logits, dim=-1)
+                # Sample from the distribution
+                idx_next = torch.multinomial(probs, num_samples=1)
+
+            # Append the new token to the sequence
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+    def save(self, path=MODEL_PATH):
+        torch.save(self.state_dict(), path)
+
+    def load(self, path=MODEL_PATH):
+    # Load the state dict
+        state_dict = torch.load(path)["model"]
+
+        new_state_dict = {}
+        for key, value in state_dict.items():
+            new_key = key.replace('_orig_mod.', '')  # Remove 'orig_mod.' prefix
+            new_state_dict[new_key] = value
+
+        self.load_state_dict(new_state_dict)
+
+
+    def configure_optimizers(self, weight_decay=0.1, learning_rate=LEARNING_RATE, device=DEVICE):
+        param_dict = {pn: p for pn, p in self.named_parameters()}
+        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
+
+        decay_parameters = [p for n, p in param_dict.items() if p.dim() >= 2]
+        nodecay_parameters = [p for n, p in param_dict.items() if p.dim() < 2]
+        optim_groups = [
+            {"params": decay_parameters, "weight_decay": weight_decay},
+            {"params": nodecay_parameters, "weight_decay": 0.0},
+        ]
+        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
+        use_fused = fused_available and device == "cuda"
+        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused = use_fused)
+        return optimizer
+MODEL_PATH = "Naive_gpt\model_weights_llama"  # Where to save weights

tokenizer_config.json

@@ -1,4 +1,4 @@
 {
     "type": "llama",
-    "vocab_size": 0
+    "vocab_size": 20000
 }