pytorch_fine_tuning_code/F9_chat.py

import torch
import numpy as np

# Configuration (MUST match training configuration)
CONFIG = {
    "FILE_PATH": 'dataset.txt',
    "SEQ_LENGTH": 32,
    "EMBEDDING_DIM": 64,
    "HIDDEN_DIM": 64,
    "NUM_LAYERS": 1,
    "DROPOUT": 0.2,
    "MODEL_SAVE_PATH": "char_lm_advanced.pth",
    "TEMPERATURE": 0.7,
    "TOP_K": 5,
    "TOP_P": 0.95
}

# Load vocabulary
with open(CONFIG["FILE_PATH"], 'r', encoding='utf-8') as f:
    text = f.read()

chars = sorted(list(set(text)))
char_to_idx = {ch: i for i, ch in enumerate(chars)}
idx_to_char = {i: ch for i, ch in enumerate(chars)}
vocab_size = len(chars)

# Model definition (must match training architecture)
class CharLM(torch.nn.Module):
    def __init__(self):
        super(CharLM, self).__init__()
        self.embedding = torch.nn.Embedding(vocab_size, CONFIG["EMBEDDING_DIM"])
        self.lstm = torch.nn.LSTM(
            CONFIG["EMBEDDING_DIM"], 
            CONFIG["HIDDEN_DIM"],
            num_layers=CONFIG["NUM_LAYERS"],
            dropout=CONFIG["DROPOUT"] if CONFIG["NUM_LAYERS"] > 1 else 0,
            batch_first=True
        )
        self.dropout = torch.nn.Dropout(CONFIG["DROPOUT"])
        self.fc = torch.nn.Linear(CONFIG["HIDDEN_DIM"], vocab_size)
        
    def forward(self, x, hidden=None):
        x = self.embedding(x)
        out, hidden = self.lstm(x, hidden)
        out = self.dropout(out)
        out = self.fc(out)
        return out, hidden

# Load trained model
model = CharLM()
model.load_state_dict(torch.load(CONFIG["MODEL_SAVE_PATH"]))
model.eval()

def generate_text(model, start_str, length=200, temperature=CONFIG["TEMPERATURE"], 
                 top_k=CONFIG["TOP_K"], top_p=CONFIG["TOP_P"]):
    """
    Generate text with temperature scaling, top-k, and nucleus (top-p) sampling
    """
    model.eval()
    chars = list(start_str)
    input_seq = torch.tensor([char_to_idx[ch] for ch in chars]).unsqueeze(0)
    hidden = None
    
    with torch.no_grad():
        for _ in range(length):
            outputs, hidden = model(input_seq, hidden)
            logits = outputs[0, -1] / temperature
            
            # Apply top-k filtering
            if top_k > 0:
                top_vals, top_idx = torch.topk(logits, top_k)
                logits[logits < top_vals[-1]] = -float('Inf')
            
            # Apply nucleus (top-p) filtering
            if top_p > 0:
                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
                cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
                sorted_indices_to_remove = cumulative_probs > top_p
                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
                sorted_indices_to_remove[..., 0] = 0
                indices_to_remove = sorted_indices[sorted_indices_to_remove]
                logits[indices_to_remove] = -float('Inf')

            probs = torch.softmax(logits, dim=-1)
            next_char = torch.multinomial(probs, num_samples=1).item()
            chars.append(idx_to_char[next_char])
            input_seq = torch.tensor([[next_char]])
    
    return ''.join(chars)

# Interactive loop
while True:
    try:
        print("\n" + "="*50)
        prompt = input("Enter your starting text (or 'exit' to quit):\n> ")
        
        if prompt.lower() == 'exit':
            print("Goodbye!")
            break
            
        # Filter invalid characters
        valid_prompt = [c for c in prompt if c in char_to_idx]
        if not valid_prompt:
            print("Please use characters from the training data.")
            continue
            
        # Get generation parameters
        length = int(input("Output length (50-500 recommended): ")) or 200
        temp = float(input(f"Temperature [{CONFIG['TEMPERATURE']}]: ") or CONFIG["TEMPERATURE"])
        top_k = int(input(f"Top-K [{CONFIG['TOP_K']}]: ") or CONFIG["TOP_K"])
        top_p = float(input(f"Top-P [{CONFIG['TOP_P']}]: ") or CONFIG["TOP_P"])
        
        # Generate and display
        print("\nGenerating...")
        generated = generate_text(
            model,
            ''.join(valid_prompt),
            length=length,
            temperature=temp,
            top_k=top_k,
            top_p=top_p
        )
        print("\nGenerated Text:")
        print(generated)
        print("="*50)
        
    except ValueError:
        print("Invalid input! Please enter valid numbers for parameters.")
    except KeyboardInterrupt:
        print("\nExiting...")
        break