211 lines
6.3 KiB
Python
211 lines
6.3 KiB
Python
# flake8: noqa: E203
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import os
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import random
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import re
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import torch
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import torch.optim as optim
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from torch.utils.tensorboard import SummaryWriter
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from gpt_model import encode, decode, load_model
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Hyperparameters
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batch_size = 32 # Reduced batch size for gradient accumulation
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accumulation_steps = 4 # Gradient accumulation steps
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block_size = 256
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max_iters = 100000 # Increased iterations
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learning_rate = 3e-5 # Adjust learning rate
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eval_iters = 100
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dropout = 0.4 # Increased dropout to prevent overfitting
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patience = 20000 # Increased patience for early stopping
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weight_decay = 0.01 # Add weight decay for regularization
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# Load the vocabulary and encoded data
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with open("vocab.txt", "r", encoding="utf-8") as f:
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text = f.read()
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chars = sorted(list(set(text)))
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required_chars = " \n\r\t"
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for char in required_chars:
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if char not in chars:
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chars.append(char)
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special_token = "<unk>"
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if special_token not in chars:
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chars.append(special_token)
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vocab_size = len(chars)
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string_to_int = {ch: i for i, ch in enumerate(chars)}
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int_to_string = {i: ch for i, ch in enumerate(chars)}
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def clean_text(text):
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text = re.sub(r"[^a-zA-Z0-9\s.,;!?\'\"]+", "", text)
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text = re.sub(r"\s+", " ", text)
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text = text.strip()
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return text
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def load_and_clean_data(file_path):
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with open(file_path, "r", encoding="utf-8") as f:
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text = f.read()
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cleaned_text = clean_text(text)
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return cleaned_text
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train_data = load_and_clean_data("train_split_cleaned.txt")
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val_data = load_and_clean_data("eval_split_cleaned.txt")
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train_data = torch.tensor(encode(train_data, string_to_int), dtype=torch.long)
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val_data = torch.tensor(encode(val_data, string_to_int), dtype=torch.long)
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def get_random_chunk(data, chunk_size):
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start = random.randint(0, len(data) - chunk_size - 1)
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chunk = data[start : start + chunk_size]
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return chunk
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def get_batch(data, block_size, batch_size):
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chunk_size = block_size * (batch_size + 1)
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chunk = get_random_chunk(data, chunk_size)
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x = chunk[: block_size * batch_size].view(batch_size, block_size)
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y = chunk[1 : block_size * batch_size + 1].view(batch_size, block_size)
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x, y = x.to(device), y.to(device)
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return x, y
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def load_or_initialize_model(vocab_size):
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model = load_model(vocab_size)
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if os.path.exists("phoebe_model.pt"):
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model.load_state_dict(torch.load("phoebe_model.pt"))
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print("Model loaded from phoebe_model.pt")
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else:
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print("Initialized a new model")
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return model
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model = load_or_initialize_model(vocab_size).to(device)
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@torch.no_grad()
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def estimate_loss():
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out = {}
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model.eval()
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for split in ["train", "val"]:
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data = train_data if split == "train" else val_data
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losses = torch.zeros(eval_iters)
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for k in range(eval_iters):
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x, y = get_batch(data, block_size, batch_size)
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logits, loss = model(x, y)
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losses[k] = loss.item()
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out[split] = losses.mean().item()
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model.train()
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return out
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def train_model():
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optimizer = optim.AdamW(
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model.parameters(), lr=learning_rate, weight_decay=weight_decay
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)
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steps_per_epoch = len(train_data) // (batch_size * block_size)
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epochs = max_iters // steps_per_epoch
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scheduler = optim.lr_scheduler.OneCycleLR(
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optimizer,
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max_lr=learning_rate * 10,
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steps_per_epoch=steps_per_epoch,
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epochs=epochs,
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)
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writer = SummaryWriter(log_dir="runs/phoebe_training")
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best_val_loss = float("inf")
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patience_counter = 0
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for iter in range(max_iters):
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if iter % eval_iters == 0:
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losses = estimate_loss()
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print(
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f"step {iter}: train loss {losses['train']:.3f}, "
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f"val loss {losses['val']:.3f}"
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)
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writer.add_scalar("Loss/train", losses["train"], iter)
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writer.add_scalar("Loss/val", losses["val"], iter)
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if losses["val"] < best_val_loss:
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best_val_loss = losses["val"]
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patience_counter = 0
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torch.save(model.state_dict(), "phoebe_model.pt")
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print("Model Saved!")
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else:
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patience_counter += eval_iters
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if patience_counter >= patience:
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print("Early stopping triggered.")
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break
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xb, yb = get_batch(train_data, block_size, batch_size)
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logits, loss = model(xb, yb)
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loss = loss / accumulation_steps
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loss.backward()
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if (iter + 1) % accumulation_steps == 0:
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optimizer.step()
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optimizer.zero_grad(set_to_none=True)
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scheduler.step()
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if patience_counter < patience:
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print("Training completed without early stopping.")
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print(f"Final loss: {loss.item()}")
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writer.close()
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def check_input_chars(s, string_to_int):
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unknown_chars = [c for c in s if c not in string_to_int]
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if unknown_chars:
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print(f"Unknown characters in input: {unknown_chars}")
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return unknown_chars
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def process_message(message):
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print(f"Processing message: '{message}'")
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if not message.strip():
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print("Message is empty or invalid.")
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return "Message is empty or invalid."
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unknown_chars = check_input_chars(message, string_to_int)
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if unknown_chars:
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print(f"Message contains unknown characters: {unknown_chars}")
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return f"Message contains unknown characters: {unknown_chars}"
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encoded_text = torch.tensor(
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[encode(message, string_to_int)], dtype=torch.long
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).to(device)
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print(f"Encoded text shape: {encoded_text.shape}")
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if encoded_text.size(1) == 0:
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print("Message could not be processed.")
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return "Message could not be processed."
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with torch.no_grad():
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generated_tokens = model.generate(
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encoded_text, max_new_tokens=50, temperature=0.7
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)
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generated_tokens = generated_tokens[0, len(encoded_text[0]) :]
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decoded_response = decode(generated_tokens.tolist(), int_to_string)
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print(f"Generated response: '{decoded_response}'")
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if decoded_response.startswith(message):
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decoded_response = decoded_response[len(message) :].strip()
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print(f"Final response: '{decoded_response}'")
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return decoded_response
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if __name__ == "__main__":
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train_model()
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