torch_cm_bridge.py

"""
PyTorch Bridge for Cognitional Mechanics
Demonstrates GPU-accelerated batch processing of non-commutative paths.
"""

import torch

def torch_logical_leap(states, c_limit, n_dims):
    """
    Vectorized Logical Leap for Tensor Cores.
    """
    norms = torch.norm(states, dim=1, keepdim=True)
    threshold = c_limit * torch.sqrt(torch.tensor(float(n_dims)))
    
    # Deterministic rescaling without branching logic (GPU friendly)
    scale = torch.clamp(norms / threshold, min=1.0)
    return states / scale

def run_gpu_cm_batch(batch_size, n_dims, depth):
    # Initialize states on GPU
    device = "cuda" if torch.cuda.is_available() else "cpu"
    states = torch.randn(batch_size, n_dims, device=device)
    
    # Operators as a stack of matrices
    operators = [torch.randn(n_dims, n_dims, device=device) for _ in range(depth)]
    
    # Execute Path
    for op in operators:
        states = states @ op.t()
        states = torch_logical_leap(states, 0.5, n_dims)
        
    return states

print("CM GPU Bridge Loaded. Ready for large-scale semantic manifold simulation.")