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228 lines
39 KiB
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228 lines
39 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/mlabonne/how-to-data-science/blob/main/Visualizing_GPT_2's_Loss_Landscape.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Visualizing GPT-2's Loss Landscape\n",
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"\n",
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"❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
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"\n",
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"Simple perturbation-based calculation of the negative log-likelihood loss in two directions, given \"I have a dream\" as input.\n",
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"\n",
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"Reference: [Visualizing the Loss Landscape of Neural Nets](https://arxiv.org/abs/1712.09913), by Li et al. (2018)"
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],
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"metadata": {
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"id": "Dgptqrg0zEY5"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "lIYdn1woOS1n",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "ffd96eb4-2861-4658-e89d-383eaeb36c3b"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m47.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m75.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m67.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h"
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]
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}
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],
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"source": [
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"!pip install -q transformers"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"%%time\n",
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"\n",
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"import torch\n",
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"from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
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"import numpy as np\n",
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"import plotly.graph_objects as go\n",
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"from tqdm import tqdm\n",
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"import imageio\n",
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"import os\n",
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"\n",
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"# Load pre-trained model\n",
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"model_name = 'gpt2'\n",
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"model = GPT2LMHeadModel.from_pretrained(model_name)\n",
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"tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
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"\n",
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"# Set model to evaluation mode\n",
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"model.eval()\n",
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"\n",
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"# Define our input\n",
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"input_text = \"I have a dream\"\n",
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"inputs = tokenizer.encode_plus(input_text, return_tensors=\"pt\")\n",
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"\n",
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"# Compute the original loss\n",
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"outputs = model(**inputs, labels=inputs[\"input_ids\"])\n",
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"original_loss = outputs.loss.item()\n",
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"\n",
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"# Define two random directions\n",
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"direction1 = [torch.randn_like(p) for p in model.parameters()]\n",
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"direction2 = [torch.randn_like(p) for p in model.parameters()]\n",
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"\n",
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"# Normalize vectors\n",
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"for p, d1, d2 in zip(model.parameters(), direction1, direction2):\n",
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" norm_p = torch.linalg.norm(p.flatten())\n",
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" d1.div_(torch.linalg.norm(d1.flatten())).mul_(norm_p)\n",
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" d2.div_(torch.linalg.norm(d2.flatten())).mul_(norm_p)\n",
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"\n",
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"# Define the range to explore\n",
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"x = np.linspace(-1, 1, 20)\n",
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"y = np.linspace(-1, 1, 20)\n",
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"X, Y = np.meshgrid(x, y)\n",
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"\n",
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"# Prepare to collect the losses\n",
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"Z = np.zeros_like(X)\n",
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"\n",
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"# Compute loss for each direction\n",
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"for i in tqdm(range(x.size), desc=\"x progress\"):\n",
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" for j in tqdm(range(y.size), desc=\"y progress\", leave=False):\n",
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" # Perturb the model parameters\n",
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" for p, d1, d2 in zip(model.parameters(), direction1, direction2):\n",
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" p.data.add_(x[i]*d1 + y[j]*d2)\n",
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" \n",
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" # Compute the loss\n",
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" outputs = model(**inputs, labels=inputs['input_ids'])\n",
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" Z[i, j] = outputs.loss.item()\n",
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" \n",
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" # Revert the model parameters\n",
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" for p, d1, d2 in zip(model.parameters(), direction1, direction2):\n",
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" p.data.sub_(x[i]*d1 + y[j]*d2)\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "4Wud37sZa1Y7",
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"outputId": "c4e9c839-2938-4df8-c54f-96f5792cc0ed"
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},
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"100%|██████████| 20/20 [12:42<00:00, 38.11s/it]\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# Create 3D plot\n",
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"fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y, \n",
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" showscale=False,)])\n",
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"fig.update_layout(\n",
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" title=\"GPT-2's Loss Landscape\",\n",
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" autosize=True,\n",
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" width=1000,\n",
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" height=600,\n",
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" # scene=dict(\n",
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" # xaxis=dict(visible=False),\n",
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" # yaxis=dict(visible=False),\n",
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" # zaxis=dict(visible=False),\n",
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" # )\n",
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")\n",
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"fig.show()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 617
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},
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"id": "5zcGwU4ji67L",
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"outputId": "f149f7eb-abfb-4b06-aead-ace3772c5379"
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},
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"execution_count": 32,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<html>\n",
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"<head><meta charset=\"utf-8\" /></head>\n",
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"<body>\n",
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" <div> <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script> <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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