First Commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# PyTorch Lightning
+lightning_logs/
+
+# VS Code
+.vscode/
+
+# MacOS
+.DS_Store
+
+# Thumbnails
+._*
+
+# Temporary files
+*.tmp
+*.temp
+*.swp
+*.swo 
+
+# extras
+*.xlsx

GPT-Shakespeare.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1d3090cfad6aa084fd0ab066772d0e4b2cd2d3169f0f043fc6a2e85b64502cf
+size 548147736

app.py

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+import os
+import torch
+import traceback
+import tiktoken
+import gradio as gr
+import torch.nn.functional as F
+from transformer import GPTConfig, GPT
+
+
+def load_model(model_path):
+    # model
+    config = GPTConfig()
+    model = GPT(config)
+
+    ckpt = torch.load(os.path.join(model_path), map_location="cpu")
+    model.load_state_dict(ckpt["model_state_dict"])
+    model.to(device)
+    model.eval()
+
+    return model
+
+
+def generate_text(text, max_length=64, num_return_sequences=2):
+
+    tokenizer = tiktoken.get_encoding('gpt2')
+    x = tokenizer.encode(text)
+    x = torch.tensor(x, dtype=torch.long)    
+    x = x.unsqueeze(0)
+    x = x.repeat(num_return_sequences, 1)
+    x = x.to(device)
+
+    for _ in range(max_length):
+
+        with torch.no_grad():
+            logits, _ = model(x)
+        # take the logits at the last position
+        logits = logits[:, -1, :] # (B, vocab_size)
+        # get the probabilities
+        probs = F.softmax(logits, dim=-1)
+        # do top-k sampling of 50 (huggingface pipeline default)
+        # topk_probs here becomes (5, 50), topk_indices is (5, 50)
+        topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+        # select a token from the top-k probabilities
+        # note: multinomial does not demand the input to sum to 1
+        ix = torch.multinomial(topk_probs, 1) # (B, 1)
+        # gather the corresponding indices
+        xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
+        # append to the sequence
+        x = torch.cat((x, xcol), dim=1)
+    
+    generated_text = []
+    for i in range(num_return_sequences):
+        tokens = x[i, :max_length].tolist()
+        decoded = tokenizer.decode(tokens)
+        generated_text.append("\n>>>\n" + decoded + "\n\n")
+
+    return "".join(generated_text)
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = load_model(R"GPT-Shakespeare.pth")
+
+# Define the Gradio interface
+demo = gr.Interface(
+    fn=generate_text,
+    inputs= [
+        gr.Textbox(
+            label="Input Text", 
+            placeholder="Enter the text in style of Coriolanus",
+            lines=5
+            ),
+        gr.Slider(minimum=1, 
+                  maximum=128, 
+                  step=1, 
+                  value=20, 
+                  label="Max Sequence Length"),
+        gr.Slider(minimum=1, 
+                  maximum=5, 
+                  step=1, 
+                  value=2, 
+                  label="Number of Sequences to Return")
+    ],
+    outputs=gr.Textbox(lines=5, 
+                       placeholder="Generated text will be shown here", 
+                       label="Generated Text"),
+    title= """<h1 style='text-align: center;'>  Text Generation using GPT   \
+        <a href='https://github.com/KD1994/session-12-Transformer-from-scratch-pt2' target='_blank'> \
+            <i class='fab fa-github' style='font-size: 24px;'></i></a> \
+            </h1> \
+                <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">""",
+    description = "<p style='text-align: center'> Decoder only transformer trained on \"Coriolanus\" by William Shakespeare </p>",
+    examples = [
+        ["My noble Coriolanus, temper thy rage. These men hold"], 
+        ["Wisdom, say’st thou? Counsel, and truth? Nay, Menenius, they are"],
+        ["What speaks this man of war and violence?"],
+        ["Enough, Coriolanus! Thy words grow wild. What wouldst thou have?"]
+    ]
+)
+
+# Add error handling to launch
+try:
+    demo.launch()
+except Exception as e:
+    print(f"Error launching interface: {str(e)}")
+    print(traceback.format_exc()) 

requirements.txt

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+gradio
+torch
+tiktoken

transformer.py

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+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dataclasses import dataclass
+
+
+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
+
+
+@dataclass
+class Config:
+    vocab_size: int = 50257
+    max_seq_len: int = 2048
+    dim: int = 768
+    num_layers: int = 12
+    num_heads: int = 12
+    dropout: float = 0.1
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.n_head = config.num_heads
+        self.n_embd = config.dim
+        
+        # Linear projections for Q, K, V
+        self.c_attn = nn.Linear(config.dim, 3 * config.dim) # [n_embd, 3 * n_embd]
+        self.c_proj = nn.Linear(config.dim, config.dim) # [n_embd, n_embd]
+        
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        B, T, C = x.size() # [B, T, n_embd]
+        
+        # Linear projection and split into Q, K, V
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2) # [B, T, n_embd] each
+        
+        # Reshape for multi-head attention
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head]
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head]
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # [B, n_head, T, n_embd/n_head]
+        
+        # Attention scores
+        att = (q @ k.transpose(-2, -1)) * (1.0 / (k.size(-1) ** 0.5)) # [B, n_head, T, T]
+        att = F.softmax(att, dim=-1) # [B, n_head, T, T]
+        att = self.attn_dropout(att) # [B, n_head, T, T]
+        
+        # Weighted sum of values
+        y = att @ v # [B, n_head, T, n_embd/n_head]
+        
+        # Reshape and project
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # [B, T, n_embd]
+        y = self.c_proj(y) # [B, T, n_embd]
+        y = self.resid_dropout(y) # [B, T, n_embd]
+        
+        return y
+
+class FeedForward(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = nn.Linear(config.dim, 4 * config.dim) # [n_embd, 4 * n_embd]
+        self.c_proj = nn.Linear(4 * config.dim, config.dim) # [4 * n_embd, n_embd]
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x) # [B, T, 4 * n_embd]
+        x = F.gelu(x) # [B, T, 4 * n_embd]
+        x = self.c_proj(x) # [B, T, n_embd]
+        x = self.dropout(x) # [B, T, n_embd]
+        return x
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.dim) # [n_embd]
+        self.attn = MultiHeadAttention(config)
+        self.ln_2 = nn.LayerNorm(config.dim) # [n_embd]
+        self.mlp = FeedForward(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x)) # [B, T, n_embd]
+        x = x + self.mlp(self.ln_2(x)) # [B, T, n_embd]
+        return x
+
+class DecoderOnlyTransformer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.wte = nn.Embedding(config.vocab_size, config.dim) # [vocab_size, n_embd]
+        self.wpe = nn.Embedding(config.max_seq_len, config.dim) # [max_seq_len, n_embd]
+        self.drop = nn.Dropout(config.dropout)
+        self.blocks = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_layers)])
+        self.ln_f = nn.LayerNorm(config.dim) # [n_embd]
+        self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False) # [n_embd, vocab_size]
+        
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def forward(self, idx):
+        B, T = idx.size() # [B, T]
+        
+        # Positional embeddings
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device).unsqueeze(0) # [1, T]
+        
+        # Token and position embeddings
+        tok_emb = self.wte(idx) # [B, T, n_embd]
+        pos_emb = self.wpe(pos) # [1, T, n_embd]
+        
+        # Combine embeddings and apply dropout
+        x = self.drop(tok_emb + pos_emb) # [B, T, n_embd]
+        
+        # Transformer blocks
+        for block in self.blocks:
+            x = block(x) # [B, T, n_embd]
+        
+        # Final layer norm and linear projection
+        x = self.ln_f(x) # [B, T, n_embd]
+        logits = self.lm_head(x) # [B, T, vocab_size]
+        
+        return logits
+
+# if __name__ == '__main__':
+#     config = Config()
+#     model = DecoderOnlyTransformer(config)
+    
+#     # Example usage
+#     batch_size = 4
+#     seq_len = 128
+    
+#     # Generate random input
+#     input_ids = torch.randint(0, config.vocab_size, (batch_size, seq_len))
+    
+#     # Forward pass
+#     logits = model(input_ids)
+    
+#     print("Input shape:", input_ids.shape)
+#     print("Output shape:", logits.shape)