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 import tiktoken import numpy as np from huggingface_hub import HfApi, Repository import gradio as gr from tqdm import tqdm 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 generate(self, idx, max_new_tokens): # idx is (B, T) array of indices in the current context for _ in range(max_new_tokens): # crop idx to the last block_size tokens idx_cond = idx[:, -self.config.block_size:] # get the predictions logits, loss = self(idx_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 idx_next = torch.multinomial(probs, num_samples=1) # (B, 1) # append sampled index to the running sequence idx = torch.cat((idx, idx_next), dim=1) # (B, T+1) return idx 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 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('/content/drive/My Drive/ERAV3/Assign12/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) train_loader = DataLoaderLite(B = 4, T = 32) # Calculate number of epochs total_tokens = len(train_loader.tokens) batches_per_epoch = total_tokens // (4 * 32) total_epochs = 5000 / batches_per_epoch print(f'\nTraining for approximately {total_epochs:.2f} epochs') print(f'Total tokens: {total_tokens:,}') print(f'Batches per epoch: {batches_per_epoch}') print(f'Total steps: 5,000\n') # Continue with training loop optimizer = torch.optim.AdamW(model.parameters(), lr = 3e-4) # Calculate total epochs and steps per epoch total_steps = 5000 steps_per_epoch = batches_per_epoch num_epochs = total_steps // steps_per_epoch remaining_steps = total_steps % steps_per_epoch print(f"Training for {num_epochs} full epochs plus {remaining_steps} steps") print(f"Steps per epoch: {steps_per_epoch}\n") step = 0 for epoch in range(num_epochs + 1): # Determine steps for this epoch if epoch == num_epochs: if remaining_steps == 0: break current_steps = remaining_steps else: current_steps = steps_per_epoch print(f"\nEpoch {epoch+1}/{num_epochs + (1 if remaining_steps > 0 else 0)}") epoch_loss = 0 # Use tqdm for progress bar pbar = tqdm(range(current_steps), desc=f'Training', leave=True, ncols=100) for i in pbar: 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() step += 1 # Update progress bar description with current loss pbar.set_description(f'Loss: {loss.item():.4f}') # Print epoch summary avg_epoch_loss = epoch_loss / current_steps print(f'\nEpoch {epoch+1} completed. Average Loss: {avg_epoch_loss:.4f}') print(f'Total steps completed: {step}/{total_steps}') # For even smaller file size, quantize the model to 8-bit model_save_path = '/content/drive/My Drive/ERAV3/Assign12/gpt_model_quantized.pt' try: # Quantize weights to 8-bit state_dict = model.state_dict() quantized_dict = {} for key, param in state_dict.items(): if param.dtype == torch.float32 or param.dtype == torch.float16: # Quantize to 8-bit param_np = param.cpu().numpy() scale = np.max(np.abs(param_np)) / 127 quantized = np.round(param_np / scale).astype(np.int8) quantized_dict[key] = { 'data': quantized, 'scale': scale } else: quantized_dict[key] = param # Save quantized weights torch.save(quantized_dict, model_save_path) print(f'\nQuantized model saved successfully to {model_save_path}') except Exception as e: print(f'\nError saving model: {e}') # To load the quantized model: # def dequantize_model(model, quantized_dict): # state_dict = {} # for key, value in quantized_dict.items(): # if isinstance(value, dict): # # Dequantize # state_dict[key] = torch.tensor( # value['data'].astype(np.float32) * value['scale'] # ) # else: # state_dict[key] = value # model.load_state_dict(state_dict) # return model context = torch.zeros((1, 1), dtype=torch.long, device=device) enc = tiktoken.get_encoding('gpt2') print(enc.decode(model.generate(context, max_new_tokens=500)[0].tolist()))