diff --git a/examples/2d_mean_flow.ipynb b/examples/2d_mean_flow.ipynb new file mode 100644 index 00000000..bb9bec16 --- /dev/null +++ b/examples/2d_mean_flow.ipynb @@ -0,0 +1,446 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "QXefm3NrSLkG" + }, + "source": [ + "# A simple 2D MeanFlow model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0hcY4EBcSLkH" + }, + "source": [ + "This notebook trains and evaluates a simple 2D MeanFlow model with CondOT (i.e., linear) scheduler.\n", + "\n", + "Dataset: 2D checkerboard\n", + "\n", + "Model (velocity): MLP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rdych2P4SLkI" + }, + "source": [ + "## Imports and init device" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rb5VSo4mNkVd", + "outputId": "b252ca43-50a2-4eb8-875e-62f6f095c3aa" + }, + "outputs": [], + "source": [ + "import time\n", + "import torch\n", + "\n", + "from torch import nn, Tensor\n", + "\n", + "# flow_matching\n", + "from flow_matching.path.scheduler import CondOTScheduler\n", + "from flow_matching.path import AffineProbPath\n", + "from flow_matching.solver import Solver, ODESolver\n", + "from flow_matching.utils import ModelWrapper\n", + "\n", + "# visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from matplotlib import cm\n", + "\n", + "\n", + "# To avoide meshgrid warning\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=UserWarning, module='torch')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nrkB3EgcSLkJ", + "outputId": "20aaddaa-032f-4bd3-b103-06f14acbb623" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using gpu\n" + ] + } + ], + "source": [ + "if torch.cuda.is_available():\n", + " device = 'cuda:6'\n", + " print('Using gpu')\n", + "else:\n", + " device = 'cpu'\n", + " print('Using cpu.')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "93orLVECSLkK", + "outputId": "8aa303fa-39b2-4240-e0b6-fec8cfc133f9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.manual_seed(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m2wy46WpLZs0" + }, + "source": [ + "## Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "7zQ9v3z3SLkL" + }, + "outputs": [], + "source": [ + "def inf_train_gen(batch_size: int = 200, device: str = \"cpu\"):\n", + " x1 = torch.rand(batch_size, device=device) * 4 - 2\n", + " x2_ = torch.rand(batch_size, device=device) - torch.randint(high=2, size=(batch_size, ), device=device) * 2\n", + " x2 = x2_ + (torch.floor(x1) % 2)\n", + "\n", + " data = 1.0 * torch.cat([x1[:, None], x2[:, None]], dim=1) / 0.45\n", + "\n", + " return data.float()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x7ybBp-nSLkL" + }, + "source": [ + "## Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "3BDuyAydSLkL" + }, + "outputs": [], + "source": [ + "# Activation class\n", + "class Swish(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " return torch.sigmoid(x) * x\n", + "\n", + "# Model class\n", + "class MLP(nn.Module):\n", + " def __init__(self, input_dim: int = 2, time_dim: int = 1, hidden_dim: int = 128):\n", + " super().__init__()\n", + "\n", + " self.input_dim = input_dim\n", + " self.time_dim = time_dim\n", + " self.hidden_dim = hidden_dim\n", + "\n", + " self.main = nn.Sequential(\n", + " nn.Linear(input_dim + 2*time_dim, hidden_dim),\n", + " Swish(),\n", + " *[nn.Sequential(\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " Swish(),\n", + " ) for _ in range(4)],\n", + " nn.Linear(hidden_dim, input_dim),\n", + " )\n", + "\n", + " def forward(self, x: Tensor, r: Tensor, t: Tensor) -> Tensor:\n", + " sz = x.size()\n", + " x = x.reshape(-1, self.input_dim)\n", + " t = t.reshape(-1, self.time_dim).float()\n", + " r = r.reshape(-1, self.time_dim).float()\n", + "\n", + " t = t.reshape(-1, 1).expand(x.shape[0], 1)\n", + " r = r.reshape(-1, 1).expand(x.shape[0], 1)\n", + " h = torch.cat([x, r, t], dim=1)\n", + " output = self.main(h)\n", + "\n", + " return output.reshape(*sz)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uk-Mw-9WSLkM" + }, + "source": [ + "## Train MeanFlow model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cp24XAWWSLkM", + "outputId": "7e5dfb0c-0c9e-40d9-bfe6-b2ab514f418f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| iter 2000 | 11.61 ms/step | loss 5.138 \n", + "| iter 4000 | 11.18 ms/step | loss 5.124 \n", + "| iter 6000 | 11.14 ms/step | loss 6.121 \n", + "| iter 8000 | 10.48 ms/step | loss 6.053 \n", + "| iter 10000 | 11.04 ms/step | loss 5.330 \n", + "| iter 12000 | 11.17 ms/step | loss 7.085 \n", + "| iter 14000 | 11.14 ms/step | loss 6.755 \n", + "| iter 16000 | 11.20 ms/step | loss 6.784 \n", + "| iter 18000 | 10.95 ms/step | loss 6.861 \n", + "| iter 20000 | 11.18 ms/step | loss 5.581 \n", + "| iter 22000 | 10.90 ms/step | loss 7.080 \n", + "| iter 24000 | 10.94 ms/step | loss 7.410 \n", + "| iter 26000 | 11.14 ms/step | loss 6.057 \n", + "| iter 28000 | 11.06 ms/step | loss 6.244 \n", + "| iter 30000 | 11.04 ms/step | loss 5.917 \n", + "| iter 32000 | 11.10 ms/step | loss 5.798 \n", + "| iter 34000 | 10.94 ms/step | loss 9.288 \n", + "| iter 36000 | 11.13 ms/step | loss 5.169 \n", + "| iter 38000 | 11.16 ms/step | loss 7.332 \n", + "| iter 40000 | 11.17 ms/step | loss 6.179 \n" + ] + } + ], + "source": [ + "# training arguments\n", + "lr = 3e-4\n", + "batch_size = 4096\n", + "iterations = 40001\n", + "print_every = 2000\n", + "hidden_dim = 256\n", + "\n", + "# average velocity field model init\n", + "avf = MLP(input_dim=2, time_dim=1, hidden_dim=hidden_dim).to(device)\n", + "# avf = torch.nn.DataParallel(avf, device_ids=list(range(torch.cuda.device_count())))\n", + "\n", + "# instantiate an affine path object\n", + "path = AffineProbPath(scheduler=CondOTScheduler())\n", + "\n", + "# init optimizer\n", + "optim = torch.optim.Adam(avf.parameters(), lr=lr)\n", + "\n", + "# train\n", + "start_time = time.time()\n", + "for i in range(iterations):\n", + " optim.zero_grad()\n", + "\n", + " # sample data (user's responsibility): in this case, (X_0,X_1) ~ pi(X_0,X_1) = q(X_1)N(X_0|0,I)\n", + " x_0 = inf_train_gen(batch_size=batch_size, device=device) # sample data\n", + " x_1 = torch.randn_like(x_0).to(device)\n", + "\n", + " # sample two time points from a logit-normal distribution\n", + " r = torch.sigmoid(torch.randn(x_1.shape[0], device=device) - 0.4)\n", + " t = torch.sigmoid(torch.randn(x_1.shape[0], device=device) - 0.4) # mean -0.4, var 1\n", + "\n", + " # set r = t for 75% of the batch\n", + " mask = torch.randperm(batch_size)[:int(batch_size * 0.75)]\n", + " r[mask] = t[mask]\n", + "\n", + " # ensure r <= t\n", + " mask = r > t\n", + " r[mask], t[mask] = t[mask], r[mask]\n", + "\n", + " # sample probability path\n", + " path_sample = path.sample(t=t, x_0=x_0, x_1=x_1)\n", + " x_t = path_sample.x_t\n", + " dx_t = path_sample.dx_t\n", + "\n", + " # predict average velocity\n", + " v, dv_t = torch.func.jvp(avf, (x_t, r, t), (dx_t, torch.zeros_like(r), torch.ones_like(t)))\n", + "\n", + " # target average velocity\n", + " v_tgt = dx_t - (t - r).unsqueeze(1) * dv_t\n", + "\n", + " # mean flow l2 loss\n", + " loss = torch.pow(avf(x_t, r, t) - v_tgt.detach(), 2).mean()\n", + " \n", + " # optimizer step\n", + " (loss / (loss.detach() + 1e-8)).backward() # backward pass with adaptive loss weighting\n", + " torch.nn.utils.clip_grad_norm_(avf.parameters(), max_norm=1.0) # gradient clipping\n", + " optim.step() # update\n", + "\n", + " # log loss\n", + " if (i+1) % print_every == 0:\n", + " elapsed = time.time() - start_time\n", + " print('| iter {:6d} | {:5.2f} ms/step | loss {:8.3f} '\n", + " .format(i+1, elapsed*1000/print_every, loss.item()))\n", + " start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Tme-Dt7OSLkN" + }, + "source": [ + "#### Sample from trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "g7rcNArdSLkN" + }, + "outputs": [], + "source": [ + "class WrappedModel(ModelWrapper):\n", + " def forward(self, x: torch.Tensor, r: torch.Tensor, t: torch.Tensor, **extras):\n", + " return self.model(x, r, t)\n", + "\n", + "wrapped_avf = WrappedModel(avf)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "neeacWHQSLkN" + }, + "outputs": [], + "source": [ + "norm = cm.colors.Normalize(vmax=50, vmin=0)\n", + "\n", + "batch_size = 50000 # batch size\n", + "eps_time = 1e-2\n", + "\n", + "x_init = torch.randn((batch_size, 2), dtype=torch.float32, device=device)\n", + "x_gen = x_init - wrapped_avf(x_init, torch.zeros(batch_size, device=device), torch.ones(batch_size, device=device)).detach() # sample from the model\n", + "sol = torch.stack([x_init, x_gen], dim=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "27KAaL_iSLkN" + }, + "source": [ + "### Visualize the path" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 536 + }, + "id": "POkvui7PSLkN", + "outputId": "bcaf993e-3f83-40e3-9f88-fc4f75e756fe" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sol = sol.cpu().numpy()\n", + "T = [0, 1]\n", + "\n", + "fig, axs = plt.subplots(1, 2,figsize=(10,10))\n", + "\n", + "for i in range(2):\n", + " H= axs[i].hist2d(sol[i,:,0], sol[i,:,1], 300, range=((-5,5), (-5,5)))\n", + "\n", + " cmin = 0.0\n", + " cmax = torch.quantile(torch.from_numpy(H[0]), 0.99).item()\n", + "\n", + " norm = cm.colors.Normalize(vmax=cmax, vmin=cmin)\n", + "\n", + " _ = axs[i].hist2d(sol[i,:,0], sol[i,:,1], 300, range=((-5,5), (-5,5)), norm=norm)\n", + "\n", + " axs[i].set_aspect('equal')\n", + " axs[i].axis('off')\n", + " axs[i].set_title('t= %.2f' % (T[i]))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "spt", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}