{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear(in_features=10, out_features=5, bias=False) 50\n",
      "Linear(in_features=5, out_features=10, bias=False) 50\n",
      "Linear(in_features=10, out_features=10, bias=False) 100\n"
     ]
    }
   ],
   "source": [
    "class Toy(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(Toy, self).__init__()\n",
    "        self.fc1 = nn.Linear(10,5, bias=False)\n",
    "        self.m3 = nn.Sequential(nn.Linear(5, 10, bias=False), nn.Linear(10,10, bias=False))\n",
    "\n",
    "t = Toy()\n",
    "for mod in t.modules():\n",
    "    for p in mod.parameters(recurse=False):\n",
    "        print(mod, p.numel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 10]) 50\n",
      "torch.Size([10, 5]) 50\n",
      "torch.Size([10, 10]) 100\n"
     ]
    }
   ],
   "source": [
    "for p in t.parameters():\n",
    "    print(p.shape, p.numel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'224'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_str = torch.__config__.parallel_info()\n",
    "inter_str = conf_str.split(\"hardware_concurrency() : \")[1]\n",
    "max_concurrency = inter_str.split(\"\\n\")[0]\n",
    "max_concurrency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n",
      "0 1\n",
      "0 2\n",
      "1 0\n",
      "1 1\n",
      "1 2\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        print(i, j)\n",
    "        if i == 1 and j == 2:break\n",
    "    else:\n",
    "        continue\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "colossalai-py310",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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