{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/duoan/TorchCode/blob/master/templates/28_moe.ipynb)\n",
    "\n",
    "# 🔴 Hard: Mixture of Experts (MoE)\n",
    "\n",
    "Implement a **Mixture of Experts** layer (Mixtral / Switch Transformer style).\n",
    "\n",
    "### Signature\n",
    "```python\n",
    "class MixtureOfExperts(nn.Module):\n",
    "    def __init__(self, d_model, d_ff, num_experts, top_k=2): ...\n",
    "    def forward(self, x: Tensor) -> Tensor:\n",
    "        # x: (B, S, D) -> (B, S, D)\n",
    "```\n",
    "\n",
    "### Architecture\n",
    "- `self.router`: `nn.Linear(d_model, num_experts)` — gating network\n",
    "- `self.experts`: `nn.ModuleList` of MLPs `(Linear→ReLU→Linear)`\n",
    "- For each token: select top-k experts, compute weighted sum of their outputs"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Install torch-judge in Colab (no-op in JupyterLab/Docker)\n",
    "try:\n",
    "    import google.colab\n",
    "    get_ipython().run_line_magic('pip', 'install -q torch-judge')\n",
    "except ImportError:\n",
    "    pass\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ✏️ YOUR IMPLEMENTATION HERE\n",
    "\n",
    "class MixtureOfExperts(nn.Module):\n",
    "    def __init__(self, d_model, d_ff, num_experts, top_k=2):\n",
    "        super().__init__()\n",
    "        pass  # router + experts\n",
    "\n",
    "    def forward(self, x):\n",
    "        pass  # route tokens to top-k experts"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 🧪 Debug\n",
    "moe = MixtureOfExperts(32, 64, num_experts=4, top_k=2)\n",
    "x = torch.randn(2, 8, 32)\n",
    "print('Output:', moe(x).shape)\n",
    "print('Params:', sum(p.numel() for p in moe.parameters()))"
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ✅ SUBMIT\n",
    "from torch_judge import check\n",
    "check('moe')"
   ],
   "execution_count": null
  }
 ],
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