/insane-shortest-paths.ipynb at a24288e28c4b53fdd6467ed4eed626fa0586bf72

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     "text": [
      "[set(), set(), {5, 6}, {4}, {3}, {2, 6}, {2, 5}]\n",
      "[set(), {6}, set(), {4, 5, 6}, {3}, {3, 6}, {1, 3, 5}]\n",
      "[set(), {4}, set(), {4, 5}, {1, 3}, {3, 6}, {5}]\n",
      "[set(), {2, 6}, {1, 6}, {6}, set(), set(), {1, 2, 3}]\n",
      "[set(), {3}, {3}, {1, 2, 5, 6}, {5}, {3, 4}, {3}]\n",
      "[set(), {3, 6}, {4}, {1}, {2}, {6}, {1, 5}]\n",
      "[set(), {2, 3}, {1, 3, 6}, {1, 2, 4}, {3}, set(), {2}]\n",
      "[set(), {4}, set(), {4}, {1, 3, 5, 6}, {4}, {4}]\n",
      "[set(), {3, 4, 5}, {6}, {1}, {1, 6}, {1}, {2, 4}]\n",
      "[set(), {5, 6}, {6}, {6}, {5, 6}, {1, 4}, {1, 2, 3, 4}]\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"how-tsp-should-be.ipynb\n",
    "\n",
    "Automatically generated by Colab.\n",
    "\n",
    "Original file is located at\n",
    "    https://colab.research.google.com/drive/1InE1iW8ARzndPpvqH_9y22s81sOiHxPs\n",
    "\"\"\"\n",
    "\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "from math import sqrt\n",
    "from collections import deque\n",
    "import os\n",
    "import random\n",
    "import pickle\n",
    "import ipdb\n",
    "\n",
    "#  torch.manual_seed(30)\n",
    "#  random.seed(30)\n",
    "torch.manual_seed(33)\n",
    "random.seed(33)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# assert device.type == \"cuda\", \"CUDA is not available. Please check your GPU setup.\"\n",
    "\n",
    "NVTXS = 6\n",
    "MAXDIST = NVTXS+1\n",
    "AVGDEG = 2\n",
    "SEQLEN = NVTXS + 1\n",
    "HIDDENDIM = 4*NVTXS+2\n",
    "\n",
    "# 0: ANSFLAG\n",
    "# 1:NVTXS+1 NBRS\n",
    "# NVTXS+1: 2*NVTXS+1 REACH\n",
    "# 2*NVTXS+1: 3*NVTXS+1 SELF\n",
    "# -1 NOTANSFLAG\n",
    "\n",
    "START_REACH = NVTXS+1\n",
    "START_OUT = 2*NVTXS+1\n",
    "START_SELF = 3*NVTXS+1\n",
    "SRC_FLAG_IDX = START_SELF\n",
    "SOURCE = 1\n",
    "TARGET = 2\n",
    "ANS_FLAG_IDX = 0\n",
    "NOTANS_FLAG_IDX = -1\n",
    "\n",
    "def print_everything(data):\n",
    "    print(\"NBRS\")\n",
    "    print(data[0, 1:, 1:1+NVTXS])\n",
    "    print(\"REACH\")\n",
    "    print(data[0, 1:, START_REACH:START_REACH+NVTXS])\n",
    "    print(\"ANSFLAG\")\n",
    "    print(data[0, :, 0])\n",
    "    print(\"MORE FLAGS\")\n",
    "    print(data[0, :, -1])\n",
    "    print(\"SELF\")\n",
    "    print(data[0, 1:, START_SELF:START_SELF+NVTXS])\n",
    "    print(\"OUT\")\n",
    "    print(data[0, 0, START_OUT:START_OUT+NVTXS])\n",
    "\n",
    "\n",
    "def random_graph():\n",
    "    data = torch.zeros((SEQLEN, HIDDENDIM))\n",
    "\n",
    "    for i in range(1,NVTXS+1):\n",
    "        data[i, START_SELF-1+i] = 1\n",
    "\n",
    "    adj_list = [set() for _ in range(SEQLEN)]\n",
    "    indices = [random.randint(1, NVTXS) for _ in range(AVGDEG * NVTXS)]\n",
    "    for i in range(0, len(indices), 2):\n",
    "        u = indices[i]\n",
    "        v = indices[i + 1]\n",
    "        if u != v:\n",
    "            data[v,u] = 1\n",
    "            data[u,v] = 1\n",
    "            data[v,NVTXS+u] = 1\n",
    "            data[u,NVTXS+v] = 1\n",
    "            adj_list[u].add(v)\n",
    "            adj_list[v].add(u)\n",
    "\n",
    "    data[0, ANS_FLAG_IDX] = 1\n",
    "    data[1:, NOTANS_FLAG_IDX] = 1\n",
    "\n",
    "    # TODO: this is kind of a hack\n",
    "    data[0, START_REACH:START_REACH+NVTXS] = 1\n",
    "    return data, adj_list\n",
    "\n",
    "\"\"\"\n",
    "input: G, represented as an adjacency list\n",
    "output: distance from SOURCE to TARGET\n",
    "\"\"\"\n",
    "def SSSP(G):\n",
    "    dist = [MAXDIST for _ in G]\n",
    "    dist[SOURCE] = 0\n",
    "    frontier = deque()\n",
    "    frontier.append(SOURCE)\n",
    "    while len(frontier) > 0:\n",
    "        vtx = frontier.popleft()\n",
    "        for x in G[vtx]:\n",
    "            if dist[x] == MAXDIST:\n",
    "                dist[x] = 1 + dist[vtx]\n",
    "                frontier.append(x)\n",
    "                if x == TARGET:\n",
    "                    return dist[TARGET]\n",
    "    return MAXDIST\n",
    "\n",
    "def mkbatch(size):\n",
    "    graphs1 = []\n",
    "    distance1 = []\n",
    "\n",
    "    for i in range(size):\n",
    "        data, adj_list = random_graph()\n",
    "        dist = SSSP(adj_list)\n",
    "        graphs1.append(data)\n",
    "        distance1.append(dist)\n",
    "\n",
    "        print(adj_list)\n",
    "\n",
    "    data = torch.stack(graphs1)\n",
    "    labels = torch.tensor(distance1, dtype=torch.float16)\n",
    "    return data, labels\n",
    "\n",
    "\"\"\"\n",
    "TODO: WRAP EVERYTHING in nn.Parameter(torch.zeros((1, HIDDENDIM)))\n",
    "and then do my perturbing parameters experiment\n",
    "\n",
    "TODO:\n",
    "    USE activation magic to bring everything back to the 0/1 realm instead of possibly being 0/2 valued\n",
    "\"\"\"\n",
    "\n",
    "class SillyTransformer(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.most_KQVs = []\n",
    "        for head in range(1,NVTXS+1):\n",
    "          Q = torch.zeros((2, HIDDENDIM))\n",
    "          Q[0, START_REACH-1+head] = 1000\n",
    "          Q[1, NOTANS_FLAG_IDX] = 1\n",
    "\n",
    "          K = torch.zeros((2, HIDDENDIM))\n",
    "          K[0, head] = 1\n",
    "          K[1, ANS_FLAG_IDX] = 200\n",
    "\n",
    "          V = torch.zeros((NVTXS,HIDDENDIM))\n",
    "          for i in range(NVTXS):\n",
    "              V[i, START_SELF+i] = 1\n",
    "\n",
    "          self.most_KQVs.append((K, Q, V))\n",
    "\n",
    "        self.weird_KQVs = []\n",
    "        for layer in range(NVTXS):\n",
    "            K = torch.zeros((3, HIDDENDIM))\n",
    "            K[0, NOTANS_FLAG_IDX] = -1000\n",
    "            K[0, SRC_FLAG_IDX] = +1100\n",
    "            K[1, NOTANS_FLAG_IDX] = -1000\n",
    "            K[1, NVTXS+TARGET] = +1100\n",
    "            K[1, ANS_FLAG_IDX] = -1100\n",
    "            K[2, ANS_FLAG_IDX] = 10\n",
    "\n",
    "            Q = torch.zeros((3, HIDDENDIM))\n",
    "            Q[:, ANS_FLAG_IDX] = 1\n",
    "\n",
    "            V = torch.zeros((NVTXS, HIDDENDIM))\n",
    "            V[layer, SRC_FLAG_IDX] = 1\n",
    "\n",
    "            self.weird_KQVs.append((K, Q, V))\n",
    "\n",
    "    def forward(self, src):\n",
    "      for layer in range(NVTXS):\n",
    "        allKQVs = [self.weird_KQVs[layer]] + self.most_KQVs\n",
    "        head_outputs = []\n",
    "        for (K, Q, V) in allKQVs:\n",
    "            ksrc = torch.matmul(src, K.unsqueeze(0).transpose(-2, -1))\n",
    "            qsrc = torch.matmul(src, Q.unsqueeze(0).transpose(-2, -1))\n",
    "            vsrc = torch.matmul(src, V.unsqueeze(0).transpose(-2, -1))\n",
    "\n",
    "            scores = torch.matmul(qsrc, ksrc.transpose(-2, -1))\n",
    "            attention_weights = torch.softmax(scores, dim=-1)\n",
    "            head_output = torch.matmul(attention_weights, vsrc)\n",
    "            head_outputs.append(head_output)\n",
    "\n",
    "        new_reaches = sum(head_outputs[1:])\n",
    "        BSZ = new_reaches.shape[0]\n",
    "\n",
    "        nodelta_nbrs = torch.zeros((BSZ, SEQLEN, NVTXS+1))\n",
    "        morepadlol = torch.zeros((BSZ, SEQLEN, 1+NVTXS))\n",
    "\n",
    "        DIFF = torch.cat((nodelta_nbrs, new_reaches, head_outputs[0], morepadlol), dim=2)\n",
    "        src += torch.cat((nodelta_nbrs, new_reaches, head_outputs[0], morepadlol), dim=2)\n",
    "        src[:, :, START_REACH:START_REACH+NVTXS] = 2*torch.sigmoid(src[:,:, START_REACH:START_REACH+NVTXS]*1000)-1\n",
    "\n",
    "        #  print(\"SRC\")\n",
    "        #  print_everything(src)\n",
    "\n",
    "      canreach = src[:,0,START_OUT:START_OUT+NVTXS]\n",
    "      #  __import__('ipdb').set_trace()\n",
    "      final_output = 1+torch.sum(1-canreach,dim=1)\n",
    "      return final_output\n",
    "\n",
    "model = SillyTransformer()\n",
    "model.to(device)\n",
    "\n",
    "data, labels = mkbatch(10)\n",
    "assert torch.all(model(data) == labels)\n",
    "\n"
   ]
  }
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