cimai/old/spin_glass_gpu.py

import jax
import jax.numpy as jnp
from functools import partial
from load_tinystories import (
    tokenize_with_punctuation, 
    load_encodings, 
    BiDict
)
from tqdm import tqdm

@partial(jax.jit, static_argnums=(3, 4))
def calculate_weights(sequences, M, N):
    """
    Calculate weight matrix using JAX for GPU acceleration.
    """
    # Convert sequences to JAX array
    sequences_jax = jnp.array(sequences)
    
    # Calculate weights using matrix multiplication
    W = jnp.sum(jnp.matmul(sequences_jax, sequences_jax.transpose(0, 2, 1)), axis=0)
    
    # Normalize weights
    W = W / len(sequences)
    return W

@partial(jax.jit, static_argnums=(3, 4, 5))
def calculate_energies(complete_sequences, W, temperature):
    """
    Calculate energies for all possible completions using JAX.
    """
    # Calculate energies using matrix operations
    energies = -0.5 * jnp.diagonal(
        jnp.matmul(
            jnp.matmul(complete_sequences, W),
            complete_sequences.transpose(0, 2, 1)
        )
    ).reshape(-1)
    
    # Normalize energies
    energies = energies - jnp.min(energies)
    energies = energies / (jnp.max(energies) + 1e-10)
    
    # Calculate probabilities
    probabilities = jnp.exp(-energies/temperature)
    probabilities = probabilities / jnp.sum(probabilities)
    
    return energies, probabilities

def get_word_sequences_gpu(encoded_stories, M=100, N=12):
    """
    Get sequences of M consecutive words, optimized for GPU.
    """
    sequences = []
    
    for story in tqdm(encoded_stories, desc="Generating sequences"):
        if len(story) >= M:
            for i in range(len(story) - M + 1):
                word_group = story[i:i + M]
                bits = []
                for word in word_group:
                    bits.extend([int(bit) for bit in word])
                vector = jnp.array(bits).reshape(M * N, 1)
                sequences.append(vector)
    
    return jnp.array(sequences)

def retrieve_sequences_gpu(sequences, partial_sequence, vocab, M=100, N=12, temperature=1.0):
    """
    GPU-accelerated version of sequence retrieval using JAX.
    """
    # Convert partial sequence to vector
    partial_vec = jnp.array([int(bit) for bit in partial_sequence]).reshape(-1, 1)
    
    # Get all possible words
    possible_words = list(vocab.values())
    
    # Calculate weight matrix
    W = calculate_weights(sequences, M, N)
    
    # Create complete sequences for all possible words
    complete_sequences = []
    for word in possible_words:
        complete_sequence = partial_sequence + word
        if len(complete_sequence) == M*N:
            complete_vec = jnp.array([int(bit) for bit in complete_sequence]).reshape(M * N, 1)
            complete_sequences.append(complete_vec)
    
    complete_sequences = jnp.array(complete_sequences)
    
    # Calculate energies and probabilities
    energies, probabilities = calculate_energies(complete_sequences, W, temperature)
    
    # Select word based on probabilities
    selected_idx = jax.random.choice(
        jax.random.PRNGKey(0),
        len(possible_words),
        p=probabilities
    )
    
    best_word = possible_words[selected_idx]
    min_energy = float(energies[selected_idx])
    
    # Find corresponding word
    for word, vector in vocab.items():
        if vector == best_word:
            return word, best_word, min_energy

def predict_sequence_gpu(initial_sequence, vocab, sequences, D=10, M=100, N=12, temperature=1.0):
    """
    GPU-accelerated version of sequence prediction.
    """
    current_tokens = initial_sequence.copy()
    predictions = []
    energies = []
    
    for _ in tqdm(range(D), desc="Predicting words"):
        partial_sequence = ""
        for token in current_tokens:
            partial_sequence += vocab[token]
        
        predicted_word, _, energy = retrieve_sequences_gpu(
            sequences,
            partial_sequence,
            vocab,
            M=M,
            N=N,
            temperature=temperature
        )
        
        predictions.append(predicted_word)
        energies.append(energy)
        
        current_tokens = current_tokens[1:] + [predicted_word]
    
    return predictions, energies

def main():
    # Load saved encodings
    vocab, encoded_stories, original_stories = load_encodings()
    if vocab is None:
        print("No saved encodings found. Please run load_tinystories.py first.")
        return
    
    # Constants
    M = 100
    N = 13
    D = 10
    temperature = 1.0
    
    print("Loading training sequences...")
    sequences = get_word_sequences_gpu(encoded_stories=encoded_stories, M=M, N=N)
    print(f"Loaded {len(sequences)} training sequences")
    
    # Get sentence from user
    print("\nEnter your story:")
    sentence = input("Enter a sentence (at least 99 words): ")
    initial_tokens = tokenize_with_punctuation(sentence)
    
    if len(initial_tokens) < M-1:
        print(f"Sentence too short. Got {len(initial_tokens)} tokens, need {M-1}.")
        return
    
    # Predict next words
    print("\nPredicting next words...")
    predicted_words, energies = predict_sequence_gpu(
        initial_tokens[:M-1],
        vocab,
        sequences,
        D=D,
        M=M,
        N=N,
        temperature=temperature
    )
    
    # Print results
    print("\nYour input ended with:")
    print(" ".join(initial_tokens[-10:]))
    print("\nPredicted continuation:")
    print(" ".join(predicted_words))
    print("\nEnergies of predictions:")
    for i, (word, energy) in enumerate(zip(predicted_words, energies)):
        print(f"Word {i+1} ('{word}'): {energy:.4f}")

if __name__ == "__main__":
    main()