Hologram-LLM-DEMO1.py

"""
Hologram-LLM-DEMO1.py: 

Francisco Angulo de Lafuente
10 September 2024
https://github.com/Agnuxo1
https://huggingface.co/Agnuxo
https://www.researchgate.net/profile/Francisco-Angulo-Lafuente-3

This script provides a basic demonstration of simulating a holographic LLM 
with raytracing using CuPy and Numba for GPU acceleration. 
It showcases the core concepts of hologram propagation and raytracing 
through a simplified model.
"""

import numpy as np
import cupy as cp
from cupyx.scipy.fft import fft2, ifft2
from numba import cuda
import math

# Configuración del dispositivo CUDA
cuda.select_device(0)

# Parámetros de simulación
GRID_SIZE = 1024
WAVELENGTH = 532e-9  # Longitud de onda verde (532 nm)
PROPAGATION_DISTANCE = 0.1  # 10 cm

# Funciones de utilidad
def angular_spectrum_propagation(field, distance, wavelength):
    kx, ky = cp.meshgrid(cp.fft.fftfreq(GRID_SIZE), cp.fft.fftfreq(GRID_SIZE))
    kz = cp.sqrt(1 - (wavelength * kx)**2 - (wavelength * ky)**2)
    return ifft2(fft2(field) * cp.exp(1j * 2 * cp.pi * distance * kz / wavelength))

@cuda.jit
def raytrace_kernel(hologram, neurons, output):
    i, j = cuda.grid(2)
    if i < output.shape[0] and j < output.shape[1]:
        ray_origin = cuda.local.array(3, dtype=cp.float32)
        ray_direction = cuda.local.array(3, dtype=cp.float32)
        
        ray_origin[0] = i / output.shape[0] - 0.5
        ray_origin[1] = j / output.shape[1] - 0.5
        ray_origin[2] = -1.0
        
        ray_direction[0] = 0
        ray_direction[1] = 0
        ray_direction[2] = 1
        
        accumulated_intensity = 0.0
        
        for k in range(neurons.shape[0]):
            sphere_center = neurons[k]
            sphere_radius = 0.01
            
            a = (ray_direction[0]**2 + ray_direction[1]**2 + ray_direction[2]**2)
            b = 2 * (ray_direction[0] * (ray_origin[0] - sphere_center[0]) +
                     ray_direction[1] * (ray_origin[1] - sphere_center[1]) +
                     ray_direction[2] * (ray_origin[2] - sphere_center[2]))
            c = ((ray_origin[0] - sphere_center[0])**2 +
                 (ray_origin[1] - sphere_center[1])**2 +
                 (ray_origin[2] - sphere_center[2])**2 - sphere_radius**2)
            
            discriminant = b**2 - 4*a*c
            
            if discriminant >= 0:
                t = (-b - math.sqrt(discriminant)) / (2*a)
                if t > 0:
                    intersection_point = (
                        ray_origin[0] + t * ray_direction[0],
                        ray_origin[1] + t * ray_direction[1],
                        ray_origin[2] + t * ray_direction[2]
                    )
                    hologram_value = hologram[
                        int((intersection_point[0] + 0.5) * hologram.shape[0]),
                        int((intersection_point[1] + 0.5) * hologram.shape[1])
                    ]
                    accumulated_intensity += abs(hologram_value)
        
        output[i, j] = accumulated_intensity

class HolographicLLM:
    def __init__(self, num_neurons):
        self.num_neurons = num_neurons
        real_part = cp.random.random((GRID_SIZE, GRID_SIZE), dtype=cp.float32)
        imag_part = cp.random.random((GRID_SIZE, GRID_SIZE), dtype=cp.float32)
        self.hologram = real_part + 1j * imag_part
        self.neurons = cp.random.uniform(-0.5, 0.5, (num_neurons, 3))
    
    def propagate_hologram(self):
        self.hologram = angular_spectrum_propagation(
            self.hologram,
            PROPAGATION_DISTANCE,
            WAVELENGTH
        )
    
    def raytrace(self):
        output = cp.zeros((GRID_SIZE, GRID_SIZE), dtype=cp.float32)
        threadsperblock = (16, 16)
        blockspergrid_x = math.ceil(output.shape[0] / threadsperblock[0])
        blockspergrid_y = math.ceil(output.shape[1] / threadsperblock[1])
        blockspergrid = (blockspergrid_x, blockspergrid_y)
        
        raytrace_kernel[blockspergrid, threadsperblock](
            self.hologram, self.neurons, output
        )
        return output
    
    def inference(self, input_data):
        # Simular la codificación de entrada
        self.hologram *= cp.array(input_data).reshape(GRID_SIZE, GRID_SIZE)
        
        # Propagar el holograma
        self.propagate_hologram()
        
        # Realizar raytracing
        output = self.raytrace()
        
        # Decodificar la salida (simplificado)
        return cp.mean(output)

# Demostración
if __name__ == "__main__":
    model = HolographicLLM(num_neurons=1000)
    
    # Simular una entrada
    input_data = cp.random.random(GRID_SIZE * GRID_SIZE)
    
    # Realizar inferencia
    result = model.inference(input_data)
    
    print(f"Resultado de la inferencia: {result}")