├── README.md ├── LICENSE └── mLSTM.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # xlstm 2 | my attempts at implementing various bits of Sepp Hochreiter's new xLSTM architecture 3 | very oversimplified and probably somewhat wrong! 4 | please open PRs and make it better. 5 | 6 | 7 | mLSTM: https://github.com/andrewgcodes/xlstm/blob/main/mLSTM.ipynb 8 | 9 | Open In Colab 10 | 11 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Andrew Kean Gao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /mLSTM.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [] 7 | }, 8 | "kernelspec": { 9 | "name": "python3", 10 | "display_name": "Python 3" 11 | }, 12 | "language_info": { 13 | "name": "python" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "markdown", 19 | "source": [ 20 | "[itsandrewgao](https://twitter.com/itsandrewgao) my very rushed and shitty attempt at implementing just the mLSTM to predict sine waves" 21 | ], 22 | "metadata": { 23 | "id": "9PvhlUCt5I-e" 24 | } 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "source": [ 29 | "\n", 30 | " \"Open\n", 31 | "" 32 | ], 33 | "metadata": { 34 | "id": "9K1Zitz28PWz" 35 | } 36 | }, 37 | { 38 | "cell_type": "code", 39 | "source": [ 40 | "import torch\n", 41 | "import torch.nn as nn\n", 42 | "import numpy as np\n", 43 | "import math\n", 44 | "import matplotlib.pyplot as plt\n", 45 | "\n", 46 | "def generate_sine_wave(seq_len, num_sequences):\n", 47 | " x = np.linspace(0, 2 * np.pi, seq_len)\n", 48 | " y = np.sin(x)\n", 49 | " return torch.tensor(y).float().view(-1, 1).repeat(1, num_sequences).unsqueeze(0)\n", 50 | "\n", 51 | "class mLSTM(nn.Module):\n", 52 | " def __init__(self, input_size, hidden_size, mem_dim):\n", 53 | " super(mLSTM, self).__init__()\n", 54 | " self.input_size = input_size\n", 55 | " self.hidden_size = hidden_size\n", 56 | " self.mem_dim = mem_dim\n", 57 | " self.Wq = nn.Parameter(torch.randn(hidden_size, input_size))\n", 58 | " self.bq = nn.Parameter(torch.randn(hidden_size, 1))\n", 59 | " self.Wk = nn.Parameter(torch.randn(mem_dim, input_size))\n", 60 | " self.bk = nn.Parameter(torch.randn(mem_dim, 1))\n", 61 | " self.Wv = nn.Parameter(torch.randn(mem_dim, input_size))\n", 62 | " self.bv = nn.Parameter(torch.randn(mem_dim, 1))\n", 63 | " self.wi = nn.Parameter(torch.randn(1, input_size))\n", 64 | " self.bi = nn.Parameter(torch.randn(1))\n", 65 | " self.wf = nn.Parameter(torch.randn(1, input_size))\n", 66 | " self.bf = nn.Parameter(torch.randn(1))\n", 67 | " self.Wo = nn.Parameter(torch.randn(hidden_size, input_size))\n", 68 | " self.bo = nn.Parameter(torch.randn(hidden_size, 1))\n", 69 | " self.reset_parameters()\n", 70 | "\n", 71 | " def reset_parameters(self):\n", 72 | " for p in self.parameters():\n", 73 | " if p.data.ndimension() >= 2:\n", 74 | " nn.init.xavier_uniform_(p.data)\n", 75 | " else:\n", 76 | " nn.init.zeros_(p.data)\n", 77 | "\n", 78 | " def forward(self, x, states):\n", 79 | " (C_prev, n_prev) = states\n", 80 | " qt = torch.matmul(self.Wq, x) + self.bq\n", 81 | " kt = (1 / math.sqrt(self.mem_dim)) * (torch.matmul(self.Wk, x) + self.bk)\n", 82 | " vt = torch.matmul(self.Wv, x) + self.bv\n", 83 | "\n", 84 | " it = torch.exp(torch.matmul(self.wi, x) + self.bi)\n", 85 | " ft = torch.sigmoid(torch.matmul(self.wf, x) + self.bf)\n", 86 | "\n", 87 | " vt = vt.squeeze()\n", 88 | " kt = kt.squeeze()\n", 89 | "\n", 90 | " C = ft * C_prev + it * torch.ger(vt, kt)\n", 91 | " n = ft * n_prev + it * kt.unsqueeze(1)\n", 92 | "\n", 93 | " max_nqt = torch.max(torch.abs(torch.matmul(n.T, qt)), torch.tensor(1.0))\n", 94 | " h_tilde = torch.matmul(C, qt) / max_nqt\n", 95 | " ot = torch.sigmoid(torch.matmul(self.Wo, x) + self.bo)\n", 96 | " ht = ot * h_tilde\n", 97 | "\n", 98 | " return ht, (C, n)\n", 99 | "\n", 100 | " def init_hidden(self):\n", 101 | " return (torch.zeros(self.mem_dim, self.mem_dim),\n", 102 | " torch.zeros(self.mem_dim, 1))\n", 103 | "\n", 104 | "input_size = 1\n", 105 | "hidden_size = 10\n", 106 | "mem_dim = 10\n", 107 | "seq_len = 100\n", 108 | "num_sequences = 1\n", 109 | "\n", 110 | "model = mLSTM(input_size, hidden_size, mem_dim)\n", 111 | "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n", 112 | "criterion = nn.MSELoss()\n", 113 | "\n", 114 | "data = generate_sine_wave(seq_len, num_sequences)\n", 115 | "\n", 116 | "for epoch in range(200):\n", 117 | " states = model.init_hidden()\n", 118 | " optimizer.zero_grad()\n", 119 | " loss = 0\n", 120 | " for t in range(seq_len - 1):\n", 121 | " x = data[:, t]\n", 122 | " y_true = data[:, t + 1]\n", 123 | " y_pred, states = model(x, states)\n", 124 | " loss += criterion(y_pred, y_true)\n", 125 | "\n", 126 | " loss.backward()\n", 127 | " optimizer.step()\n", 128 | "\n", 129 | " if epoch % 10 == 0:\n", 130 | " print(f'Epoch {epoch} Loss {loss.item()}')\n", 131 | "\n", 132 | "test_output = []\n", 133 | "states = model.init_hidden()\n", 134 | "for t in range(seq_len - 1):\n", 135 | " x = data[:, t]\n", 136 | " y_pred, states = model(x, states)\n", 137 | " test_output.append(y_pred.detach().numpy().ravel()[0])\n", 138 | "\n", 139 | "plt.figure(figsize=(10, 4))\n", 140 | "plt.title('Original vs. Predicted Sine Wave')\n", 141 | "plt.plot(data.numpy().ravel(), label='Original')\n", 142 | "plt.plot(test_output, label='Predicted')\n", 143 | "plt.legend()\n", 144 | "plt.show()\n" 145 | ], 146 | "metadata": { 147 | "colab": { 148 | "base_uri": "https://localhost:8080/", 149 | "height": 738 150 | }, 151 | "id": "aqW1CSpC4GtV", 152 | "outputId": "5e0182e0-439b-43ba-d4ea-473f66be3343" 153 | }, 154 | "execution_count": null, 155 | "outputs": [ 156 | { 157 | "output_type": "stream", 158 | "name": "stdout", 159 | "text": [ 160 | "Epoch 0 Loss 50.32358169555664\n", 161 | "Epoch 10 Loss 42.4661979675293\n", 162 | "Epoch 20 Loss 34.83522033691406\n", 163 | "Epoch 30 Loss 28.28700065612793\n", 164 | "Epoch 40 Loss 23.147504806518555\n", 165 | "Epoch 50 Loss 18.864025115966797\n", 166 | "Epoch 60 Loss 15.362940788269043\n", 167 | "Epoch 70 Loss 12.43962574005127\n", 168 | "Epoch 80 Loss 9.934250831604004\n", 169 | "Epoch 90 Loss 7.787623882293701\n", 170 | "Epoch 100 Loss 5.969943046569824\n", 171 | "Epoch 110 Loss 4.4392194747924805\n", 172 | "Epoch 120 Loss 3.1804585456848145\n", 173 | "Epoch 130 Loss 2.2156834602355957\n", 174 | "Epoch 140 Loss 1.5426660776138306\n", 175 | "Epoch 150 Loss 1.1080152988433838\n", 176 | "Epoch 160 Loss 0.841367781162262\n", 177 | "Epoch 170 Loss 0.6825742125511169\n", 178 | "Epoch 180 Loss 0.5876235961914062\n", 179 | "Epoch 190 Loss 0.5289116501808167\n" 180 | ] 181 | }, 182 | { 183 | "output_type": "display_data", 184 | "data": { 185 | "text/plain": [ 186 | "
" 187 | ], 188 | "image/png": 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\n" 189 | }, 190 | "metadata": {} 191 | } 192 | ] 193 | } 194 | ] 195 | } 196 | --------------------------------------------------------------------------------