Delete transformer.py

transformer.py

@@ -1,346 +0,0 @@
-# Solving for residual std scaling issue
-import os
-import math
-import time
-from dataclasses import dataclass
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from tqdm import tqdm  # Import tqdm for progress bar
-import torch.quantization  # Import quantization module
-import torch.nn.utils.prune as prune
-import tiktoken
-
-
-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 print_num_parameters(self):
-        num_params = sum(p.numel() for p in self.parameters())
-        print(f"Number of model parameters: {num_params}")
-
-    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
-
-
-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)
-
-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, device=device)  # Move tokens to the correct device
-        print(f'loaded {len(self.tokens)} tokens')
-        print(f'1 epoch = {len(self.tokens)} 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
-    
-
-import os
-import time
-import torch
-
-# Initialize the model and data loader
-config = GPTConfig()
-model = GPT(config).to(device)  # Move model to the correct device
-
-# Print the model architecture and number of parameters
-print(model)
-model.print_num_parameters()
-
-train_loader = DataLoaderLite(B=4, T=1024)
-
-# Define the optimizer
-optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
-
-# Function to load the most recent checkpoint
-def load_latest_checkpoint(model):
-    checkpoint_file = 'checkpoint.pt'
-    if not os.path.exists(checkpoint_file):
-        return 0  # No checkpoint found, start from epoch 0
-
-    print(f'Loading checkpoint from {checkpoint_file}')
-    checkpoint = torch.load(checkpoint_file, map_location=device)  # Load checkpoint to the correct device
-    model.load_state_dict(checkpoint['model_state_dict'])
-    return checkpoint['epoch']
-
-# Load the latest checkpoint if available
-start_epoch = load_latest_checkpoint(model)
-
-# Training loop
-num_epochs = 100
-
-# Start time tracking
-start_time = time.time()
-
-for epoch in range(start_epoch, num_epochs):  # Start from the loaded epoch
-    epoch_loss = 0.0  # Initialize epoch loss
-    num_steps = 0  # Initialize step counter for the epoch
-    last_loss = None  # Variable to store the last loss
-
-    # Calculate total steps for the progress bar
-    total_steps = len(train_loader.tokens) // (train_loader.B * train_loader.T)
-
-    # Use tqdm to create a progress bar
-    with tqdm(total=total_steps, desc=f'Epoch {epoch + 1}/{num_epochs}') as pbar:
-        for step in range(total_steps):  # Iterate over the number of steps
-            x, y = train_loader.next_batch()
-            x, y = x.to(device), y.to(device)
-            optimizer.zero_grad()
-            logits, loss = model(x, y)
-            loss.backward()
-            optimizer.step()
-            
-            epoch_loss += loss.item()  # Accumulate loss
-            num_steps += 1  # Increment step counter
-            last_loss = loss.item()  # Store the last loss
-            pbar.update(1)  # Update progress bar
-
-            # Check if the loss is below the threshold
-            if last_loss < 0.099999:
-                print(f'Loss below threshold: {last_loss:.6f}')  # Print loss before breaking
-                break  # Exit the loop if the loss condition is met
-
-    # Print the loss at the end of the epoch
-    print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {last_loss:.6f}')
-
-    # Check if the loss condition was met to break out of the epoch loop
-    if last_loss < 0.099999:
-        print(f'Early stopping at epoch {epoch + 1} due to loss condition met.')
-        break  # Exit the epoch loop if the loss condition is met
-
-    # Checkpointing: Save the model and the current epoch after each epoch
-    checkpoint_path = 'checkpoint.pt'  # Save to a single checkpoint file
-    torch.save({
-        'epoch': epoch + 1,  # Save the current epoch number
-        'model_state_dict': model.state_dict(),  # Save the model state
-    }, checkpoint_path)
-
-# End time tracking
-end_time = time.time()
-training_duration = end_time - start_time
-
-# Convert training duration to minutes and seconds
-minutes = int(training_duration // 60)
-seconds = int(training_duration % 60)
-
-# Print the total training time in minute:second format
-print(f'Total training time: {minutes} minutes and {seconds} seconds')
-
-# After training your model, apply quantization and save it with compression
-def save_model_with_quantization(model, file_path):
-    # Switch model to evaluation mode
-    model.eval()
-    
-    # Apply dynamic quantization
-    quantized_model = torch.quantization.quantize_dynamic(
-        model,  # the model to be quantized
-        {nn.Linear},  # layers to quantize
-        dtype=torch.qint8  # quantization type
-    )
-    
-    # Save the quantized model with compression
-    torch.save(quantized_model.state_dict(), file_path, _use_new_zipfile_serialization=True)
-    print(f'Model saved to {file_path} with quantization and compression.')
-
-# Call this function after training your model
-save_model_with_quantization(model, 'trained_model_quantized.pt')