From d6a0048012fdd37353906db6fb3ee168e8e0f41b Mon Sep 17 00:00:00 2001 From: zhangir-azerbayev Date: Fri, 27 Oct 2023 00:21:41 -0400 Subject: [PATCH] updated analysis --- analysis/analysis.ipynb | 243 -- .../data-constrained-scaling.html | 14 + analysis/hf_reanalysis/reanalysis.ipynb | 2817 +++++++++++++++++ analysis/scaling/analysis.ipynb | 244 ++ analysis/{ => scaling}/pile-scaling-0.3.csv | 0 analysis/scaling/pile-scaling-0.4.csv | 6 + analysis/scaling/pile-scaling-0.5.csv | 8 + analysis/{ => scaling}/scaling-0.3.csv | 0 8 files changed, 3089 insertions(+), 243 deletions(-) delete mode 100644 analysis/analysis.ipynb create mode 100644 analysis/hf_reanalysis/data-constrained-scaling.html create mode 100644 analysis/hf_reanalysis/reanalysis.ipynb create mode 100644 analysis/scaling/analysis.ipynb rename analysis/{ => scaling}/pile-scaling-0.3.csv (100%) create mode 100644 analysis/scaling/pile-scaling-0.4.csv create mode 100644 analysis/scaling/pile-scaling-0.5.csv rename analysis/{ => scaling}/scaling-0.3.csv (100%) diff --git a/analysis/analysis.ipynb b/analysis/analysis.ipynb deleted file mode 100644 index f77978747..000000000 --- a/analysis/analysis.ipynb +++ /dev/null @@ -1,243 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "823a8645", - "metadata": {}, - "outputs": [], - "source": [ - "import csv\n", - "import numpy as np\n", - "from scipy.optimize import fmin_l_bfgs_b\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from scipy.special import logsumexp as lse\n", - "plt.style.use('ggplot')\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "38e11773", - "metadata": {}, - "outputs": [], - "source": [ - "BATCH_SIZE = 2**21\n", - "\n", - "def compute_residuals(params, N, D, L):\n", - " E, A, alpha, B, beta = params\n", - " return E + A/(N**alpha) + B/(D**beta) - L\n", - "\n", - "def compute_loss(params, N, D):\n", - " E, A, alpha, B, beta = params\n", - " return E + A/(N**alpha) + B/(D**beta)\n", - "\n", - "param_key = {\n", - " \"70M\": 70_426_624,\n", - " \"160M\": 162_322_944,\n", - " \"410M\": 405_334_016,\n", - " \"1-4B\": 1_414_647_808,\n", - " \"2-8B\": 2_775_208_960,\n", - " \"6-9B\": 6_857_302_016,\n", - " \"12B\": 11_846_072_320\n", - "}\n", - "\n", - "def column_rename(name: str):\n", - " for x in [*param_key.keys(), \"Step\"]:\n", - " if x in name:\n", - " return x\n", - " \n", - " raise ValueError " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e0a6ea25", - "metadata": {}, - "outputs": [], - "source": [ - "def scaling_scatter(N, N_labels, D, L, params=None):\n", - " if params is not None:\n", - " # if params, fit excess risk instead of loss\n", - " entropy = params[0]\n", - " L = L - entropy\n", - " params[0] = 0\n", - " ylabel = \"Excess risk\"\n", - " else:\n", - " ylabel = \"Loss\"\n", - " \n", - " plt.figure(figsize=(10, 6))\n", - "\n", - " unique_labels = np.unique(N_labels)\n", - " for label in unique_labels:\n", - " idx = np.where(N_labels == label)\n", - " plt.scatter(D[idx], L[idx], label=label)\n", - " \n", - " if params is not None:\n", - " D_values = np.logspace(np.log10(min(D)), np.log10(max(D)), 100) # Create an array of D values to plot\n", - " for label in unique_labels:\n", - " num_params = param_key[label]\n", - " losses = compute_loss(params, num_params, D_values)\n", - " plt.plot(D_values, losses, label=None, linestyle='--')\n", - " \n", - " # efficient frontier\n", - " E, A, alpha, B, beta = params\n", - " N_opt = np.power((alpha*A)/(beta*B) * np.power(D_values, beta), 1/alpha)\n", - " efficient_loss = compute_loss(params, N_opt, D_values)\n", - " plt.plot(D_values, efficient_loss, label='Efficient frontier', linestyle='dotted')\n", - " \n", - " E, A, alpha, B, beta = tuple(r'{' + f'{x:.2f}' r'}' for x in params)\n", - " plt.text(\n", - " D_values[0], min(L), \n", - " f'$L = {entropy:.2f} + {A}/N^{alpha} + {B}/D^{beta}$',\n", - " verticalalignment='bottom', horizontalalignment='left'\n", - " )\n", - " \n", - "\n", - " plt.xscale('log')\n", - " plt.yscale('log')\n", - " plt.xlabel('Tokens (D)')\n", - " plt.ylabel(ylabel)\n", - " plt.legend(title='N_label')\n", - " plt.title('Scatter Plot of Loss vs Tokens')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8a6b6dae", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(4096.0, '1-4B', 1.5263808965682983), (8192.0, '1-4B', 1.4269617795944214), (1536.0, '160M', 1.8795303106307983), (2048.0, '160M', 1.8067480325698853), (3072.0, '160M', 1.7305755615234375), (4096.0, '160M', 1.6863418817520142), (2048.0, '410M', 1.7620457410812378), (3072.0, '410M', 1.6689311265945437), (4096.0, '410M', 1.6168882846832275), (6144.0, '410M', 1.552321434020996), (1024.0, '70M', 2.106158971786499), (1536.0, '70M', 2.0132057666778564), (2048.0, '70M', 1.953058362007141), (3072.0, '70M', 1.8852198123931885)]\n" - ] - } - ], - "source": [ - "df = pd.read_csv(\"scaling-0.3.csv\")\n", - "\n", - "cols_to_drop = [col for col in df.columns if 'MIN' in col or 'MAX' in col]\n", - "df.drop(columns=cols_to_drop, inplace=True)\n", - "\n", - "df = df.rename(columns=column_rename)\n", - "\n", - "df = df.groupby(df.columns, axis=1).first()\n", - "\n", - "melted_df = df.melt(id_vars=['Step'], value_vars=['1-4B', '160M', '410M', '70M'], \n", - " var_name='Column_name', value_name='Value')\n", - "filtered_df = melted_df.dropna(subset=['Value'])\n", - "\n", - "# Convert to triples\n", - "triples = list(filtered_df.itertuples(index=False, name=None))\n", - "\n", - "N = np.array([param_key[x[1]] for x in triples])\n", - "N_labels = np.array([x[1] for x in triples])\n", - "D = np.array([x[0]*BATCH_SIZE for x in triples])\n", - "L = np.array([x[2] for x in triples])\n", - "\n", - "print(triples)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d45b2fee", - "metadata": {}, - "outputs": [ - { - "data": { - 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scaling_scatter(N, N_labels, D, L)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "45bb0664", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimized parameters: E = 0.7096414667491263, A = 8085.886889867782, alpha = 0.5569057167389312, B = 135.65496347130258, beta = 0.22619296807762118\n", - "Cost: 0.0009669666791482262\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from scipy.optimize import least_squares\n", - "\n", - "initial_guess = [1.0, 400, 0.5, 400, 0.5]\n", - "\n", - "result = least_squares(compute_residuals, initial_guess, args=(N, D, L))\n", - "\n", - "params = result.x\n", - "E_opt, A_opt, alpha_opt, B_opt, beta_opt = params\n", - "cost = result.cost\n", - "\n", - "print(f\"Optimized parameters: E = {E_opt}, A = {A_opt}, alpha = {alpha_opt}, B = {B_opt}, beta = {beta_opt}\")\n", - "print(f\"Cost: {cost}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9433f2a2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scaling_scatter(N, N_labels, D, L, params=params)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/analysis/hf_reanalysis/data-constrained-scaling.html b/analysis/hf_reanalysis/data-constrained-scaling.html new file mode 100644 index 000000000..0338f87a0 --- /dev/null +++ b/analysis/hf_reanalysis/data-constrained-scaling.html @@ -0,0 +1,14 @@ + + + +
+
+ + \ No newline at end of file diff --git a/analysis/hf_reanalysis/reanalysis.ipynb b/analysis/hf_reanalysis/reanalysis.ipynb new file mode 100644 index 000000000..6c45bd917 --- /dev/null +++ b/analysis/hf_reanalysis/reanalysis.ipynb @@ -0,0 +1,2817 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "61210e9f", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List, Dict\n", + "\n", + "import numpy as np\n", + "import plotly.graph_objects as go" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "34a421d7", + "metadata": {}, + "outputs": [], + "source": [ + "### DATA POINTS ###\n", + "\n", + "# Read from the Log / TensorBoard of \"val\" designated folder of each model on the hub\n", + "# losses of 0 are skipped as these models haven't finished training\n", + "NAMES_TO_VAL_LOSSES = {\n", + " \"2b84b4b\": 3.265024E+00,\n", + " \"2b88b4b\": 2.995093E+00,\n", + " \"2b812b4b\": 2.884387E+00,\n", + " \"2b816b4b\": 2.823208E+00,\n", + " \"2b824b4b\": 2.752407E+00,\n", + " \"2b832b4b\": 2.722962E+00,\n", + " \"2b840b4b\": 2.706547E+00,\n", + "\n", + " # \"2b855b4bc4subopt\": 2.700284E+00,\n", + " # \"2b855b9bsubopt\": 2.632432E+00,\n", + " # \"2b855b9bc4opt\": 2.609009E+00, # Optimal shape; Slightly better loss than the same seed 2b855b9bc4\n", + " # \"2b855b4bc4opt1\": 2.689857E+00, # Not quite optimal shape, but already better loss\n", + " # \"2b855b4bc4opt2\": 2.692264E+00, # Not enough regularization so may not hold anymore\n", + "\n", + " \"2b855b11bc4seed1\": 2.599277E+00,\n", + " \"2b855b11bc4seed2\": 2.599763E+00,\n", + " \"2b855b11bc4seed3\": 2.600254E+00,\n", + " \"2b855b11bc4seed4\": 2.598042E+00,\n", + "\n", + " \"2b855b14bc4seed1\": 2.592292E+00,\n", + " \"2b855b14bc4seed2\": 2.590478E+00,\n", + " \"2b855b14bc4seed3\": 2.588968E+00,\n", + " \"2b855b14bc4seed4\": 2.591426E+00,\n", + "\n", + " \"2b855b18bc4seed1\": 2.582283E+00,\n", + " \"2b855b18bc4seed2\": 2.581189E+00,\n", + " \"2b855b18bc4seed3\": 2.584074E+00,\n", + " \"2b855b18bc4seed4\": 2.581949E+00,\n", + "\n", + " \"2b855b1b25c4seed1\": 3.425332E+00,\n", + " \"2b855b1b25c4seed2\": 3.422794E+00,\n", + " \"2b855b1b25c4seed3\": 3.425154E+00,\n", + " \"2b855b1b25c4seed4\": 3.428524E+00,\n", + "\n", + "\n", + " \"2b855b28bc4seed1\": 2.575713E+00,\n", + " \"2b855b28bc4seed2\": 2.576707E+00,\n", + " \"2b855b28bc4seed3\": 2.574265E+00,\n", + " \"2b855b28bc4seed4\": 2.575176E+00,\n", + "\n", + " \"2b855b4bc4seed1\": 2.693317E+00,\n", + " \"2b855b4bc4seed2\": 2.691274E+00,\n", + " \"2b855b4bc4seed3\": 2.692641E+00,\n", + " \"2b855b4bc4seed4\": 2.692532E+00,\n", + "\n", + " \"2b855b55bc4seed1\": 2.569702E+00,\n", + " \"2b855b55bc4seed2\": 2.568221E+00,\n", + " \"2b855b55bc4seed3\": 2.569587E+00,\n", + " \"2b855b55bc4seed4\": 2.570589E+00,\n", + "\n", + " \"2b855b9bc4seed1\": 2.609228E+00,\n", + " \"2b855b9bc4seed2\": 2.605090E+00,\n", + " \"2b855b9bc4seed3\": 2.605155E+00,\n", + " \"2b855b9bc4seed4\": 2.606114E+00,\n", + "\n", + " # \"2b855b55bperplexity\": 2.604075E+00\n", + " # \"2b855b55bdedup\": 2.758145E+00,\n", + " # \"2b855b55bc4perplexity25\": 2.740080E+00,\n", + " # \"2b855b55bc4perplexity50\": 2.659265E+00\n", + "\n", + " # \"2b884b84bperplexity25\": 2.701157E+00\n", + " # \"2b884b84bperplexity50\": 2.636081E+00\n", + " # \"2b884b84bperplexity2575\": 2.612976E+00\n", + "\n", + " \"2b855b1b25\": 3.422766E+00,\n", + " \"2b855b4b\": 2.696432E+00,\n", + " \"2b855b9b\": 2.611045E+00,\n", + " \"2b855b11b\": 2.598793E+00,\n", + " \"2b855b14b\": 2.589427E+00,\n", + " \"2b855b18b\": 2.584592E+00,\n", + " \"2b855b28b\": 2.579361E+00,\n", + " \"2b855b55b\": 2.574117E+00,\n", + "\n", + " \"4b212b12b\": 2.848890E+00,\n", + " \"4b224b12b\": 2.691483E+00,\n", + " \"4b236b12b\": 2.618343E+00,\n", + " \"4b248b12b\": 2.577519E+00,\n", + " \"4b260b12b\": 2.560390E+00,\n", + " \"4b272b12b\": 2.539524E+00,\n", + "\n", + " # \"4b284b12bsubopt\": 2.562510E+00,\n", + " # \"4b284b6bsubopt\": 2.650107E+00, # Probably worse cuz loss midway goes up (verify in graph)\n", + " # \"4b284b12bopt\": 2.525244E+00, # Not quite shaped correctly\n", + " # \"4b284b6bopt1\": 2.641864E+00 # Too many excess params resulting val loss increasing midway, i.e. needs more regularization\n", + " \"4b284b12bc4opt2\": 2.520537E+00, # Params: 3.089856e9\n", + "\n", + " \"4b284b12bseed1\": 2.538173E+00,\n", + " \"4b284b12bc4seed2\": 2.526042E+00,\n", + " \"4b284b12bc4seed3\": 2.537281E+00,\n", + " \"4b284b12bc4seed4\": 2.527790E+00,\n", + "\n", + " \"4b284b17bc4seed1\": 2.549558E+00,\n", + " \"4b284b17bc4seed2\": 2.503345E+00,\n", + " \"4b284b17bc4seed3\": 2.499933E+00,\n", + " \"4b284b17bc4seed4\": 2.496040E+00,\n", + "\n", + " \"4b284b1b9c4seed1\": 3.118388E+00,\n", + " \"4b284b1b9c4seed2\": 3.082603E+00,\n", + " \"4b284b1b9c4seed3\": 3.111291E+00,\n", + " \"4b284b1b9c4seed4\": 3.146578E+00,\n", + "\n", + " \"4b284b21bc4seed1\": 2.502885E+00,\n", + " \"4b284b21bc4seed2\": 2.480593E+00,\n", + " \"4b284b21bc4seed3\": 2.491781E+00,\n", + " \"4b284b21bc4seed4\": 2.477816E+00,\n", + "\n", + " \"4b284b28bc4seed1\": 2.479697E+00,\n", + " \"4b284b28bc4seed2\": 2.482804E+00,\n", + " \"4b284b28bc4seed3\": 2.477427E+00,\n", + " \"4b284b28bc4seed4\": 2.494708E+00,\n", + "\n", + " \"4b284b42bc4seed1\": 2.470463E+00,\n", + " \"4b284b42bc4seed2\": 2.491261E+00,\n", + " \"4b284b42bc4seed3\": 2.469242E+00,\n", + " \"4b284b42bc4seed4\": 2.469873E+00,\n", + "\n", + " \"4b284b84bc4v2seed1\": 2.464149E+00,\n", + " \"4b284b84bc4v2seed2\": 2.461136E+00,\n", + " \"4b284b84bc4v2seed3\": 2.480773E+00,\n", + " \"4b284b84bc4v2seed4\": 2.476263E+00,\n", + "\n", + " \"4b284b6bseed1\": 2.602034E+00,\n", + " \"4b284b6bseed2\": 2.604310E+00,\n", + " \"4b284b6bseed3\": 2.609000E+00,\n", + " \"4b284b6bseed4\": 2.614594E+00,\n", + "\n", + " # \"4b284b84b10c4py\": 2.813708E+00,\n", + " # \"4b284b84b20c4py\": 2.683304E+00\n", + " # \"4b284b84b30c4py\": 2.613236E+00,\n", + " # \"4b284b84b40c4py\": 2.568575E+00,\n", + " # \"4b284b84b50c4py\": 2.535289E+00,\n", + " # \"4b284b84b60c4py\": 2.509820E+00,\n", + " # \"4b284b84b70c4py\": 2.491829E+00,\n", + " # \"4b284b84b80c4py\": 2.479359E+00,\n", + " # \"4b284b84b90c4py\": 2.462060E+00,\n", + "\n", + " # \"4b284bc4perplexity\": 2.496036E+00,\n", + " # \"4b284bc4perplexity25\": 2.624402E+00,\n", + " # \"4b284bc4perplexity50\": 2.539634E+00\n", + " # \"4b284bc4dedup\": 2.687236E+00,\n", + "\n", + " \"4b284b1b9\": 3.276627E+00,\n", + " \"4b284b6b\": 2.639410E+00,\n", + " \"4b284b12b\": 2.526961E+00,\n", + " \"4b284b17b\": 2.485060E+00,\n", + " \"4b284b21b\": 2.471375E+00,\n", + " \"4b284b28b\": 2.467156E+00,\n", + " \"4b284b42b\": 2.470016E+00,\n", + " \"4b284b84b\": 2.456530E+00,\n", + "\n", + "\n", + " \"8b7178b25bopt\": 2.358995E+00,\n", + "\n", + " # \"8b7178b4b\": 3.131161E+00,\n", + " \"8b7178b13b\": 2.451606E+00,\n", + " \"8b7178b25b\": 2.376484E+00,\n", + " \"8b7178b35b\": 2.360029E+00,\n", + " \"8b7178b44b\": 2.351177E+00,\n", + " \"8b7178b58b\": 2.348107E+00,\n", + " \"8b7178b88b\": 2.337275E+00,\n", + " \"8b7178b178b\": 2.336741E+00,\n", + "\n", + " \"7m100m100m\": 8.102005E+00, # MassiveW optimal one\n", + " \"7m200m100m\": 7.362360E+00,\n", + "\n", + " \"1b1100m100m\": 6.611002E+00,\n", + " \"2b8100m100m\": 7.518863E+00,\n", + "\n", + " \"14m100m100m\": 7.278144E+00, # C4 optimal one\n", + " \"20m400m400m\": 6.096268E+00,\n", + " \"25m400m400m\": 5.794130E+00, # MassiveW optimal one\n", + "\n", + " \"25m200m100m\": 6.252892E+00,\n", + " \"25m400m100m\": 5.799587E+00,\n", + " \"25m800m100m\": 5.284175E+00,\n", + "\n", + " \"14m200m100m\": 6.664138E+00,\n", + " \"14m400m100m\": 6.273184E+00,\n", + " \"14m800m100m\": 5.816824E+00,\n", + "\n", + " \"14m1b5100m\": 5.364749E+00,\n", + " \"14m2b7100m\": 4.992776E+00,\n", + " \"14m3b9100m\": 4.741132E+00,\n", + " \"14m5b9100m\": 4.546227E+00,\n", + " \"14m7b5100m\": 4.472296E+00,\n", + " \"14m14b100m\": 4.383392E+00,\n", + " \"14m20b100m\": 4.510410E+00, ### DOUBLE DESCENT ###\n", + " \"14m91b100m\": 4.396074E+00,\n", + " \"14m174b100m\": 4.359691E+00,\n", + " \"14m300b100m\": 4.369110E+00, ### DOUBLE DESCENT ###\n", + " \"14m600b100m\": 4.331469E+00,\n", + " \"14m900b100m\": 4.304018E+00,\n", + "\n", + " \"44m200m100m\": 6.189970E+00,\n", + " \"44m400m100m\": 5.648868E+00,\n", + "\n", + " \"44m1b5100m\": 4.387672E+00,\n", + " \"44m2b7100m\": 4.113597E+00,\n", + " \"44m3b9100m\": 3.988205E+00,\n", + " \"44m5b9100m\": 3.894340E+00,\n", + " \"44m7b5100m\": 3.854261E+00,\n", + " \"44m14b100m\": 3.807672E+00,\n", + " \"44m20b100m\": 3.929030E+00, ### DOUBLE DESCENT ###\n", + " \"44m32b100m\": 3.892240E+00,\n", + " \"44m91b100m\": 3.836200E+00,\n", + " \"44m174b100m\": 3.812315E+00,\n", + "\n", + " \"83m1b51b5\": 4.362080E+00, # MassiveW optimal one\n", + "\n", + " \"196m1b51b5\": 3.929866E+00, # C4 optimal one\n", + "\n", + " \"1b11b51b5\": 3.680017E+00,\n", + " \"1b11b5400m\": 3.704141E+00,\n", + " \"1b11b5100m\": 3.819042E+00,\n", + "\n", + " \"1b12b7100m\": 4.097820E+00,\n", + " \"1b13b9100m\": 4.834920E+00,\n", + " \"1b15b9100m\": 6.013517E+00,\n", + " \"1b17b5100m\": 6.742290E+00,\n", + "\n", + " \"619m2b72b7\": 3.415532E+00,\n", + " \"619m2b71b5\": 3.409955E+00,\n", + " \"619m2b7400m\": 3.449187E+00,\n", + " \"619m2b7100m\": 3.829336E+00,\n", + "\n", + " \"619m1b5100m\": 3.784308E+00,\n", + " \"619m3b9100m\": 4.152834E+00,\n", + " \"619m5b9100m\": 4.668028E+00,\n", + " \"619m7b5100m\": 5.001218E+00,\n", + "\n", + " \"421m3b93b9\": 3.348126E+00,\n", + " \"421m3b91b5\": 3.354960E+00,\n", + " \"421m3b9400m\": 3.404755E+00,\n", + " \"421m3b9100m\": 3.898620E+00,\n", + "\n", + " \"421m1b5100m\": 3.797898E+00,\n", + " \"421m2b7100m\": 3.747371E+00,\n", + " \"421m5b9100m\": 4.169935E+00,\n", + " \"421m7b5100m\": 4.376178E+00,\n", + "\n", + " \"280m5b95b9\": 3.319542E+00,\n", + " \"280m5b91b5\": 3.330755E+00,\n", + " \"280m5b9400m\": 3.386692E+00,\n", + " \"280m5b9100m\": 3.882211E+00,\n", + "\n", + " \"280m1b5100m\": 3.877228E+00,\n", + " \"280m2b7100m\": 3.727590E+00,\n", + " \"280m3b9100m\": 3.754965E+00,\n", + " \"280m7b5100m\": 3.993410E+00,\n", + " \"280m91b400m\": 3.312059E+00,\n", + "\n", + " \"220m7b57b5\": 3.310159E+00,\n", + " \"220m7b51b5\": 3.325092E+00,\n", + " \"220m7b5400m\": 3.374362E+00,\n", + " \"220m7b5100m\": 3.848433E+00,\n", + "\n", + " \"220m1b5100m\": 3.932167E+00,\n", + " \"220m2b7100m\": 3.752793E+00,\n", + " \"220m3b9100m\": 3.721901E+00,\n", + " \"220m5b9100m\": 3.780970E+00,\n", + "\n", + " \"220m3b9100mdedup\": 3.863982E+00,\n", + "\n", + " \"146m14b14b\": 3.319729E+00,\n", + " \"146m14b1b5\": 3.334061E+00,\n", + " \"146m14b400m\": 3.390927E+00,\n", + " \"146m14b100m\": 3.801428E+00, ### DOUBLE DESCENT\n", + "\n", + " \"146m60b400m\": 3.340250E+00,\n", + " \"146m91b400m\": 3.311519E+00,\n", + " \"146m174b400m\": 3.277013E+00,\n", + "\n", + " \"146m1b5100m\": 4.033501E+00,\n", + " \"146m2b7100m\": 3.807640E+00,\n", + " \"146m3b9100m\": 3.732758E+00,\n", + " \"146m5b9100m\": 3.718068E+00, # Best model for 100M; to FLOPS: 6 * 146M * 5.9B = 5.0622e+18\n", + " \"146m7b5100m\": 3.735044E+00,\n", + " \"146m20b100m\": 3.756257E+00,\n", + " \"146m32b100m\": 3.793078E+00,\n", + " \"146m60b100m\": 3.846421E+00,\n", + " \"146m91b100m\": 3.862952E+00,\n", + " \"146m174b100m\": 3.897418E+00,\n", + "\n", + " \"146m1b5100mdedup\": 4.169387E+00,\n", + " \"146m2b7100mdedup\": 3.949182E+00,\n", + " \"146m3b9100mdedup\": 3.871975E+00,\n", + " \"146m5b9100mdedup\": 3.860607E+00,\n", + " \"146m7b5100mdedup\": 3.883653E+00,\n", + " \"146m14b100mdedup\": 3.963449E+00, ### WEAK DOUBLE DESCENT ###\n", + " \"146m32b100mdedup\": 3.962464E+00,\n", + " \"146m60b100mdedup\": 4.017775E+00,\n", + " \"146m91b100mdedup\": 4.065124E+00,\n", + " \"146m174b100mdedup\": 4.094653E+00,\n", + "\n", + " \"83m20b20b\": 3.608018E+00,\n", + " \"83m20b1b5\": 3.618937E+00,\n", + " \"83m20b400m\": 3.651057E+00,\n", + " \"83m20b100m\": 3.790125E+00, ### DOUBLE DESCENT ###\n", + "\n", + " \"83m400m100m\": 5.488318E+00,\n", + " \"83m1b5100m\": 4.206960E+00,\n", + " \"83m2b7100m\": 3.953894E+00,\n", + " \"83m3b9100m\": 3.844048E+00,\n", + " \"83m5b9100m\": 3.774708E+00,\n", + " \"83m7b5100m\": 3.747734E+00,\n", + " \"83m14b100m\": 3.725231E+00,\n", + " \"83m32b100m\": 3.747091E+00,\n", + " \"83m60b100m\": 3.747091E+00,\n", + " \"83m91b100m\": 3.743505E+00,\n", + " \"83m174b100m\": 3.734349E+00,\n", + " \"83m300b100m\": 3.868704E+00,\n", + "\n", + " \"83m14b100mdedup\": 3.872943E+00,\n", + "\n", + " \"2b84b84b8\": 3.236253E+00,\n", + " \"2b84b81b5\": 3.244249E+00,\n", + " \"2b84b8400m\": 3.378791E+00,\n", + " \"2b84b8100m\": 5.340819E+00,\n", + "\n", + " \"1b58b88b8\": 3.022525E+00,\n", + " \"1b58b84b8\": 3.022745E+00,\n", + " \"1b58b81b5\": 3.070052E+00,\n", + " \"1b58b8400m\": 3.479142E+00,\n", + " \"1b58b8100m\": 7.679766E+00,\n", + "\n", + " \"1b112b12b\": 2.925644E+00,\n", + " \"1b112b4b8\": 2.927054E+00,\n", + " \"1b112b1b5\": 2.996886E+00,\n", + " \"1b112b400m\": 3.547616E+00,\n", + " \"1b112b100m\": 8.136283E+00,\n", + "\n", + " \"619m22b22b\": 2.900172E+00,\n", + " \"619m22b4b8\": 2.912009E+00,\n", + " \"619m22b1b5\": 2.973545E+00, # Best model for 1.5b; To FLOPS: 6 * 619M * 22B = 8.1708e+19\n", + " \"619m22b400m\": 3.441785E+00,\n", + " \"619m22b100m\": 6.840158E+00,\n", + "\n", + " \"619m60b400m\": 3.688865E+00,\n", + " \"619m91b400m\": 3.756154E+00,\n", + "\n", + " \"421m32b32b\": 2.912103E+00,\n", + " \"421m32b4b8\": 2.926206E+00,\n", + " \"421m32b1b5\": 2.980892E+00,\n", + " \"421m32b400m\": 3.366502E+00,\n", + " \"421m32b100m\": 5.783605E+00,\n", + "\n", + " \"421m60b400m\": 3.431953E+00,\n", + " \"421m91b400m\": 3.465185E+00,\n", + "\n", + " \"421m91b1b5\": 2.917592E+00,\n", + " \"421m250b1b5\": 2.890927E+00,\n", + " \"421m300b1b5\": 2.884335E+00,\n", + "\n", + " \"221m60b60b\": 2.981407E+00,\n", + " \"221m60b4b8\": 2.993185E+00,\n", + " \"221m60b1b5\": 3.032319E+00,\n", + " \"221m60b400m\": 3.258704E+00,\n", + "\n", + " \"221m3b9400m\": 3.525297E+00,\n", + " \"221m32b400m\": 3.260515E+00,\n", + " \"221m91b400m\": 3.261209E+00,\n", + " \"221m174b400m\": 3.260720E+00,\n", + " \"221m300b400m\": 3.257426E+00,\n", + " \"221m600b400m\": 3.261908E+00,\n", + "\n", + " \"8b712b12b\": 3.140306E+00,\n", + " \"8b712b1b5\": 3.063302E+00,\n", + " \"8b712b400m\": 4.538278E+00,\n", + " \"8b712b100m\": 7.385309E+00,\n", + "\n", + " \"3b926b26b\": 2.916993E+00,\n", + " \"3b926b12b\": 2.947956E+00,\n", + " \"3b926b1b5\": 3.020170E+00,\n", + " \"3b926b400m\": 0,\n", + " \"3b926b100m\": 0,\n", + "\n", + " \"2b836b36b\": 2.618932E+00,\n", + " \"2b836b12b\": 2.636968E+00,\n", + " \"2b836b1b5\": 3.065092E+00,\n", + " \"2b836b400m\": 6.548141E+00,\n", + " \"2b836b100m\": 1.069564E+01,\n", + "\n", + " \"2b246b46b\": 2.603919E+00,\n", + " \"2b246b12b\": 2.638341E+00,\n", + " \"2b246b1b5\": 3.029574E+00,\n", + " \"2b246b400m\": 5.896346E+00,\n", + " \"2b246b100m\": 1.087606E+01,\n", + "\n", + " \"1b566b66b\": 2.592968E+00,\n", + " \"1b566b12b\": 2.626113E+00,\n", + " \"1b566b1b5\": 2.992921E+00,\n", + " \"1b566b400m\": 0,\n", + " \"1b566b100m\": 1.101838E+01,\n", + "\n", + " \"1b191b91b\": 2.636805E+00,\n", + " \"1b191b12b\": 2.665932E+00,\n", + " \"1b191b400m\": 4.618763E+00,\n", + " \"1b191b1b5\": 2.939082E+00,\n", + "\n", + " \"574m174b174b\": 2.708514E+00,\n", + " \"574m174b12b\": 2.738098E+00,\n", + " \"574m174b1b5\": 2.895393E+00,\n", + " \"574m174b400m\": 3.752652E+00,\n", + "\n", + " \"220m200b1b5\": 0,\n", + " \"220m250b1b5\": 0,\n", + " \"220m300b1b5\": 2.958765E+00,\n", + " \"220m600b1b5\": 2.937761E+00,\n", + " \"220m900b1b5\": 0,\n", + "\n", + " \"574m200b1b5\": 0,\n", + " \"574m250b1b5\": 0,\n", + " \"574m300b1b5\": 2.881076E+00,\n", + " \"574m600b1b5\": 2.863888E+00,\n", + "\n", + " \"1b1174b1b5\": 2.974257E+00,\n", + " \"1b1250b1b5\": 2.986145E+00,\n", + "\n", + " \"1b591b1b5\": 3.034805E+00,\n", + " \"1b5174b1b5\": 3.099245E+00,\n", + "\n", + " \"2b266b1b5\": 0,\n", + " \"2b291b1b5\": 3.176858E+00,\n", + " \"2b2174b1b5\": 3.351022E+00,\n", + "\n", + " \"8b720b1b5\": 3.084724E+00,\n", + " \"2b877b1b5\": 3.297776E+00,\n", + "\n", + "}\n", + "\n", + "TOKENS_MAP = {\n", + " \"100m\": 0.1,\n", + " \"200m\": 0.2,\n", + " \"400m\": 0.4,\n", + " \"800m\": 0.8,\n", + " \"1b25\": 1.25,\n", + " \"1b5\": 1.5,\n", + " \"1b9\": 1.9,\n", + " \"2b7\": 2.7,\n", + " \"2b8\": 2.8,\n", + " \"3b9\": 3.9,\n", + " \"4b\": 4,\n", + " \"5b9\": 5.9,\n", + " \"7b5\": 7.5,\n", + " \"13b\": 13,\n", + " \"14b\": 14,\n", + " \"20b\": 20,\n", + " \"174b\": 174,\n", + " \"178b\": 178,\n", + "}\n", + "\n", + "PARAMS_MAP = {\n", + " \"7m\": 7.098752E+06,\n", + " \"14m\": 1.41E+07,\n", + " \"20m\": 19.703712E+06,\n", + " \"25m\": 35.5E+06,\n", + " \"44m\": 4.4E+07,\n", + " \"83m\": 8.27E+07,\n", + " \"146m\": 1.465E+08,\n", + " \"196m\": 201.236224E+06,\n", + " \"220m\": 2.205E+08,\n", + " \"221m\": 2.205E+08,\n", + " \"280m\": 2.81E+08,\n", + " \"421m\": 4.212E+08,\n", + " \"574m\": 573.7E+06,\n", + " \"619m\": 6.187E+08,\n", + " \"1b1\": 1.0963E+09,\n", + " \"1b5\": 1.5173E+09,\n", + " \"2b2\": 2.160013824E+09,\n", + " \"2b8\": 2.81E+09,\n", + " \"3b8\": 3.581186048E+09,\n", + " \"3b9\": 3.89971072E+09,\n", + " \"4b2\": 4.2465E+09,\n", + " \"8b7\": 8.67E+09,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "67588239", + "metadata": {}, + "outputs": [], + "source": [ + "def scaling_scatter(\n", + " runs: List[Dict[str, int]],\n", + " x_key: str, \n", + " y_key: str, \n", + " z_key: str = None,\n", + " color_key: str = None,\n", + " savepath: str = None,\n", + "):\n", + " x = runs[x_key]\n", + " y = runs[y_key]\n", + " if z_key:\n", + " z = runs[z_key]\n", + " scatter = go.Scatter3d\n", + " scatter_kwargs = dict(x=x, y=y, z=z)\n", + " else:\n", + " z = None\n", + " scatter = go.Scatter\n", + " scatter_kwargs = dict(x=x, y=y)\n", + " \n", + " if color_key:\n", + " color_variable = runs[color_key]\n", + " else:\n", + " color_variable = None\n", + " \n", + " fig = go.Figure(\n", + " data=[\n", + " scatter(\n", + " **scatter_kwargs,\n", + " mode='markers',\n", + " marker = dict(\n", + " size=8,\n", + " color=color_variable,\n", + " colorscale='Viridis',\n", + " colorbar=dict(title=color_key),\n", + " opacity=0.8,\n", + " )\n", + " )\n", + " ]\n", + " )\n", + " \n", + " fig.update_layout(\n", + " scene=dict(\n", + " xaxis=dict(type='log', title=x_key),\n", + " yaxis=dict(type='log', title=y_key),\n", + " zaxis=dict(type='log', title=z_key),\n", + " ),\n", + " # width=800,\n", + " # height=600,\n", + " )\n", + " fig.write_html(savepath)\n", + " fig.show()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "969c0e98", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + 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TOKENS_MAP:\n", + " tokens = TOKENS_MAP[tokens]\n", + " else:\n", + " tokens = float(tokens.replace(\"b\", \".\").strip(\".\"))\n", + "\n", + " return tokens * 1e9 # Defined in billions\n", + "\n", + "for name, loss in NAMES_TO_VAL_LOSSES.items():\n", + " if loss != 0 and not(name.endswith(\"dedup\")):# and not(\"seed\" in name) and not(\"opt\" in name):\n", + " name = name.replace(\"op2\", \"\").replace(\"seed1\", \"\").replace(\"seed2\", \"\").replace(\"seed3\", \"\").replace(\"seed4\", \"\").replace(\"opt1\", \"\").replace(\"opt\", \"\").replace(\"c4\", \"\").replace(\"v2\", \"\")\n", + " p, tok_start_idx = get_params(name)\n", + " ut, tok_end_idx = get_unique_tokens(name)\n", + " t = get_tokens(name[tok_start_idx:tok_end_idx])\n", + "\n", + " model_params.append(p)\n", + " unique_tokens.append(ut)\n", + " tokens.append(t)\n", + " losses.append(loss)\n", + "\n", + " names.append(name)\n", + "\n", + " # print(name, p, t, ut, loss)\n", + "\n", + "runs = {\n", + " 'N': np.array(model_params),\n", + " 'D': np.array(unique_tokens),\n", + " 'L': np.array(losses),\n", + " 'R': np.array([x/y for x,y in zip(tokens, unique_tokens)])\n", + "}\n", + "\n", + "scaling_scatter(runs, x_key='D', y_key='N', z_key='R', color_key='L', savepath='data-constrained-scaling.html')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/analysis/scaling/analysis.ipynb b/analysis/scaling/analysis.ipynb new file mode 100644 index 000000000..03cd90e55 --- /dev/null +++ b/analysis/scaling/analysis.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e96d8415", + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "import numpy as np\n", + "from scipy.optimize import fmin_l_bfgs_b\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from scipy.special import logsumexp as lse\n", + "plt.style.use('ggplot')\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bde65749", + "metadata": {}, + "outputs": [], + "source": [ + "BATCH_SIZE = 2**21\n", + "\n", + "def compute_residuals(params, N, D, L):\n", + " E, A, alpha, B, beta = params\n", + " return E + A/(N**alpha) + B/(D**beta) - L\n", + "\n", + "def compute_loss(params, N, D):\n", + " E, A, alpha, B, beta = params\n", + " return E + A/(N**alpha) + B/(D**beta)\n", + "\n", + "param_key = {\n", + " \"70M\": 70_426_624,\n", + " \"160M\": 162_322_944,\n", + " \"410M\": 405_334_016,\n", + " \"1-4B\": 1_414_647_808,\n", + " \"2-8B\": 2_775_208_960,\n", + " \"6-9B\": 6_857_302_016,\n", + " \"12B\": 11_846_072_320\n", + "}\n", + "\n", + "def column_rename(name: str):\n", + " for x in [*param_key.keys(), \"Step\"]:\n", + " if x in name:\n", + " return x\n", + " \n", + " raise ValueError " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3f5472c3", + "metadata": {}, + "outputs": [], + "source": [ + "def scaling_scatter(N, N_labels, D, L, params=None):\n", + " if params is not None:\n", + " # if params, fit excess risk instead of loss\n", + " entropy = params[0]\n", + " L = L - entropy\n", + " params[0] = 0\n", + " ylabel = \"Excess risk\"\n", + " else:\n", + " ylabel = \"Loss\"\n", + " \n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " unique_labels = np.unique(N_labels)\n", + " for label in unique_labels:\n", + " idx = np.where(N_labels == label)\n", + " plt.scatter(D[idx], L[idx], label=label)\n", + " \n", + " if params is not None:\n", + " D_values = np.logspace(np.log10(min(D)), np.log10(max(D)), 100) # Create an array of D values to plot\n", + " for label in unique_labels:\n", + " num_params = param_key[label]\n", + " losses = compute_loss(params, num_params, D_values)\n", + " plt.plot(D_values, losses, label=None, linestyle='--')\n", + " \n", + " # efficient frontier\n", + " E, A, alpha, B, beta = params\n", + " N_opt = np.power((alpha*A)/(beta*B) * np.power(D_values, beta), 1/alpha)\n", + " efficient_loss = compute_loss(params, N_opt, D_values)\n", + " plt.plot(D_values, efficient_loss, label='Efficient frontier', linestyle='dotted')\n", + " \n", + " E, A, alpha, B, beta = tuple(r'{' + f'{x:.2f}' r'}' for x in params)\n", + " plt.text(\n", + " D_values[0], min(L), \n", + " f'$L = {entropy:.2f} + {A}/N^{alpha} + {B}/D^{beta}$',\n", + " verticalalignment='bottom', horizontalalignment='left'\n", + " )\n", + " \n", + "\n", + " plt.xscale('log')\n", + " plt.yscale('log')\n", + " plt.xlabel('Tokens (D)')\n", + " plt.ylabel(ylabel)\n", + " plt.legend(title='N_label')\n", + " plt.title('Scatter Plot of Loss vs Tokens')\n", + " plt.savefig(\"pile-scaling-0.3.png\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db65684f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(4096.0, '1-4B', 2.256822109222412), (8192.0, '1-4B', 2.110085248947144), (12288.0, '1-4B', 2.045180320739746), (1536.0, '160M', 2.7899272441864014), (2048.0, '160M', 2.693813800811768), (3072.0, '160M', 2.58536148071289), (4096.0, '160M', 2.521445035934448), (2048.0, '410M', 2.639939546585083), (3072.0, '410M', 2.496314764022827), (4096.0, '410M', 2.41260290145874), (6144.0, '410M', 2.315460205078125), (1024.0, '70M', 3.1299679279327397), (1536.0, '70M', 2.9992475509643555), (2048.0, '70M', 2.917292594909668), (3072.0, '70M', 2.8291783332824707)]\n" + ] + } + ], + "source": [ + "df = pd.read_csv(\"pile-scaling-0.3.csv\")\n", + "\n", + "cols_to_drop = [col for col in df.columns if 'MIN' in col or 'MAX' in col]\n", + "df.drop(columns=cols_to_drop, inplace=True)\n", + "\n", + "df = df.rename(columns=column_rename)\n", + "\n", + "df = df.groupby(df.columns, axis=1).first()\n", + "\n", + "melted_df = df.melt(id_vars=['Step'], value_vars=['1-4B', '160M', '410M', '70M'], \n", + " var_name='Column_name', value_name='Value')\n", + "filtered_df = melted_df.dropna(subset=['Value'])\n", + "\n", + "# Convert to triples\n", + "triples = list(filtered_df.itertuples(index=False, name=None))\n", + "\n", + "N = np.array([param_key[x[1]] for x in triples])\n", + "N_labels = np.array([x[1] for x in triples])\n", + "D = np.array([x[0]*BATCH_SIZE for x in triples])\n", + "L = np.array([x[2] for x in triples])\n", + "\n", + "print(triples)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fd5ce721", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scaling_scatter(N, N_labels, D, L)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5b95b9ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimized parameters: E = 0.8050473207999971, A = 3724.438173586794, alpha = 0.4855443635639823, B = 96.96000616194083, beta = 0.18691371714905564\n", + "Cost: 0.003908275585120159\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from scipy.optimize import least_squares\n", + "\n", + "initial_guess = [1.0, 400, 0.5, 400, 0.5]\n", + "\n", + "result = least_squares(compute_residuals, initial_guess, args=(N, D, L))\n", + "\n", + "params = result.x\n", + "E_opt, A_opt, alpha_opt, B_opt, beta_opt = params\n", + "cost = result.cost\n", + "\n", + "print(f\"Optimized parameters: E = {E_opt}, A = {A_opt}, alpha = {alpha_opt}, B = {B_opt}, beta = {beta_opt}\")\n", + "print(f\"Cost: {cost}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7dd2dd56", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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