first commit to test the transformer in spaces

app.py

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+import gradio as gr
+import torch
+import torch.nn.functional as F
+import tiktoken
+from huggingface_hub import hf_hub_download
+from transformer-basic import GPT, GPTConfig  # Import your model class
+
+# Load the model from Hugging Face Hub
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+def load_model_from_hf():
+    # Replace with your Hugging Face model ID (username/model-name)
+    model_id = "satyanayak/transformer-basic"
+    checkpoint_path = hf_hub_download(repo_id=model_id, filename="trained_model.pt")
+    
+    checkpoint = torch.load(checkpoint_path, map_location=device)
+    config = checkpoint['config']
+    model = GPT(config)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.to(device)
+    model.eval()
+    return model
+
+model = load_model_from_hf()
+
+def generate_text(prompt, max_length=100, num_samples=1, temperature=0.8):
+    enc = tiktoken.get_encoding('gpt2')
+    tokens = enc.encode(prompt)
+    tokens = torch.tensor(tokens, dtype=torch.long)
+    tokens = tokens.unsqueeze(0).repeat(num_samples, 1)
+    tokens = tokens.to(device)
+    
+    with torch.no_grad():
+        for _ in range(max_length):
+            if tokens.size(1) >= 1024:  # GPT context length
+                break
+                
+            logits = model(tokens)[0]
+            logits = logits[:, -1, :] / temperature
+            probs = F.softmax(logits, dim=-1)
+            
+            # Top-k sampling
+            topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+            ix = torch.multinomial(topk_probs, 1)
+            next_token = torch.gather(topk_indices, -1, ix)
+            
+            tokens = torch.cat((tokens, next_token), dim=1)
+            
+            # Check for end of text token
+            if next_token.item() == enc.encode('<|endoftext|>')[0]:
+                break
+    
+    generated_texts = []
+    for i in range(num_samples):
+        text = enc.decode(tokens[i].tolist())
+        generated_texts.append(text)
+    
+    return '\n\n---\n\n'.join(generated_texts)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Prompt", value="We are accounted poor citizens, the"),
+        gr.Slider(minimum=10, maximum=200, value=100, step=1, label="Max Length"),
+        gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Number of Samples"),
+        gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="Shakespeare-style Text Generator",
+    description="Enter a prompt to generate Shakespeare-style text continuation"
+)
+
+if __name__ == "__main__":
+    iface.launch() 

requirements.txt

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+torch
+gradio
+tiktoken
+transformers 
+huggingface_hub 

transformer-basic.py

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+# Solving for residual std scaling issue
+import os
+import math
+import time
+import inspect
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.c_proj.NANGPT_SCALE_INIT = 1
+        # regularization
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)).view(1, 1, config.block_size, config.block_size))
+
+    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)
+
+        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
+        att = F.softmax(att, dim=-1)
+        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+        # output projection
+        y = self.c_proj(y)
+        return y
+
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd)
+        self.gelu    = nn.GELU(approximate='tanh')
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.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
+
+class Block(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+@dataclass
+class GPTConfig:
+    block_size: int = 1024 # max sequence length
+    vocab_size: int = 50257 # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
+    n_layer: int = 12 # number of layers
+    n_head: int = 12 # number of heads
+    n_embd: int = 768 # embedding dimension
+
+
+class GPT(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(config.vocab_size, config.n_embd),
+            wpe = nn.Embedding(config.block_size, config.n_embd),
+            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f = nn.LayerNorm(config.n_embd),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # weight sharing
+        self.transformer.wte.weight = self.lm_head.weight
+
+        # weight initialization
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            if hasattr(module, 'NANGPT_SCALE_INIT'):
+                std *= (2 * self.config.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):
+        # idx is of shape (B, T)
+        B, T = idx.size()
+        assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
+        # forward the token and posisition embeddings
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
+        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd)
+        tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd)
+        x = tok_emb + pos_emb
+        # forward the blocks of the transformer
+        for block in self.transformer.h:
+            x = block(x)
+        # forward the final layernorm and the classifier
+        x = self.transformer.ln_f(x)
+        logits = self.lm_head(x) # (B, T, vocab_size)
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+        return logits, loss
+
+    @classmethod
+    def from_pretrained(cls, model_type):
+        """Loads pretrained GPT-2 model weights from huggingface"""
+        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
+        from transformers import GPT2LMHeadModel
+        print("loading weights from pretrained gpt: %s" % model_type)
+
+        # n_layer, n_head and n_embd are determined from model_type
+        config_args = {
+            'gpt2':         dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
+            'gpt2-medium':  dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
+            'gpt2-large':   dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
+            'gpt2-xl':      dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
+        }[model_type]
+        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
+        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
+        # create a from-scratch initialized minGPT model
+        config = GPTConfig(**config_args)
+        model = GPT(config)
+        sd = model.state_dict()
+        sd_keys = sd.keys()
+        sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param
+
+        # init a huggingface/transformers model
+        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
+        sd_hf = model_hf.state_dict()
+
+        # copy while ensuring all of the parameters are aligned and match in names and shapes
+        sd_keys_hf = sd_hf.keys()
+        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
+        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
+        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
+        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
+        # this means that we have to transpose these weights when we import them
+        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
+        for k in sd_keys_hf:
+            if any(k.endswith(w) for w in transposed):
+                # special treatment for the Conv1D weights we need to transpose
+                assert sd_hf[k].shape[::-1] == sd[k].shape
+                with torch.no_grad():
+                    sd[k].copy_(sd_hf[k].t())
+            else:
+                # vanilla copy over the other parameters
+                assert sd_hf[k].shape == sd[k].shape
+                with torch.no_grad():
+                    sd[k].copy_(sd_hf[k])
+
+        return model
+
+# model = GPT.from_pretrained('gpt2')
+
+device = 'cpu'
+if torch.cuda.is_available():
+    device = 'cuda'
+elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+    device = "mps"
+print(f"using device: {device}")
+
+# SEED
+torch.manual_seed(1337)
+if torch.cuda.is_available():
+    torch.cuda.manual_seed(1337)
+
+# STOP
+num_return_sequences = 5
+max_length = 30
+
+
+
+import tiktoken
+
+class DataLoaderLite:
+    def __init__(self, B, T):
+        self.B = B
+        self.T = T
+
+        # at init load tokens from disk and store them in memory
+        with open('input.txt', 'r') as f:
+            text = f.read()
+        enc = tiktoken.get_encoding('gpt2') 
+        tokens = enc.encode(text)
+        self.tokens = torch.tensor(tokens)
+        print(f'loaded {len(self.tokens)} tokens')
+        print(f'1 epoch = {len(self.tokens) // (B * T)} batches')
+
+        # state
+        self.current_position = 0
+    
+    def next_batch(self):
+        B, T = self.B, self.T
+        buf = self.tokens[self.current_position: self.current_position + B * T + 1]
+        x = (buf[:-1]).view(B, T) # inputs
+        y = (buf[1:]).view(B, T) # targets
+        # advance the position in the tensor
+        self.current_position += B*T
+        # if loading the next batch would be out of bounds, reset
+        if self.current_position + (B * T + 1) > len(self.tokens):
+            self.current_position = 0
+        return x, y
+
+
+model = GPT(GPTConfig())
+model.to(device)
+
+# Increase batch size slightly but keep it manageable
+train_loader = DataLoaderLite(B=8, T=64)
+
+# Calculate total steps for one cycle
+total_steps = 10000
+print(f"Training for {total_steps} steps")
+
+# Initialize optimizer with more conservative parameters
+optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0.1, betas=(0.9, 0.95))
+
+# Use OneCycleLR scheduler
+scheduler = torch.optim.lr_scheduler.OneCycleLR(
+    optimizer,
+    max_lr=3e-4,
+    total_steps=total_steps,
+    pct_start=0.1,  # Warm up for 10% of steps
+    anneal_strategy='cos',
+    cycle_momentum=False,
+    div_factor=25.0,  # Initial lr = max_lr/25
+    final_div_factor=10000.0,  # Min lr = initial_lr/10000
+)
+
+# Training loop
+best_loss = float('inf')
+step = 0
+losses = []  # Keep track of losses for monitoring
+last_time = time.time()
+interval = 10  # Print every 10 steps
+
+while step < total_steps and best_loss > 0.099999:
+    x, y = train_loader.next_batch()
+    x, y = x.to(device), y.to(device)
+    
+    optimizer.zero_grad()
+    logits, loss = model(x, y)
+    loss.backward()
+    
+    # Gradient clipping
+    torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)  # Reduced from 1.0
+    
+    optimizer.step()
+    scheduler.step()
+    
+    # Update best loss
+    if loss.item() < best_loss:
+        best_loss = loss.item()
+    
+    losses.append(loss.item())
+    
+    # Print progress
+    if step % interval == 0:
+        current_time = time.time()
+        time_per_batch = (current_time - last_time) / interval if step > 0 else 0
+        last_time = current_time
+        
+        # Calculate average loss over last 100 steps for stability
+        avg_loss = sum(losses[-100:]) / min(len(losses), 100)
+        
+        print(f'step {step}, '
+              f'loss: {loss.item():.4f}, '
+              f'avg_loss: {avg_loss:.4f}, '
+              f'best_loss: {best_loss:.4f}, '
+              f'lr: {scheduler.get_last_lr()[0]:.2e}, '
+              f'time/batch: {time_per_batch:.3f}s')
+    
+    step += 1
+
+print(f'Final loss: {loss.item():.6f}')
+print(f'Best loss: {best_loss:.6f}')
+print(f'Average of last 100 losses: {sum(losses[-100:]) / min(len(losses), 100):.6f}')
+
+# Save the trained model
+save_path = 'trained_model.pt'
+torch.save({
+    'model_state_dict': model.state_dict(),
+    'optimizer_state_dict': optimizer.state_dict(),
+    'scheduler_state_dict': scheduler.state_dict(),
+    'best_loss': best_loss,
+    'config': model.config,
+}, save_path)
+print(f"Model saved to {save_path}")
+
+# Generation code
+enc = tiktoken.get_encoding('gpt2')
+prompt = "We are accounted poor citizens, the"
+tokens = enc.encode(prompt)
+tokens = torch.tensor(tokens, dtype=torch.long)
+tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1)
+x = tokens.to(device)
+
+# Rest of generation code remains same...