ucsb-nlp-chang/skill-usage/mhc-algorithm

# Required packages
pip install torch einops numpy

import torch
import torch.nn as nn
from einops import rearrange, einsum
def sinkhorn_knopp(logits, num_iters=20, tau=0.05):
    log_alpha = logits / tau
    for _ in range(num_iters):
        log_alpha = log_alpha - torch.logsumexp(log_alpha, dim=-1, keepdim=True)
        log_alpha = log_alpha - torch.logsumexp(log_alpha, dim=-2, keepdim=True)
    return torch.exp(log_alpha)
class HyperConnections(nn.Module):
    def __init__(self, num_streams, dim, branch=None, layer_idx=0):
        super().__init__()
        self.num_streams = num_streams
        self.branch = branch
        # Initialize H_res near identity (use small negative for gradient flow)
        init_h_res = torch.full((num_streams, num_streams), -0.1)
        init_h_res.fill_diagonal_(0.0)
        self.H_res_logits = nn.Parameter(init_h_res)
        # H_pre/H_post for depth connections
        init_h_pre = torch.full((1, num_streams), -0.1)
        init_h_pre[0, layer_idx % num_streams] = 0.0
        self.H_pre_logits = nn.Parameter(init_h_pre)
        self.H_post_logits = nn.Parameter(torch.zeros(1, num_streams))
    def forward(self, x):
        s = self.num_streams
        x = rearrange(x, "(b s) t d -> b t s d", s=s)
        h_res = sinkhorn_knopp(self.H_res_logits)
        x_mixed = einsum(h_res, x, "s t, b n s d -> b n t d")
        h_pre = self.H_pre_logits.softmax(dim=-1)
        branch_in = einsum(h_pre, x, "v s, b n s d -> b n v d").squeeze(-2)
        branch_out = self.branch(branch_in) if self.branch else branch_in
        h_post = self.H_post_logits.softmax(dim=-1)
        depth_out = einsum(branch_out, h_post, "b t d, v s -> b t s d")
        output = x_mixed + depth_out
        return rearrange(output, "b t s d -> (b s) t d")

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, einsum, repeat, reduce