From b6ed618517066d04d1d13e5d792bc0937c4bc015 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 24 Jul 2026 09:58:38 +0200 Subject: [PATCH 01/24] Run rollout validation for the mixture of experts at 50 GeV. --- analysis/rollout_validation.ipynb | 164 ++++++++++++++++++++++++------ 1 file changed, 135 insertions(+), 29 deletions(-) diff --git a/analysis/rollout_validation.ipynb b/analysis/rollout_validation.ipynb index 369c434..e16259f 100644 --- a/analysis/rollout_validation.ipynb +++ b/analysis/rollout_validation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "f91460f3", "metadata": {}, "outputs": [], @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "251dc1f7", "metadata": {}, "outputs": [], @@ -43,14 +43,10 @@ "from giant.analysis import RolloutVsTruth, plot_kl_bars_pl\n", "\n", "# `giant rollout` output for the shower(s) under test.\n", - "ROLLOUT_FILE = (\n", - " \"/ceph/lbogner/geant_steps/predictions/9e76bc2c-f4ef-4488-9f62-b6d14e1f298e.parquet\"\n", - ")\n", + "ROLLOUT_FILE = \"/ceph/lbogner/geant_steps/predictions/563f5ee3-507c-4f07-98ec-7b25bff7dfb2.parquet\"\n", "# Any held-out file sharing giant train's input schema (real miniCaloSim\n", "# steps) — e.g. the val split the rollout's seed events were drawn from.\n", - "TRUTH_FILE = (\n", - " \"/ceph/lbogner/geant_steps/processed/steps/gen3/schema2/pbwo4/shard-009.parquet\"\n", - ")\n", + "TRUTH_FILE = \"/ceph/lbogner/geant_steps/processed/steps/gen3/schema2/pbwo4_50gev/shard-023.parquet\"\n", "\n", "# sample_frac subsamples each side of the Tier 1-3 checks independently\n", "# (kept memory-bounded for large files); defaults to every row. Tier 4\n", @@ -91,10 +87,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "7bab2e35", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from giant.analysis import plot_marginals, plot_correlation_matrices, plot_pairwise\n", "from giant.analysis import plot_direction_alignment, plot_constraint_violations\n", @@ -104,20 +111,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "6c2958e7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = plot_marginals(SOURCE, group_by=\"energy\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "0fe70835", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = plot_marginals(SOURCE, group_by=\"pdg\")" ] @@ -201,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "9175876a", "metadata": {}, "outputs": [], @@ -217,70 +246,147 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "83e6dd0e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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SnDlz9Oabb8pqtSo4OFipqalKSEgo8BiVK1dWbGys7fHZs2fVokUL2+OaNWvq9OnTkqSTJ0/q3LlztlFSF8u+pU/Kul0uIiIi13INGzZU165dVblyZe3Zs8e2/cSJE6patWqecRZUdteuXdq+fbvdJOhVq1ZVcHCwPvzwQ0lSly5d1KBBA2VkZMhisSguLk7lypUjEYXCi4uWwttLaUlZjz19JOtmElIAADigKKfBKGokowAAuEDr1q0VERGhfv365Vj57s8//9Tw4cP10EMPKS0tTW+99ZZDbbZo0ULR0dE6f/68ypcvr+bNm2vlypUaM2aM3NzctHz5cjVv3lyStGzZMt1yyy129Q8cOGD7+e+//7b97MjIqM6dO+ull17SmTNnFBQUpHnz5qlLly651nGk7Keffqo777xT5cuXt6s3YcIEnTt3ThUqVND+/fvl7+8vi8UiSdq6dWuRrAqIMigpNisRNWBG1uOFo7K2kYwCAOCKRjIKAOBye04WPLrIWe2NHj1a33//verXr6/WrVvLw8ND7du31/jx43XLLbfotttu07p167R37167W+/yU65cOfXt21crV65U//79NWrUKM2dO1ctW7ZUYGCgDhw4oNWrV0uSFi9erOeff96u/unTp3XttdeqQoUK8vT01K+//qpFixbptttu08CBA/M9dsuWLXXzzTerRYsWqlevnqKiorRhwwZJ/zfn1Jdffqny5cvnW1bKWm1v9uzZ+v777+2OUadOHQ0bNkxXXXWVmjVrpk2bNmnKlCm2/UuWLNHQoUMdOldArkIaujoCAABQhEhGAQBcJsi3nLw9LXps3rYib9vb06Ig33L5lrFYLPrmm2/stnl6emr58uXavn279u7dq7S0NIWGhkrKWhXur7/+0n///afmzZsrLi7ONiF3kyZN9Morr+R5rPHjx2v8+PHq37+/fHx89OuvvyoyMlLnz59Xu3bt5Ofnp6SkJO3cuVNXX321Xd02bdro5Zdf1vHjx9WhQwf9/vvvatSokcPnYvbs2dq8ebNOnTqljh07KjAwMOsc/f+RVReOAMurrJQ199Unn3yi1q1b5zjGu+++q3vuuUeHDx/WjBkzbOfs3LlzWrt2rV5//XWH4wUAAEDpRjIKAOAyoYHeWjWus23Z2aLkyBK2bm5udnMfXahZs2a5Tkxep04d1alTR5JsCRcp67a4vG5/k7Ju1XvqqaeUlpYmT09PeXh45Lh17fz58/riiy9sq+5dqEGDBmrQoIEkqWPHjvn2Kzft27fPsc3LyyvXkVW5lZWkgIAA3XrrrXkeo0WLFnZzYUlSQkKCZs+ebVtZDwAAAMXjSFyyziSmFvldB8WBZBQAwKVCA71L1Moexal79+757q9YsaKuu+46u20dOnSwm2D9SlOtWjVVq1bN1WEAAACUOtnJJ0mKTUzVg3P+VHJahqT/u0ugOL70LQokowAAKMFCQ0PtRmABAAAAR+KS1ePddbbkk5SVgPrivvYK9i1nu0uAZBQAAAAAAAAu25nEVCWnZWjq4FaqX9lPkmPTVJQUJKMAAAAAAACuQPUr+6lZaICrwyg0d1cHAAAAAAAAgLKDZBQAAAAAAACchmQUAAClkDFG06dPz7fMmjVrdPDgQSdFVDKkpaXpyy+/dHUYLhUeHq6mTZuqXbt2rg4FAAAU0pG4ZG0/clZ7Tia4OpTLwpxRAADXiouWkmKLvl2fYCkwrOjbdbKpU6fqscceK3S9jIwMPfnkk3rkkUdy3X/27Fk99thjioyMdKi9AwcOaPfu3brxxhsLHUtuNm7cqH/++Udt2rTRNddck2/ZdevWadeuXWrevLmuvfbaXGNbvHixhgwZoqpVq+a7z9PTU4sWLVLt2rV1/fXXF0lfrjRWq1VWq1Xx8fEKCLjy5pgAAKCsungFPW9Pi4J8y7k4qktDMgoA4Dpx0VJ4eyktqejb9vSRrJuv+ITU448/fknJqILMmjVLffv2lZeXl0PlZ86cqZo1axbJsd98801NmzZNN910kyZPnqwXX3xRDz74YK5l+/fvr8TERNWpU0cvvfSShgwZorffftu2PzMzU6NHj9bmzZt17bXX2iWj8to3evRovfvuu2U2GQUAAK5MF6+gdyWtnnexEpeMSkpK0ieffCJJatKkiXr27OniiAAAxSYpNisRNWCGFNKw6NqN2SUtHJXVfgHJqLS0NC1fvlxpaWm66aab9OWXX9qNJvrll18UFRWlOnXqqEePHpKyboF7//33dc899+iHH36Qu7u7+vfvLw8PD4fq3X333frpp58UFhama6+9Vh9//LGSk5Pl7++vjh07qlGjRpKkxYsXS8oaHSVJY8aMkcViybVtSUpPT9eyZcuUlpZmtz03CxYs0BtvvGF7nJqaqu+++07R0dHKyMj6tu3C9+Fly5bphx9+0Pnz5zVnzhyNGjVKUtYIrI8//lgPP/xwvsfLlpycrDfeeENr165Vy5Yt9fvvv+u2227T/fffb3f+sj377LO228nWr1+ve++91y4ZNXXqVPXs2VP79u3LUTevfd26ddMdd9yhpKQk+fj4OBQ3AABASXGlrqB3oRKXjMrIyNCBAwe0Z88e/fHHHySjAKAsCGkoVW/lkkPffPPNiouLU/PmzfXee+/pjz/+sCWj7r//fu3du1dNmjTRrFmzNHPmTH399dfKyMjQ448/rsWLF6tevXrasmWLli5dqtmzZztU79tvv1WjRo00YMAASVJ0dLQSEhKUmJio559/XjNnzlTPnj114sQJSVm3mklZyay82pakvn37KiYmRi1atNC0adPy7feff/6p5s2bS8oaQXTDDTcoJSVFbdu21bJly1SxYkU9++yzkqR9+/bJ19dXVapUUUxMjCZNmmRLRqWlpWn8+PG2ZNTs2bN1+vTpXI85ZswY7dy5U35+fmrZsqUk6ZprrlFaWpoOHjyoevXq5ajTrl07ff311zp06JBWrlypp556yrbvv//+03fffacff/xRH374oV29/PZ5enqqfv362rp1qzp27JjveQIAAEDRK3HJqAoVKmjq1KlasGCB7RthAACKw4YNG3T48GFt375dFotFq1atUt++fSVlJWt+/PFHPfHEE5KkBg0a6Pnnn9eJEycUHByszMxMvffee2rWrJnOnDmjWrVqOVxvypQpatOmjS2OCRMmaOXKlTp27JjS09P15ZdfqmfPnnrggQf04IMP2kZG5df2nj17dODAAe3YsUMWi0U//fSTbrnlllz7nZiYqPPnz8vf31+StHbtWp06dUo7d+6Up6en/vrrL3Xr1k133XWXpKwRWv369XPonB45csSWRLuYMUYxMTEKDg62216xYkWdOnUq12SUJB07dkx79uzRsWPHbKOnMjIy9NBDD+mjjz6Su7v9eiz57csWFBSk2NhimKsMRSN7LrmYXa6OBAAAFIMiT0ZFR0drxowZ2rJli5588kl16dIlR5k///xTn376qU6dOqXWrVtr7Nix8vPzK+pQAADI1759+9S2bVtZLBZJspscOyoqSl5eXrZRSZI0cuRIGWMkSRaLRc2aNZOUldhITU1Venp6gfXc3d3VunVr276YmBi1atVKbdq0Uc2aNRUXF6fz58/nGm9+be/Zs0dXX311rn25mI+Pjzw9PZWYmCg/Pz9FR0erdevW8vT0lCS1bNlSiYmJOnPmjCpWrKglS5ZoxowZBZ1OSVJoaKi8vXOfu8DNzU1BQUE6e/as3fazZ8+qYsWKebaZnXzbvXu3mjdvruHDh2vu3LlKSUnRd999p++++05nz57VvHnzFBgYqF9++SXPfY0bN5YknTt3ToGBgQ71CU528Vxynj5ZCxIUx0IHAADAJYo0GfXFF1/ohRde0L333qsVK1ZoyJAhOcqsW7dON954ox566CH17dtX06dP16JFi/Tbb7/ZLoIBAHCG6tWr67///rM9joqKsv1cq1YtZWRk6K233lK5cvarlKSnp+fZZkH13Nzc5ObmZtv2yy+/qFWrVlq6dKkk6d1339VPP/1k22+xWJSZmSl3d/d8265Ro4Zd/Bf+fDE3Nze1aNFC//77r9q1a6datWppx44dMsbIzc1N//33nzw9PRUUFKSYmBidPn1aDRv+35xeiYmJysjIkMVi0dGjR+3aLmhkVMOGDXXy5EkdOHBAtWvX1n///aeUlBTbyLILxcXFyc3Nzbbim7e3t20+q5o1a6pdu3a2xFxaWpqOHTumxMTEfPdJWbcl7tq1y3arIEqYi+eSy14ZMzsZlT1aqpSsmAkAQFlUpMmonj17atiwYbJYLJo8eXKuZcaPH69BgwbZ5rLo3bu3wsLCNHfuXN17771FGQ4AAPnq2rWrzp8/r4EDB6ply5ZauXKlbWRRp06d1LhxY3Xq1En9+vWTj4+PvLy89NBDD+XbZmHrNWnSRBs3btRzzz2npKQkffvtt2rSpIltf4MGDfTEE0+odu3aeuSRR/JsO3skcv/+/dWmTRutXLky3zj79++vn376Se3atdMNN9ygwMBA9enTRx06dNDs2bM1fvx4ubm5afny5bZbF7Olp6dr2LBhatGihX7++We5ublp3rx5Gjx4sJ5++ul8j+vv769Ro0apb9++GjhwoCIiIjR27Fjbqn6zZ89Wp06dVKdOHSUmJqpfv37q0aOHypcvr6+++kqjRo1S+fLldeONN+rGG2+0tbt8+XI99thjatu2rSTlu2/Tpk1q27atLcmFEuriueR8grNGSS3Mmq9Mnj7S4DmST8j/7Sc5BQDAFaFIk1EXLqecmzNnzmjz5s0aP368bVuVKlXUqVMnrVy50paM+uCDDxQZGamdO3dq6tSpGjhwoGrUqJFrmykpKUpJSbE9jo+Pv/yOAACcq6jnhXGwPQ8PD23YsEGfffaZ0tLS9OKLL8pqtUqSLRHz7bffatu2bTp58qTt9jN3d3eNHTvWrq0xY8bI3d290PUaNmyoFStW6Pvvv1fjxo21aNEiRUZG2vYvXLhQERERtlE+ebVtsVi0du1azZw5U6mpqfr66681ffr0PPs+atQo9ezZUxMmTJDFYtGaNWs0e/ZsHT58WFOnTlWfPn0kZc0XNXHiRLu6AQEBevjhh7VmzRq988472rlzZ64r4eXlf//7n+bOnavt27frxRdf1ODBg237jhw5ouTkZElZt/wtX75cc+fO1blz5/Tee++pV69eubY5YsQIVatWzaF9M2fO1GOPPeZwvCghAsMk6+asEVJJMdK84dKXt//ffk+frP0kpAAAKPGcOoH5/v37JSlHYiksLEz//vuv7fHBgwcVEBCg66+/XgcOHMhz7gxJev311/MchQUAKOEuHulQlLLnmSnAhg0b5O7urvPnz+vhhx+2rRInZSWPBg0apEGDBtnVcXd317vvvmu37e23376kepLUsWNHu1XdLpzcvEmTJjne53JrW8oadfT444/nGtPFqlSpovHjx2v//v2qX7++vL299cADD+Qo16pVK11zzTU5tnfq1EmdOnWSJLVo0SLP4+TG3d1dw4cPz3XfxSOrqlatqnHjxhXYZvbKfwXtS09PV4MGDXKM9sIVIjDs/5JN2YkpKSsBvXBU1mOSUQAAlHhOTUalpqZKypo49UI+Pj62fZL05ptvOtzm008/bZvYVMoaGRUWxkUIAFwRLhzpUNQcvGUne/6iChUqaNq0abr55puLPpYS6s477yywzIsvvmj32NvbW6NHjy6miIqfh4dHjpFeuEJdmJgCAABXFKcmo7JXrTl9+rTd9tjY2Ete0cbLy8s2zwQA4Ark4g+UI0eOdNmxr0S+vr566aWXXB0GAAAArmDuzjxYvXr15Ovrq7/++stu+7Zt21jRBgAAAAAAoAxwajLK09NTQ4cO1UcffWSbaHzx4sXavXt3nnNHOCo8PFxNmzZVu3btiiJUAEAxMca4OgQgV/xuAgAAOEeRJqP++usv9e3b1zYp6Lvvvqu+ffvq/ffft5V5++23FRQUpPr166tDhw6688479fbbb9uWW75UVqtVUVFRdisQAQBKDk9PT0lSUlKSiyMBcpc9f6XFYnFxJAAAAKVbkc4ZFRoaqgcffFCSbP9LUs2aNW0/BwYGauPGjdq2bZtiY2PVvHlzValSpSjDAACUQBaLRYGBgTp58qSkrMUr3NzcXBwVkCUzM1OnTp2Sj4+PPDycOqUmAABAmVOkV1shISEOLZXs5uam1q1bF+WhAQBXgKpVq0qSLSEFlCTu7u6qWbMmSVIAAIBixld/AACncXNzU7Vq1VS5cmWlpaW5OhzATrly5eTu7tTpNAEAAMqkUpOMCg8PV3h4uDIyMlwdCgCgABaLhXl5AAAAAAcciUvWmcRU7TmZ4OpQikypSUZZrVZZrVbFx8crICDA1eEAAAAAAABcliNxyerx7jolp2UNvPH2tCjIt5yLo7p8pSYZBQAAgIKV6tHkMbuy/vcJlgLDXBsLAABF4ExiqpLTMjR1cCvVr+ynIN9yCg30dnVYl41kFAAAQBlSKkeT+wRLnj7SwlFZjz19JOtmElIAgFKjfmU/NQstJe/bIhkFAACAK11gWFbyKSk2a3TUwlFZP5OMAgCgRCIZBQAAgCtfYBjJJwAArhClZv3i8PBwNW3aVO3atXN1KAAAAAAAAMhDqUlGWa1WRUVFKTIy0tWhAAAAAAAAIA+lJhkFAAAAAACAko9kFAAAAAAAAJyGZBQAAAAAAACchmQUAAAAAAAAnKbUJKNYTQ8AAAAAAKDkKzXJKFbTAwAAAAAAKPk8XB0AAAAAyri4aCkpNuvnmF2ujQUAABQ7klEAAABwnbhoKby9lJb0f9s8fSSf4MtrNzup5RMsBYZdXlsAAKBIkYwCAACA6yTFZiWiBsyQQhpmbbucBJJPcFYya+GorMeePpJ1MwkpAABKEJJRAAAAcL2QhlL1VpffTmBYVvIpKTZrdNTCUVk/k4wCAKDEIBkFAACA0iUwjOQTAOCKdiQuWWcSU7XnZIKrQykWJKMAAAAAAABKiCNxyerx7jolp2VIkrw9LQryLefiqIpWqUlGhYeHKzw8XBkZGa4OBQAAAAAA4JKcSUxVclqGpg5upfqV/RTkW06hgd6uDqtIubs6gKJitVoVFRWlyMhIV4cCAAAAAABwWepX9lOz0IBSl4iSSlEyCgAAAAAAACVfqblNryTKnnAsP6V1MjIAAAAAAIDckIwqJhdPOJaf0jgZGQAAAAAAQG5IRhWTiyccy09IxklVTfpPSsp9f/mYBFVXTDFECQAAAAAA4Fwko4pZ9oRjeYqLlsJvkNLyyERJqi9plZeXTkYHSW418z+gT7AUGHZpwQIAAAAAABQzklGulhSblYgaMEMKaZhrkQOHDqny9/er9srhBbfn6SNZN5OQAgAAAAAAJRLJqJIipKFUvVWuuxJMHd2Z8rZmD62n+pXyueUvZpe0cFRWgotkFAAAAAAAKIFKTTIqPDxc4eHhysgoeMLwK9FRheh8SHOpej63/AEAAAAAAJRw7q4OoKhYrVZFRUUpMjLS1aEAAAAAAAAgD6UmGQUAAAAAAICSr9TcpgcAAFAWLV++XNu2bVPr1q118803y83NzdUhAQAA5IuRUQAAAFeoBx54QB9//LHOnz+vMWPG6NVXX3V1SAAAAAViZBQAAMAV6vHHH1fjxo0lSdddd53ee+89F0cEAABQMEZGAQAAuEB6eroWLVqkW265Rddee22uKwKnpqbqnXfe0Y033qjevXtrxowZMsbY9mcnoiRp/vz5uvPOO50SOwAAwOVgZBQAAIAL9OzZUxUqVFDt2rW1fPlyuyRTthEjRuiXX37RW2+9pcTERD3xxBM6ePCgXnnlFVuZzMxMjR07Vg0aNNDdd9/tzC4AAABcEpJRAAAALrBw4UIFBAQoIiJC06dPz7H/n3/+0VdffaWff/5ZXbp0kSRlZGTIarVq3LhxCgoK0vnz5zV8+HB16dJFVqvVyT0AAAC4NCSjSqOYXQWX8QmWAsOKPxYAAJCrgICAfPevXr1a/v7+uuGGG2zb+vXrp1GjRmn9+vXq16+fhgwZor1796pevXqaOHGiqlevrkcffTTX9lJSUpSSkmJ7HB8fXzQdAQAAKCSSUaWJT7Dk6SMtHFVwWU8fybqZhBQAACXUwYMHVbVqVbm7/98Un5UqVZKnp6cOHjwoKSs5deLECdv+ChUq5Nne66+/rsmTJxdfwCVZ9hd1fBkHAECJQDKqNAkMy0owJcXmXy5mV1bCKimWCzIAAEqotLQ0eXl55dhevnx5paWlScqaU8pRTz/9tJ544gnb4/j4eIWFlfLrgIu/qOPLOAAASoRSk4wKDw9XeHh4rivRlCmBYVxgAQBQClSsWFGnT5+225aamqpz584pODi40O15eXnlmtwq1S78oo4v4wAAKDHcCy5yZbBarYqKilJkZKSrQwEAALhsbdq00dGjR3X06FHbts2bN0uSWrdu7aqwrjyBYVL1VlJIQ1dHAgBAno7EJWv7kbPafuSs9pxMcHU4xa7UjIwCAAAoTXr16qXq1avrpZde0kcffaT09HS9+uqratu2rVq2bOnq8AAAQBE5EpesHu+uU3La/93p5e1pUZBvORdGVbxIRgEAALjA66+/riVLlig2Nmuux+uvv16S9P7776tdu3YqX768vv32Ww0aNEihoaFKSUlR1apVtXjx4ss6LlMbAABQspxJTFVyWoamDm6l+pX9JElBvuUUGujt4siKD8mosix7ZZm8sOIMAADFZvDgweratWuO7Y0aNbL9fM0112j//v36999/5eHhoUaNGsnNze2yjmu1WmW1WhUfH6+AgIDLagsAABSd+pX91Cy0eN6bs2/9KylJLpJRZdHFK8vkhRVnAAAoNnXr1lXdunULLGexWNSsWTMnRAQAAEqbIN9y8va06LF52yRl3f63alxnlyekSEaVRReuLJMXVpwBAAAAAOCKFhrorVXjOutMYqr2nEzQY/O26UxiKskouEhgGEkmAAAAAABKudBAb5cnny7m7uoAAAAAAAAAUHaQjAIAAAAAAIDTcJteMaquGJWP+Udy88u7UEEr2gEAABSh8PBwhYeHKyMjw9WhAACAMopkVDHxTDiiVV5PyWdRigOFfbJWuAMAAChmVqtVVqtV8fHxCggonuWjAQAA8kMyqphYzp+Wj1uKortOU1iDVvkX9glmMnEAAAAAAFAmkIwqZimB9aXqrVwdBgAAAAAAQInABOYAAAAAAABwGpJRAAAAAAAAcBpu0wMAAEDZceFKxszbCQCAS5SaZBTLFAMAABSszF4z+QRnrWC8cNT/bfP0kaybSUgBAOBkpeY2PavVqqioKEVGRro6FAAAgBKrzF4zBYZlJZ5Gr8v6N2CGlJYkJcW6OjIAAMqcUjMyCsXkwqHseWGIOwAAuBIEhnHNAgBACUAyCrnLbSh7XhjiDgAAAAAAHEQyCrnLHspe0ND1mF1ZCaukWJJRAAAAAACgQCSjkDeGsgMAAAAAgCJWaiYwBwAAAAAAQMlHMgoAAAAAAABOQzIKAACgDAkPD1fTpk3Vrl07V4cCAADKKJJRAAAAZYjValVUVJQiIyNdG0hctHR0W9ZiKAAAoExhAnMAAAA4V1y0FN5eSkvKeuzpI/kEuzYmAADgNCSjAAAA4FxJsVmJqAEzpJCGWYkoVvAFAKDMIBkFAAAA1whpKFVv5doYsm8TJCEGAHCyI3HJOpOYqj0nE1wditORjAIAAEDZ4xOcdXvgwlFZjz19JOtmElIAAKc4EpesHu+uU3JahiTJ29OiIN9yLo7KeUhGAQAAoOwJDMtKPiXFZo2OWjgq62eSUQAAJziTmKrktAxNHdxK9Sv7Kci3nEIDvV0dltOQjAIAAEDZFBhG8gkA4FL1K/upWWiAq8NwOndXBwAAAAAAAICyg2QUAABAGRIeHq6mTZuqXbt2rg4FAACUUSSjAAAAyhCr1aqoqChFRka6OhQAAFBGkYwCAAAAAACA05C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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = plot_mean_energy_per_step(obs)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "4536acb0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = plot_mean_length_per_step(obs)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "3a03c04f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_ = plot_shower_max_depth(obs)" ] From 60c2ca1985eb66853dd881d2f6bc441726fd02ed Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 24 Jul 2026 13:32:25 +0200 Subject: [PATCH 02/24] analyze: add MoE router gating/share diagnostic plots New "model" family in the gallery: router_gating (mean soft gate weight vs. pre-step energy, showing the router's soft decision boundaries) and router_share_by_pdg/router_share_by_process (stacked top-1 dispatch share by species / true physics process). Needs a live checkpoint's Router, so it's a documented exception to the rest of the package's polars/numpy-only contract; gracefully degrades to a placeholder for non-MoE checkpoints. Co-Authored-By: Claude Sonnet 5 --- giant/analysis/catalog.py | 26 ++- giant/analysis/condor.py | 4 +- giant/analysis/reduced.py | 13 +- giant/analysis/render.py | 77 ++++++++ giant/analysis/router_gating.py | 339 ++++++++++++++++++++++++++++++++ tests/test_catalog.py | 14 ++ tests/test_router_gating.py | 126 ++++++++++++ 7 files changed, 592 insertions(+), 7 deletions(-) create mode 100644 giant/analysis/router_gating.py create mode 100644 tests/test_router_gating.py diff --git a/giant/analysis/catalog.py b/giant/analysis/catalog.py index a91398a..a0e5bc0 100644 --- a/giant/analysis/catalog.py +++ b/giant/analysis/catalog.py @@ -37,6 +37,11 @@ from giant.analysis.reduce import ( weighted_profile, ) from giant.analysis.reduced import Reduced +from giant.analysis.router_gating import ( + compute_router_gating, + compute_router_share_by_pdg, + compute_router_share_by_process, +) from giant.analysis.sources import Side, open_side, physical_steps, secondaries from giant.analysis.variables import RANGED_VARS, cos_scatter_expr @@ -50,9 +55,10 @@ class Bundle: t_all: pl.LazyFrame # reference, all rows r_phys: pl.LazyFrame # rollout, physical steps only t_phys: pl.LazyFrame # reference, physical steps only + checkpoint: str | None = None # from the rollout YAML; router_gating only @classmethod - def open(cls, rollout, reference, ctx: Context) -> "Bundle": + def open(cls, rollout, reference, ctx: Context, checkpoint=None) -> "Bundle": r_all = open_side(rollout, Side.rollout) t_all = open_side(reference, Side.reference) return cls( @@ -61,6 +67,7 @@ class Bundle: t_all=t_all, r_phys=physical_steps(r_all, Side.rollout), t_phys=physical_steps(t_all, Side.reference), + checkpoint=checkpoint, ) @@ -559,6 +566,23 @@ def build_catalog() -> list[PlotSpec]: PlotSpec("sec_count_per_species", "secondaries", _sec_count_per_species), PlotSpec("sec_energy", "secondaries", _sec_energy), PlotSpec("sec_cos_angle", "secondaries", _sec_cos_angle), + PlotSpec( + "router_gating", + "model", + lambda b: compute_router_gating(b.checkpoint, b.r_phys, b.t_phys), + ), + PlotSpec( + "router_share_by_pdg", + "model", + lambda b: compute_router_share_by_pdg( + b.checkpoint, b.r_phys, b.t_phys, b.ctx.top_pdgs + ), + ), + PlotSpec( + "router_share_by_process", + "model", + lambda b: compute_router_share_by_process(b.checkpoint, b.t_phys), + ), ] return specs diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index e2ef763..b9ad621 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -152,10 +152,11 @@ def compute_reduced( reference: str | Path, shared: str | Path, out: str | Path, + checkpoint: str | None = None, ) -> Path: """Core: run one plot's reduction against explicit paths → ``Reduced`` JSON.""" ctx = Context.load(shared) - bundle = Bundle.open(rollout, reference, ctx) + bundle = Bundle.open(rollout, reference, ctx, checkpoint=checkpoint) reduced = get_spec(spec_id).compute(bundle) out = Path(out) reduced.save(out) @@ -172,6 +173,7 @@ def compute_one(spec_id: str, run_dir: str | Path) -> Path: meta.reference, run_path / "shared.json", run_path / "reduced" / f"{spec_id}.json", + checkpoint=meta.plot_meta.get("checkpoint"), ) diff --git a/giant/analysis/reduced.py b/giant/analysis/reduced.py index 947641e..836c923 100644 --- a/giant/analysis/reduced.py +++ b/giant/analysis/reduced.py @@ -12,11 +12,14 @@ from dataclasses import asdict, dataclass, field from pathlib import Path # Reduced.kind values: -# "overlay_hist" rollout vs reference density histogram over shared edges -# "grouped_hist" one panel per group (energy/pdg/material), each an overlay -# "profile" edep-weighted mean +/- event-RMS vs depth/radius, two series -# "bar" per-category rollout vs reference bars (share / counts) -# "single_hist" one series only (e.g. rollout leakage; reference has none) +# "overlay_hist" rollout vs reference density histogram over shared edges +# "grouped_hist" one panel per group (energy/pdg/material), each an overlay +# "profile" edep-weighted mean +/- event-RMS vs depth/radius, two series +# "bar" per-category rollout vs reference bars (share / counts) +# "single_hist" one series only (e.g. rollout leakage; reference has none) +# "router_gating" stacked mean MoE gate weight vs energy, rollout + reference +# "router_share" stacked bar of MoE top-1 dispatch share by category +# "unavailable" plot not applicable to this run (e.g. non-MoE checkpoint) @dataclass diff --git a/giant/analysis/render.py b/giant/analysis/render.py index d5066c8..35cd3d6 100644 --- a/giant/analysis/render.py +++ b/giant/analysis/render.py @@ -137,12 +137,89 @@ def _render_bar(r: Reduced, params: dict): return fig +def _render_router_gating(r: Reduced, params: dict): + n_experts = r.payload["n_experts"] + log_x = r.payload.get("log_x", False) + fig, axes = ps.new_figure( + "slide-16x9", title=r.title, params=params, nrows=1, ncols=2, squeeze=False + ) + flat = axes.ravel() + for ax, key in zip(flat, ("rollout", "reference")): + side = r.payload.get(key, {}) + centers = np.asarray(side.get("centers", [])) + means = np.asarray(side.get("means", [])) + if len(centers) and means.size: + cum = np.zeros(len(centers)) + for i in range(n_experts): + ax.fill_between( + centers, cum, cum + means[:, i], alpha=0.7, label=f"expert {i}" + ) + cum = cum + means[:, i] + if log_x: + ax.set_xscale("log") + ax.set_ylim(0, 1) + ax.set_title(_SERIES_LABELS[key], fontsize=8) + ax.set_xlabel(r.xlabel) + flat[0].set_ylabel("mean gate weight") + ps.style_legend(flat[0], title=f"{r.payload.get('router_type', '')} router") + return fig + + +def _render_router_share(r: Reduced, params: dict): + categories = r.payload["categories"] + n_experts = r.payload["n_experts"] + x = np.arange(len(categories)) + present = [k for k in ("rollout", "reference") if k in r.payload] + fig, axes = ps.new_figure( + "slide-16x9", + title=r.title, + params=params, + nrows=1, + ncols=len(present), + squeeze=False, + ) + flat = axes.ravel() + for ax, key in zip(flat, present): + side = r.payload[key] + shares = np.array([side[c] for c in categories]) # (n_cat, n_experts) + bottom = np.zeros(len(categories)) + for i in range(n_experts): + ax.bar(x, shares[:, i], bottom=bottom, label=f"expert {i}") + bottom += shares[:, i] + ax.set_xticks(x) + ax.set_xticklabels(categories, rotation=45, ha="right") + ax.set_ylim(0, 1) + ax.set_title(_SERIES_LABELS[key], fontsize=8) + flat[0].set_ylabel("share of rows dispatched to expert") + ps.style_legend(flat[0], title=f"{r.payload.get('router_type', '')} router") + return fig + + +def _render_unavailable(r: Reduced, params: dict): + fig, ax = ps.new_figure("thesis-single", title=r.title, params=params) + ax.axis("off") + ax.text( + 0.5, + 0.5, + r.payload.get("note", "not available"), + ha="center", + va="center", + wrap=True, + fontsize=10, + transform=ax.transAxes, + ) + return fig + + _RENDERERS = { "overlay_hist": _render_overlay, "single_hist": _render_single, "grouped_hist": _render_grouped, "profile": _render_profile, "bar": _render_bar, + "router_gating": _render_router_gating, + "router_share": _render_router_share, + "unavailable": _render_unavailable, } diff --git a/giant/analysis/router_gating.py b/giant/analysis/router_gating.py new file mode 100644 index 0000000..6dbc3f8 --- /dev/null +++ b/giant/analysis/router_gating.py @@ -0,0 +1,339 @@ +"""Router gating diagnostic: where a MoE checkpoint's decision boundaries sit. + +Unlike everything else in this package, this reduction needs a live PyTorch +model — soft expert gate weights aren't columns in a rollout/predict parquet, +they only exist by calling `Router.gate(cond_cont, cond_cat)` (see +`giant.model.network.Router`) against the checkpoint that produced the +rollout. That's a deliberate, narrow exception to the rest of the catalog's +"polars/numpy only" contract; it still runs fine as a `compute-one` HTCondor +job since torch is already installed there (the same env trains checkpoints). + +The routing axis is fixed to pre-step energy: every router type at least +indirectly depends on it (`EnergyRouter` reads it directly; `PdgRouter` and +`ProcessRouter` correlate with it through the physics), and it's the one axis +a reader can interpret without knowing the checkpoint's specific router +config. `x` is binned into equal-population (quantile) bins rather than +equal-width ones, since energy is heavy-tailed and equal-width bins would +leave the upper end almost empty. Mean gate weight per bin is stacked as +filled areas per expert — since `gate` rows are a partition of unity, the +stack always fills exactly to 1, and the crossover bands are the router's +soft decision boundaries (where two experts' means cross ~0.5). +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import TYPE_CHECKING + +import numpy as np +import polars as pl + +from giant.analysis.grouping import pdg_label +from giant.analysis.reduced import Reduced + +if TYPE_CHECKING: + import torch + + from giant.data.transforms import Normalizer + +_SAMPLE_ROWS = 200_000 +_N_BINS = 40 +_TOP_K_PROCESS = 8 + +_COLS = ( + "pre_x", + "pre_y", + "pre_z", + "pre_E", + "pre_dx", + "pre_dy", + "pre_dz", + "layer_id", + "pdg", + "material", +) + + +@dataclass +class _RouterHandle: + router: "torch.nn.Module" + pdg_map: dict[int, int] + mat_map: dict[str, int] + cond_normalizer: "Normalizer" + conditioning: str + router_type: str + + +def load_router(checkpoint: str | Path) -> _RouterHandle | None: + """Load a checkpoint's Stage-1 router, or None if it isn't a MoE checkpoint.""" + import torch + + from giant.data.transforms import Normalizer + from giant.model.network import build_models + + ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) + model_cfg = ckpt.get("model_config") or {} + router_cfg = model_cfg.get("router") + if not router_cfg or not router_cfg.get("enabled"): + return None + + stage1, _ = build_models(model_cfg) + stage1.load_state_dict(ckpt["model"]) + stage1.eval() + + return _RouterHandle( + router=stage1.router, + pdg_map={int(k): v for k, v in ckpt["pdg_map"].items()}, + mat_map={str(k): v for k, v in ckpt["mat_map"].items()}, + cond_normalizer=Normalizer.from_dict(ckpt["normalizer"]["cond"]), + conditioning=model_cfg.get("conditioning", "embedding"), + router_type=router_cfg["type"], + ) + + +def _subsample( + lf: pl.LazyFrame, n: int, seed: int, extra_cols: tuple = () +) -> pl.DataFrame: + total = lf.select(pl.len()).collect(engine="streaming").item() + if total > n: + threshold = int(n / total * 2**32) + lf = lf.filter((pl.col("pre_E").hash(seed=seed) % 2**32) < threshold) + return lf.select(*_COLS, *extra_cols).collect(engine="streaming") + + +def _gate_for_df( + handle: _RouterHandle, df: pl.DataFrame +) -> tuple[pl.DataFrame, np.ndarray]: + """(filtered df, gate_weights) for rows in ``df`` with a known pdg/material. + + Rows whose species or material never appeared in the checkpoint's + training vocab can't be embedded — dropped here the same way + `giant.rollout`'s own known-pdg gate drops them at inference. The + returned df keeps every original column (filtered to the same rows), so + callers can key gate weights by any of them (energy, pdg, process, ...). + """ + import torch + + from giant.data.transforms import build_cond_features + + known = np.array( + [ + int(p) in handle.pdg_map and str(m) in handle.mat_map + for p, m in zip(df["pdg"].to_list(), df["material"].to_list()) + ] + ) + if not known.any(): + return df.clear(), np.zeros((0, handle.router.n_experts)) + df = df.filter(pl.Series(known, dtype=pl.Boolean)) + + data = { + "pre_pos": np.column_stack( + [df["pre_x"].to_numpy(), df["pre_y"].to_numpy(), df["pre_z"].to_numpy()] + ), + "pre_E": df["pre_E"].to_numpy(), + "pre_dir": np.column_stack( + [df["pre_dx"].to_numpy(), df["pre_dy"].to_numpy(), df["pre_dz"].to_numpy()] + ), + "layer_id": df["layer_id"].to_numpy(), + "pdg": df["pdg"].to_numpy(), + "material": df["material"].to_numpy(), + } + cond_cont, cond_cat = build_cond_features( + data, + handle.pdg_map, + handle.mat_map, + cond_normalizer=handle.cond_normalizer, + conditioning=handle.conditioning, + ) + with torch.no_grad(): + gate = handle.router.gate( + torch.from_numpy(cond_cont).float(), torch.from_numpy(cond_cat).long() + ).numpy() + return df, gate + + +def _quantile_bins(x: np.ndarray, gate: np.ndarray, n_bins: int) -> dict: + order = np.argsort(x) + x_sorted, g_sorted = x[order], gate[order] + edges = np.quantile(x_sorted, np.linspace(0, 1, n_bins + 1)) + edges[-1] = np.nextafter(edges[-1], np.inf) # include the max value + bin_idx = np.clip(np.digitize(x_sorted, edges[1:-1]), 0, n_bins - 1) + + n_experts = gate.shape[1] + centers = np.full(n_bins, np.nan) + means = np.full((n_bins, n_experts), np.nan) + for b in range(n_bins): + mask = bin_idx == b + if mask.any(): + centers[b] = x_sorted[mask].mean() + means[b] = g_sorted[mask].mean(axis=0) + valid = ~np.isnan(centers) + return {"centers": centers[valid].tolist(), "means": means[valid].tolist()} + + +def _top1_shares( + categories: np.ndarray, idx: np.ndarray, order: list, n_experts: int +) -> dict[str, list[float]]: + """Fraction of each category's rows hard-dispatched to each expert. + + Uses `Router.top1` (argmax), not the soft `gate` mean — grouped top-1 + dispatch is what `_route_forward` actually runs in eval mode (rollout, + predict), so this answers "which expert does a photon/Compton step + actually go through", not just its average soft weight. + """ + shares: dict[str, list[float]] = {} + for key in order: + mask = categories == key + total = int(mask.sum()) + if total == 0: + shares[str(key)] = [0.0] * n_experts + continue + counts = np.bincount(idx[mask], minlength=n_experts) + shares[str(key)] = (counts / total).tolist() + return shares + + +_NOTE_NOT_MOE = ( + "checkpoint has no enabled MoE router (model.router.enabled is " + "false/absent) — nothing to show" +) + +_TITLES = { + "router_gating": "Router gating (mixture-of-experts decision boundaries)", + "router_share_by_pdg": "Router expert share by particle species", + "router_share_by_process": "Router expert share by physics process", +} + + +def _unavailable(spec_id: str) -> Reduced: + return Reduced( + id=spec_id, + family="model", + kind="unavailable", + title=_TITLES[spec_id], + xlabel="n/a", + payload={"note": _NOTE_NOT_MOE}, + ) + + +def compute_router_gating( + checkpoint: str | Path | None, + r_phys: pl.LazyFrame, + t_phys: pl.LazyFrame, + seed: int = 0, +) -> Reduced: + """`Reduced` for the router-gating figure, or an explanatory note if n/a.""" + handle = load_router(checkpoint) if checkpoint else None + if handle is None: + return _unavailable("router_gating") + + sides: dict[str, dict] = {} + for name, lf in (("rollout", r_phys), ("reference", t_phys)): + df = _subsample(lf, _SAMPLE_ROWS, seed) + df, gate = _gate_for_df(handle, df) + x = df["pre_E"].to_numpy() + sides[name] = ( + _quantile_bins(x, gate, _N_BINS) if len(x) else {"centers": [], "means": []} + ) + + return Reduced( + id="router_gating", + family="model", + kind="router_gating", + title=_TITLES["router_gating"], + xlabel="pre-step energy [MeV]", + payload={ + "router_type": handle.router_type, + "n_experts": handle.router.n_experts, + "log_x": True, + **sides, + }, + ) + + +def compute_router_share_by_pdg( + checkpoint: str | Path | None, + r_phys: pl.LazyFrame, + t_phys: pl.LazyFrame, + top_pdgs: list[int], + seed: int = 0, +) -> Reduced: + """Stacked-bar share of each particle species dispatched to each expert.""" + handle = load_router(checkpoint) if checkpoint else None + if handle is None: + return _unavailable("router_share_by_pdg") + + labels = [pdg_label(p) for p in top_pdgs] + sides: dict[str, dict] = {} + for name, lf in (("rollout", r_phys), ("reference", t_phys)): + df = _subsample(lf, _SAMPLE_ROWS, seed) + df, gate = _gate_for_df(handle, df) + if len(df): + idx = gate.argmax(axis=1) + shares = _top1_shares( + df["pdg"].to_numpy(), idx, top_pdgs, handle.router.n_experts + ) + else: + shares = {str(p): [0.0] * handle.router.n_experts for p in top_pdgs} + sides[name] = {labels[i]: shares[str(p)] for i, p in enumerate(top_pdgs)} + + return Reduced( + id="router_share_by_pdg", + family="model", + kind="router_share", + title=_TITLES["router_share_by_pdg"], + xlabel="particle species", + payload={ + "router_type": handle.router_type, + "n_experts": handle.router.n_experts, + "categories": labels, + **sides, + }, + ) + + +def compute_router_share_by_process( + checkpoint: str | Path | None, + t_phys: pl.LazyFrame, + seed: int = 0, + top_k: int = _TOP_K_PROCESS, +) -> Reduced: + """Stacked-bar share of each physics process dispatched to each expert. + + Reference-only: ``process`` is the true post-step physics process — a + label the rollout side has no equivalent of (see + `giant.model.network.ProcessRouter`, which predicts it from pre-step + conditioning alone, never observes it at eval time). This plot instead + checks *after the fact*, on real data, how well the router's conditioning + -based dispatch lines up with the true process. + """ + handle = load_router(checkpoint) if checkpoint else None + if handle is None: + return _unavailable("router_share_by_process") + + df = _subsample(t_phys, _SAMPLE_ROWS, seed, extra_cols=("process",)) + df, gate = _gate_for_df(handle, df) + if len(df): + counts = df["process"].value_counts().sort("count", descending=True) + order = counts["process"].to_list()[:top_k] + idx = gate.argmax(axis=1) + shares = _top1_shares( + df["process"].to_numpy(), idx, order, handle.router.n_experts + ) + else: + order, shares = [], {} + + return Reduced( + id="router_share_by_process", + family="model", + kind="router_share", + title=_TITLES["router_share_by_process"], + xlabel="physics process", + payload={ + "router_type": handle.router_type, + "n_experts": handle.router.n_experts, + "categories": order, + "reference": {p: shares[p] for p in order}, + }, + ) diff --git a/tests/test_catalog.py b/tests/test_catalog.py index 02d449b..90447d3 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -44,6 +44,9 @@ def test_every_spec_computes_valid_reduced(bundle: Bundle): "profile", "bar", "single_hist", + "router_gating", + "router_share", + "unavailable", } assert r.title and r.xlabel _validate_payload(r) @@ -67,3 +70,14 @@ def _validate_payload(r) -> None: assert len(p[k]) == n elif r.kind == "bar": assert len(p["labels"]) == len(p["rollout"]) == len(p["reference"]) + elif r.kind == "unavailable": + assert p["note"] + elif r.kind == "router_gating": + for side in ("rollout", "reference"): + if side in p: + assert len(p[side]["centers"]) == len(p[side]["means"]) + elif r.kind == "router_share": + for cat in p["categories"]: + for side in ("rollout", "reference"): + if side in p: + assert cat in p[side] diff --git a/tests/test_router_gating.py b/tests/test_router_gating.py new file mode 100644 index 0000000..51123d3 --- /dev/null +++ b/tests/test_router_gating.py @@ -0,0 +1,126 @@ +"""Tests for the MoE router-gating diagnostic (giant.analysis.router_gating).""" + +from __future__ import annotations + +import numpy as np +import polars as pl +import torch + +from giant.analysis.router_gating import ( + compute_router_gating, + compute_router_share_by_pdg, + compute_router_share_by_process, +) +from giant.data.transforms import Normalizer +from giant.model.network import build_models + +_PDG_MAP = {11: 0, 22: 1} +_MAT_MAP = {"G4_PbWO4": 0, "G4_Pb": 1} + + +def _model_cfg() -> dict: + return { + "router": { + "enabled": True, + "type": "energy", + "n_experts": 2, + "temperature": 0.5, + "learn_centers": True, + "energy_idx": 3, + }, + "pdg_vocab": len(_PDG_MAP), + "mat_vocab": len(_MAT_MAP), + "conditioning": "embedding", + } + + +def _write_checkpoint(tmp_path) -> str: + cfg = _model_cfg() + stage1, _ = build_models(cfg) + norm = Normalizer() + norm.mean = np.zeros(15, dtype=np.float32) + norm.std = np.ones(15, dtype=np.float32) + ckpt = { + "model_config": cfg, + "model": stage1.state_dict(), + "pdg_map": _PDG_MAP, + "mat_map": _MAT_MAP, + "normalizer": {"cond": norm.to_dict()}, + } + path = tmp_path / "ckpt.pt" + torch.save(ckpt, path) + return str(path) + + +def _steps_frame(process: bool = False) -> pl.LazyFrame: + n = 40 + rng = np.random.default_rng(0) + pre_e = np.concatenate([rng.uniform(1, 10, n // 2), rng.uniform(100, 1000, n // 2)]) + pdg = np.where(np.arange(n) % 2 == 0, 11, 22) + material = np.where(np.arange(n) % 3 == 0, "G4_Pb", "G4_PbWO4") + data = { + "event_id": np.arange(n), + "pdg": pdg, + "pre_x": np.zeros(n), + "pre_y": np.zeros(n), + "pre_z": np.zeros(n), + "pre_E": pre_e, + "pre_dx": np.zeros(n), + "pre_dy": np.zeros(n), + "pre_dz": np.ones(n), + "post_x": np.zeros(n), + "post_y": np.zeros(n), + "post_z": np.ones(n), + "post_E": pre_e * 0.5, + "post_dx": np.zeros(n), + "post_dy": np.zeros(n), + "post_dz": np.ones(n), + "edep": pre_e * 0.5, + "step_length": np.ones(n), + "material": material, + "layer_id": np.zeros(n, dtype=np.int64), + } + if process: + data["process"] = np.where(pdg == 11, "eIoni", "compt") + return pl.DataFrame(data).lazy() + + +def test_compute_router_gating_shapes(tmp_path): + checkpoint = _write_checkpoint(tmp_path) + lf = _steps_frame() + r = compute_router_gating(checkpoint, lf, lf) + assert r.kind == "router_gating" + assert r.payload["n_experts"] == 2 + for side in ("rollout", "reference"): + means = r.payload[side]["means"] + assert means, f"{side} produced no bins" + assert all(abs(sum(row) - 1.0) < 1e-5 for row in means) + + +def test_compute_router_gating_missing_checkpoint_is_unavailable(): + lf = _steps_frame() + r = compute_router_gating(None, lf, lf) + assert r.kind == "unavailable" + assert "note" in r.payload + assert r.title + + +def test_compute_router_share_by_pdg(tmp_path): + checkpoint = _write_checkpoint(tmp_path) + lf = _steps_frame() + r = compute_router_share_by_pdg(checkpoint, lf, lf, top_pdgs=[11, 22]) + assert r.kind == "router_share" + for side in ("rollout", "reference"): + assert set(r.payload[side]) == {"e-", "gamma"} + for shares in r.payload[side].values(): + assert abs(sum(shares) - 1.0) < 1e-5 + + +def test_compute_router_share_by_process(tmp_path): + checkpoint = _write_checkpoint(tmp_path) + lf = _steps_frame(process=True) + r = compute_router_share_by_process(checkpoint, lf) + assert r.kind == "router_share" + assert set(r.payload["categories"]) <= {"eIoni", "compt"} + for shares in r.payload["reference"].values(): + assert abs(sum(shares) - 1.0) < 1e-5 From b6893b0118df98ccf7fe243131dc203a20e50793 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 24 Jul 2026 13:49:00 +0200 Subject: [PATCH 03/24] chore: remove stray CUDA sanity script and stale Phase 2 planning doc test-cuda.py was a one-off local CUDA check, not part of tests/ or scripts/. docs/phase2_plan.md is superseded by the "Phase 2 (implemented)" section of CLAUDE.md's roadmap. Co-Authored-By: Claude Sonnet 5 --- docs/phase2_plan.md | 172 -------------------------------------------- test-cuda.py | 19 ----- 2 files changed, 191 deletions(-) delete mode 100644 docs/phase2_plan.md delete mode 100644 test-cuda.py diff --git a/docs/phase2_plan.md b/docs/phase2_plan.md deleted file mode 100644 index 596edec..0000000 --- a/docs/phase2_plan.md +++ /dev/null @@ -1,172 +0,0 @@ -# Phase 2: Secondary Particle Prediction - -## Context - -Phase 1 takes `n_sec` (secondary count) and `e_sec` (total secondary energy) as **conditioning inputs**. Phase 2 must instead **predict** them, making the surrogate self-contained for shower rollout. Per Jan's 2026-06-29 decision: hard discrete `n_sec` integer head; escalation to Gumbel-Softmax only if empirically needed. - -Two-stage factorization: -- **Stage 1**: existing 9D flow model (reduced conditioning: drop `n_sec` + `log(e_sec)`) + a new discrete `n_sec` classification head -- **Stage 2**: non-AR flow matching over `K_MAX` secondary slots simultaneously, each slot predicting `(stick_break_logit, dir_local_3D, type_emb)` — conditioned on pre-step state + Stage 1 output; padded slots masked from loss - -Training: joint, combined loss `L = L_flow_s1 + λ_nsec * L_nsec + λ_s2 * L_flow_s2`. - ---- - -## Prerequisite: Determine K_MAX - -Before implementing, run a quick analysis over existing parquet files to find `max(n_sec)` and the 99th percentile. Expected to be 5–20 for EM shower steps. Set `K_MAX` as a constant in `giant/constants.py` (suggest 15 as a starting point, revise from data). - ---- - -## New Branch - -```bash -git checkout -b phase2-secondary-prediction master -``` - ---- - -## Part A — Data Pipeline - -### A1. `scripts/steps_to_parquet.py` - -Extend `_add_secondary_energy` to also collect per-secondary attributes from the spawning tree join: -- For each `child_track_id`, look up the child's first step → get `pdg`, `pre_E`, `pre_dx/dy/dz` -- Emit list columns in the parquet: `sec_pdg_list`, `sec_E_list`, `sec_dx_list`, `sec_dy_list`, `sec_dz_list` -- Lists are sorted **descending by energy** at write time -- Truncate to `K_MAX` entries if needed (flag if any row truncated) - -Re-run ROOT→parquet conversion after this change. - -### A2. `giant/data/loader.py` - -In `_df_to_dict`: read the five new list columns. Pad each to length `K_MAX` with zeros (energy) / sentinel values (pdg → 0, dir → (0,0,1)). Return as fixed-shape arrays `(N, K_MAX)` / `(N, K_MAX, 3)`. - -Also return a boolean validity mask `sec_valid` of shape `(N, K_MAX)`: `True` for slots `i < n_sec`. - -### A3. `giant/data/transforms.py` - -Add `encode_secondaries(sec_pdg_list, sec_E_list, sec_dir_list, sec_valid, e_sec, pdg_emb_weight, pre_dir, K_MAX)`: -1. **Direction**: call existing `local_frame_rotation` per slot -2. **Energy (stick-breaking)**: - - Slot 0: `f_0 = E_0 / e_sec` → logit `log(f_0/(1-f_0))` (clamped) - - Slot i: `f_i = E_i / (e_sec - sum(E_0..E_{i-1}))` → logit - - Last valid slot: logit = large positive constant (takes all remaining budget) - - Padding slots (beyond `n_sec`): set logit = 0, masked out of loss anyway -3. **Type embedding**: index into `pdg_emb_weight` (the PDG embedding table weights) to get the target embedding vector for each secondary's `pdg`. Shape `(K_MAX, emb_dim)`. - -Returns `sec_targets: (K_MAX, 1 + 3 + emb_dim)` and `sec_valid: (K_MAX,)`. - -Inverse (`decode_secondaries`): sigmoid stick-breaking fractions → energies, inv local frame rotation → world dirs, nearest-neighbor lookup in PDG embedding table → pdg code. - -### A4. `giant/data/dataset.py` - -Update `build_features` and `StreamingStepsDataset.__iter__` to also yield `sec_targets` and `sec_valid` alongside the existing `(cond_cont, cond_cat, x1)` batch items. - ---- - -## Part B — Constants (`giant/constants.py`) - -- `COND_DIM`: 10 → **8** (remove `n_sec` and `log(e_sec)`) -- Add `K_MAX: int` (set after data analysis, e.g. 15) -- Add `SEC_SLOT_DIM: int` (= 4 + `emb_dim` = 20 for default emb_dim=16; 1 stick + 3 dir + 16 type) -- Add `SEC_DIM: int = K_MAX * SEC_SLOT_DIM` (flattened Stage 2 target dimension) -- Update `LOCAL_TARGET_NAMES` (Stage 1 only, still 9D) - ---- - -## Part C — Model (`giant/model/network.py`) - -### C1. `DenoisingMLP` — Stage 1 (minimal changes) - -- `ConditionEncoder.cont_dim` drops from 10 to 8 (COND_DIM change propagates automatically) -- Add `n_sec_head = nn.Sequential(Linear(cond_out_dim, hidden_dim//2), SiLU(), Linear(hidden_dim//2, K_MAX + 1))` applied to `c_emb` (the condition encoding, not the diffused latent) -- Add method `predict_n_sec(cond_cont, cond_cat) -> Tensor[B, K_MAX+1]` — no diffusion, just encode conditioning and run the head - -### C2. `SecondaryDecoder` — Stage 2 (new class) - -Architecture mirrors `DenoisingMLP` but: -- **Input**: `x_t` of shape `(B, SEC_DIM)` (flattened K_MAX secondary slots) -- **Conditioning**: pre-step state (8D cont + 2 cat → same ConditionEncoder as Stage 1) concatenated with Stage 1 output (9D normalized target, detached from Stage 1 loss for stability initially). Total cond dim to the ResBlocks: `time_dim + cond_s1_out_dim + 9` -- **Output**: vector field of shape `(B, SEC_DIM)` -- Uses same `ResBlock` / `SinusoidalEmbedding` / `ConditionEncoder` building blocks - -A `SecondaryConditionEncoder` wraps the base `ConditionEncoder` and concatenates the Stage 1 output: -```python -class SecondaryConditionEncoder(nn.Module): - # base: ConditionEncoder(pdg_vocab, mat_vocab, 8, emb_dim, cond_out_dim) - # stage1_proj: Linear(X_DIM, stage1_cond_dim) - # mlp: fuses both -``` - ---- - -## Part D — Loss / Training - -### `giant/model/schedule.py` - -Add `flow_matching_loss_masked(model, x1, cond_cont, cond_cat, mask)`: -- Same as `flow_matching_loss` but divides by `mask.sum()` instead of `B * SEC_DIM`, zeroing out padded slots before averaging. `mask` shape: `(B, K_MAX)`, broadcast over slot dims. - -### `giant/train.py` - -Batch now unpacks as `(cond_cont, cond_cat, x1_s1, n_sec_target, x1_s2, sec_mask)`. - -Combined loss per batch: -``` -L_s1 = flow_matching_loss(stage1_model, x1_s1, cond_cont, cond_cat) -L_nsec = cross_entropy(stage1_model.predict_n_sec(cond_cont, cond_cat), n_sec_target) -L_s2 = flow_matching_loss_masked(sec_decoder, x1_s2, cond_cont, cond_cat, stage1_detached, sec_mask) -L = L_s1 + lambda_nsec * L_nsec + lambda_s2 * L_s2 -``` - -Config adds `lambda_nsec` (suggest 0.1) and `lambda_s2` (suggest 1.0) under `[train]`. - -Both `stage1_model` and `sec_decoder` share a single `optimizer` (AdamW over all parameters). - -Checkpoint saves both `stage1_model.state_dict()` and `sec_decoder.state_dict()`, plus `K_MAX` and `SEC_SLOT_DIM` in `model_config`. - -### `giant/pipeline.py` - -- Compute `K_MAX` from data (max `n_sec` over training events) before constructing models -- Build both `DenoisingMLP` and `SecondaryDecoder`, pass both to `run_training` - ---- - -## Part E — Sampling (`giant/sample.py`) - -```python -def sample_stage1(model, cond_cont, cond_cat, steps=10): - # Euler ODE → primary sample (9D), + argmax n_sec head - ... - -def sample_secondaries(sec_decoder, cond_cont, cond_cat, stage1_out, n_sec, steps=10): - # Euler ODE on SEC_DIM → decode stick-breaking → energies - # inv_local_frame_rotation → world-frame dirs - # nearest-neighbor in pdg_emb_weight → pdg codes - # mask slots >= n_sec - ... -``` - ---- - -## Part F — Wiring - -- **`giant/validate.py`**: add secondary-specific marginals (n_sec distribution, species distribution, energy fraction per slot) -- **`giant/cli.py`**: `predict` command loads both checkpoints, calls both samplers, appends secondary columns to output parquet - ---- - -## Type embedding design note - -The type embedding target at training is `pdg_emb.weight[sec_pdg_idx]` (the Stage 1 PDG embedding table rows). Gradients flow into the embedding table from both the conditioning path (input PDG) and the secondary type loss — this is intentional; the shared embedding space is the bridge. At inference, snap: `argmin_k ||pred_emb - pdg_emb.weight[k]||`. - ---- - -## Verification - -1. `uv run pytest` — existing tests pass (Stage 1 shape/interface unchanged beyond COND_DIM) -2. Unit tests for `encode_secondaries` / `decode_secondaries` (round-trip: energies sum to `e_sec`, directions are unit vectors) -3. Unit test for `flow_matching_loss_masked`: verify padded slots contribute zero gradient -4. Short training run (1–2 epochs): confirm all three loss components decrease -5. Sampling smoke test: verify `sum(sec_E) ≈ e_sec` per sample, all directions unit-normed diff --git a/test-cuda.py b/test-cuda.py deleted file mode 100644 index 2b95e64..0000000 --- a/test-cuda.py +++ /dev/null @@ -1,19 +0,0 @@ -import torch -import torch.version - -print(f"PyTorch version: {torch.__version__}") -print(f"CUDA available: {torch.cuda.is_available()}") - -if torch.cuda.is_available(): - print(f"CUDA version: {torch.version.cuda}") - print(f"Device count: {torch.cuda.device_count()}") - print(f"Device name: {torch.cuda.get_device_name(0)}") - - # Run a small tensor op on the GPU - a = torch.randn(1000, 1000, device="cuda") - b = torch.randn(1000, 1000, device="cuda") - c = a @ b - torch.cuda.synchronize() - print(f"Matrix multiply: OK (result shape {c.shape}, device {c.device})") -else: - print("No CUDA device found — check driver/CUDA installation.") From 80d544aa73c09942dfce6c21d451507ec87ac098 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 24 Jul 2026 14:20:14 +0200 Subject: [PATCH 04/24] docs: document compute environment, WGAN/MoE status, and condor-gpu-train-rollout Adds a Compute environment section (laptop/desktop vs. shared portal machines vs. condor workers) and corrects the roadmap: the WGAN-GP and MoE routing-trunk tracks are actually implemented (untested and under-testing respectively), not "not yet built" as previously stated. Also notes the in-progress condor-gpu-train-rollout branch. Co-Authored-By: Claude Sonnet 5 --- CLAUDE.md | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/CLAUDE.md b/CLAUDE.md index 81fcdae..32a2617 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -12,6 +12,8 @@ uv sync --extra cpu --extra geometry # add scikit-learn for the geometry oracle pytest # run tests giant train path/to/steps.parquet --mode flow # train (flow matching) giant train path/to/steps.parquet --mode ddpm # train (DDPM baseline) +giant train path/to/steps.parquet --mode wgan # train (WGAN-GP, single-pass eval; implemented, not yet tested) +giant train path/to/steps.parquet --router --router-type energy # MoE routing trunk (implemented, currently under testing) giant predict path/to/steps.parquet --checkpoint ckpt/best.pt # per-step predictions giant rollout path/to/steps.parquet --checkpoint ckpt/best.pt --geometry oracle.pkl # full showers giant analyze submit rollout.yaml --accounting-group cms # parallel rollout-vs-reference analysis on HTCondor @@ -34,6 +36,14 @@ uv run ty check . # type check Part of the `dev` extra. Run these periodically (not just at commit time) to catch drift early. +## Compute environment + +Work on this repo happens across three kinds of machine: + +- **Local dev machines** (laptop + desktop, identical): repo at `~/Programming/giant`, no access to `/ceph` — datasets, training results, and models aren't reachable here. +- **Portal machines** (`portal1`, `deepthought`, `deepthought2`, `bms1`, `bms2`, `bms3`): repo lives under `/work`, and `/ceph` holds ROOT/parquet files and trained models. **These are shared with other users** — stay strictly within `/work/lbogner` and `/ceph/lbogner`, and keep resource usage to roughly a quarter of CPU/RAM and a single GPU so as not to disturb other users' jobs. +- **HTCondor worker nodes**: never run or SSH onto these directly — the only sanctioned path is submitting jobs through condor (`giant analyze submit`, and the in-progress remote-GPU train/rollout submission on `condor-gpu-train-rollout`). `/ceph` is available there; `/work` is only sometimes mounted, depending on the node. + ## Architecture GIANT is a conditional generative surrogate for the Geant4 step function. It replaces the stochastic physics engine: given a pre-step particle state (conditioning), it samples a post-step outcome — now including the variable-length list of secondary particles the step produces (Phase 2, see Roadmap). @@ -56,6 +66,10 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep **Samplers** (`giant/sample.py`): DDPM, DDIM, and flow matching (ODE integration, ~10 steps). Flow matching is the primary mode. +**WGAN-GP mode (`--mode wgan`, implemented, not yet tested):** a throwaway fast-eval alternative to the flow/DDPM samplers above — single forward pass instead of ~10 ODE steps. Dedicated noise-conditioned generators (`WGANGenerator`/`WGANSecondaryGenerator`, `giant/model/network.py`) stand in for `DenoisingMLP`/`SecondaryDecoder`, trained against `Critic`/`SecondaryCritic` discriminators with the gradient-penalty loss in `giant/model/wgan.py` (Gulrajani et al. 2017); `sample_wgan` (`giant/sample.py`) does the single-pass draw at inference. Not yet validated against the flow-matching baseline. + +**MoE routing trunk (`--router`, implemented, currently under testing):** an alternative to `DenoisingMLP`'s monolithic `ResBlock` trunk — a `Router` (`giant/model/network.py`, `ROUTER_REGISTRY`/`build_router`) gates between small per-expert `ResBlock` stacks (`Expert`), soft-mixed over all experts at train time but **top-1 dispatched at eval time** (each row runs exactly one small expert), which is the actual inference-speed win. Router types gate on different conditioning axes: `EnergyRouter`/`PdgRouter` read a quantity already known at inference time, `ProcessRouter` runs its own small classifier over pre-step conditioning (since process isn't known upfront); `ComposedRouter` gates jointly over multiple axes (outer-product expert cells) via repeated `--router-axis "type:key=val,..."` flags. Config lives under `model.router` (`giant/config.py`), deep-merged one level so `router.enabled` alone doesn't drop the rest of the defaults. + **Validation** (`giant/validate.py`): step-level marginal comparisons. **Analysis** (`giant/analysis/`, `giant analyze` CLI): a lean, streaming rollout-vs-reference plotting pipeline that compares one autoregressive `giant rollout` (for a given checkpoint) against a held-out miniCaloSim reference steps file, and produces publication-styled PDFs assembled into an HTML gallery. It exploits the fact that rollout output and a raw reference file share a world-frame physical column subset under identical names (`pre_*`/`post_*`/`edep`/`step_length`/`pdg`/`material`/`event_id`), so no ALR/local-frame decode is needed — everything is world-frame mm/MeV. Structure: `sources.py` (canonical LazyFrames + synthetic-termination-row filtering + the secondary view, which is `generation>0 & step_no==0` rollout tracks vs exploded `sec_*_list` reference columns), `reduce.py` (the streaming primitives — a single `hist1d` `group_by([group,bin]).len()` pass, per-event scalars, edep-weighted depth/transverse profiles, species share, leakage), `grouping.py`/`context.py` (fixed bin edges + energy-quantile/pdg/material group sets resolved once by `prep` into `shared.json`, so every compute job is one pass with no range scan), `catalog.py` (the declarative `PlotSpec` registry — marginals × {overall,energy,pdg,material}, per-event totals, shower profiles, species/leakage, secondaries), and `render.py` (the only module importing ETPlot's `plotstyle`/LaTeX; dispatches on `Reduced.kind`, writes PDFs + `metadata.yaml`). **Input is a `giant rollout` YAML sidecar** (`condor.py:load_rollout_yaml`): its `output`/`dataset` keys name the rollout parquet and the seed file (= the reference truth), and the rest of the YAML (checkpoint, geometry oracle, cutoffs) flows into each plot's gallery metadata. `prep` derives its own **run directory** next to the rollout parquet (`<...>/analysis_/`) holding `shared.json`, `run_meta.json`, `reduced/`, `plots/`. **Compute/render split:** `giant analyze submit rollout.yaml` runs `prep` then submits one HTCondor job per plot (`compute-one --run-dir`, polars/numpy only — no LaTeX on workers), each writing a small `reduced/.json`; the local `giant analyze render ` turns those into the styled PDF/gallery tree. See `giant/analysis/__init__.py`. @@ -70,4 +84,10 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep **Physical-property conditioning (implemented):** `model.conditioning = "physical" | "embedding"` (see above) replaces the learned PDG/material embeddings with a small MLP over particle mass/charge and material Z_eff/A_eff/density/X0/λ_int, and Stage 2 predicts a secondary's mass/charge directly instead of a snapped species embedding. `"embedding"` stays available as the generalization-comparison baseline. `giant/materials.py`'s table is already filled with real values for every material the geometry produces. **Not yet done:** the actual held-out-material/species generalization comparison against the `"embedding"` baseline is unrun — the 34GB multi-material dataset at the repo root (6 materials, 237 PDG codes including nuclear/ion codes) is the natural dataset for that experiment. -**Next directions** (parallel, not yet built): faster-eval architectures measured against a ~10× native-Geant4 budget — a Wasserstein-GAN throwaway (single-pass eval) and a mixture-of-experts / routing tree of small nets selected per call (pdg / energy / process), with soft/differentiable gating on continuous routing axes; a sampling-calorimeter (multi-material) dataset. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`). +**Faster-eval architectures (implemented, validation in progress):** both tracks below target a ~10× native-Geant4 eval budget and are now wired into `giant train`/`giant/model/network.py`, but neither has a validated result yet — treat both as unproven until the corresponding analysis run says otherwise: +- **WGAN-GP** (`--mode wgan`, see Architecture above): implemented, **not yet tested** — no rollout-vs-reference analysis run against it yet. +- **MoE routing trunk** (`--router`, see Architecture above): implemented, **currently undergoing testing** — this is what the `giant analyze` MoE router gating/share diagnostic plots (`giant/analysis/router_gating.py`) were built to evaluate. + +A sampling-calorimeter (multi-material) dataset is still a planned future direction, not yet built. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`). + +**Condor-submitted GPU training/rollout (in progress, `condor-gpu-train-rollout` branch, not yet merged):** moves `giant train`/`giant rollout` off the shared portal GPU dev machines (see Compute environment) onto remote-GPU HTCondor submission on TOpAS/NEMO2 (`giant/condor.py`). Partway between "needs major features" and feature-complete — not ready to merge yet. From 70d0f043260e1f98db4d680e87ed3f7acb6a1a03 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Fri, 24 Jul 2026 14:32:06 +0200 Subject: [PATCH 05/24] analyze: normalize pdg dtype in open_side to fix rollout/reference concat Rollout output and the reference file's ROOT-derived parquet disagree on pdg's integer width (Int32 vs Int64), which only surfaced downstream as a pl.concat SchemaError in build_context's pdg-count merge. Cast to a canonical Int64 at the single scan entry point instead. --- giant/analysis/sources.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/giant/analysis/sources.py b/giant/analysis/sources.py index d71b129..2248c90 100644 --- a/giant/analysis/sources.py +++ b/giant/analysis/sources.py @@ -106,18 +106,27 @@ def open_side(source: str | Path | pl.LazyFrame, side: Side) -> pl.LazyFrame: can push their own narrow projection into the parquet read — the single biggest lever on a larger-than-RAM file. ``pl.LazyFrame`` inputs pass straight through (used by tests). + + ``pdg`` is cast to a canonical ``Int64`` here: the rollout writer and the + reference file's upstream ROOT→parquet conversion don't agree on integer + width, and an uncast mismatch only surfaces later as a ``pl.concat`` + ``SchemaError`` (e.g. in ``build_context``'s pdg-count merge). """ if isinstance(source, pl.LazyFrame): - return source + return source.with_columns(pl.col("pdg").cast(pl.Int64)) path = Path(source) if side is Side.rollout: _check_rollout_metadata(path) - return pl.scan_parquet(path) - # The reference (a rollout's seed `dataset`) may be a directory of parquet - # shards rather than a single file — scan them all. - if path.is_dir(): - return pl.scan_parquet(str(path / "**/*.parquet")) - return pl.scan_parquet(path) + lf = pl.scan_parquet(path) + else: + # The reference (a rollout's seed `dataset`) may be a directory of + # parquet shards rather than a single file — scan them all. + lf = ( + pl.scan_parquet(str(path / "**/*.parquet")) + if path.is_dir() + else pl.scan_parquet(path) + ) + return lf.with_columns(pl.col("pdg").cast(pl.Int64)) def physical_steps(lf: pl.LazyFrame, side: Side) -> pl.LazyFrame: From 86fc46b5a809cd916bff16373dc5e93bd79d7bea Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 09:24:19 +0200 Subject: [PATCH 06/24] analyze: chunk per-plot aggregation across HTCondor jobs Add a second parallelism axis to giant analyze: each plot's data can now be split into a configurable number of event_id-disjoint chunks, each computed as its own HTCondor job, bounding per-job walltime and scan cost on large rollout/reference files instead of one job re-scanning the whole file per plot. Every PlotSpec now splits into compute_partial (runs per (plot, chunk) job against a chunk-filtered Bundle) and finalize (merges chunks - elementwise sum for fixed-edge histograms/species shares, concatenate -then-recompute for specs that derive edges or mean/std from the full per-event/per-secondary array). Router diagnostics stay chunkable=False and always run as a single job. giant analyze render now joins every plot's chunk partials (merge_all) before rendering, transparently. New: --chunks on `analyze prep`/`analyze submit`, --chunk on `analyze compute-one`, and a new `analyze merge-one` command. --- CLAUDE.md | 2 +- giant/analysis/__init__.py | 10 +- giant/analysis/catalog.py | 508 +++++++++++++++++++++++++++---------- giant/analysis/condor.py | 150 +++++++++-- giant/analysis/reduce.py | 63 ++++- giant/analysis/reduced.py | 24 ++ giant/analysis/render.py | 10 +- giant/cli.py | 46 +++- tests/test_catalog.py | 84 +++++- tests/test_condor.py | 82 +++++- 10 files changed, 787 insertions(+), 192 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 32a2617..bbeb296 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -72,7 +72,7 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep **Validation** (`giant/validate.py`): step-level marginal comparisons. -**Analysis** (`giant/analysis/`, `giant analyze` CLI): a lean, streaming rollout-vs-reference plotting pipeline that compares one autoregressive `giant rollout` (for a given checkpoint) against a held-out miniCaloSim reference steps file, and produces publication-styled PDFs assembled into an HTML gallery. It exploits the fact that rollout output and a raw reference file share a world-frame physical column subset under identical names (`pre_*`/`post_*`/`edep`/`step_length`/`pdg`/`material`/`event_id`), so no ALR/local-frame decode is needed — everything is world-frame mm/MeV. Structure: `sources.py` (canonical LazyFrames + synthetic-termination-row filtering + the secondary view, which is `generation>0 & step_no==0` rollout tracks vs exploded `sec_*_list` reference columns), `reduce.py` (the streaming primitives — a single `hist1d` `group_by([group,bin]).len()` pass, per-event scalars, edep-weighted depth/transverse profiles, species share, leakage), `grouping.py`/`context.py` (fixed bin edges + energy-quantile/pdg/material group sets resolved once by `prep` into `shared.json`, so every compute job is one pass with no range scan), `catalog.py` (the declarative `PlotSpec` registry — marginals × {overall,energy,pdg,material}, per-event totals, shower profiles, species/leakage, secondaries), and `render.py` (the only module importing ETPlot's `plotstyle`/LaTeX; dispatches on `Reduced.kind`, writes PDFs + `metadata.yaml`). **Input is a `giant rollout` YAML sidecar** (`condor.py:load_rollout_yaml`): its `output`/`dataset` keys name the rollout parquet and the seed file (= the reference truth), and the rest of the YAML (checkpoint, geometry oracle, cutoffs) flows into each plot's gallery metadata. `prep` derives its own **run directory** next to the rollout parquet (`<...>/analysis_/`) holding `shared.json`, `run_meta.json`, `reduced/`, `plots/`. **Compute/render split:** `giant analyze submit rollout.yaml` runs `prep` then submits one HTCondor job per plot (`compute-one --run-dir`, polars/numpy only — no LaTeX on workers), each writing a small `reduced/.json`; the local `giant analyze render ` turns those into the styled PDF/gallery tree. See `giant/analysis/__init__.py`. +**Analysis** (`giant/analysis/`, `giant analyze` CLI): a lean, streaming rollout-vs-reference plotting pipeline that compares one autoregressive `giant rollout` (for a given checkpoint) against a held-out miniCaloSim reference steps file, and produces publication-styled PDFs assembled into an HTML gallery. It exploits the fact that rollout output and a raw reference file share a world-frame physical column subset under identical names (`pre_*`/`post_*`/`edep`/`step_length`/`pdg`/`material`/`event_id`), so no ALR/local-frame decode is needed — everything is world-frame mm/MeV. Structure: `sources.py` (canonical LazyFrames + synthetic-termination-row filtering + the secondary view, which is `generation>0 & step_no==0` rollout tracks vs exploded `sec_*_list` reference columns), `reduce.py` (the streaming primitives — a single `hist1d` `group_by([group,bin]).len()` pass, per-event scalars, edep-weighted depth/transverse profiles, species share, leakage), `grouping.py`/`context.py` (fixed bin edges + energy-quantile/pdg/material group sets resolved once by `prep` into `shared.json`, so every compute job is one pass with no range scan), `catalog.py` (the declarative `PlotSpec` registry — marginals × {overall,energy,pdg,material}, per-event totals, shower profiles, species/leakage, secondaries), and `render.py` (the only module importing ETPlot's `plotstyle`/LaTeX; dispatches on `Reduced.kind`, writes PDFs + `metadata.yaml`). **Input is a `giant rollout` YAML sidecar** (`condor.py:load_rollout_yaml`): its `output`/`dataset` keys name the rollout parquet and the seed file (= the reference truth), and the rest of the YAML (checkpoint, geometry oracle, cutoffs) flows into each plot's gallery metadata. `prep` derives its own **run directory** next to the rollout parquet (`<...>/analysis_/`) holding `shared.json`, `run_meta.json`, `reduced_partial/`, `reduced/`, `plots/`. **Compute/merge/render split:** `giant analyze submit rollout.yaml --chunks N` runs `prep` (recording the run's chunk count `N` in `run_meta.json`) then submits one HTCondor job per (plot, chunk) pair (`compute-one --id --chunk --run-dir`, polars/numpy only — no LaTeX on workers), each streaming over an `event_id`-disjoint slice (`event_id % N == chunk`) and writing a small `reduced_partial/__.json`; every `PlotSpec` (`catalog.py`) splits into a `compute_partial`/`finalize` pair so a plot's chunks can be summed/concatenated back together correctly (`chunkable=False` specs — the router diagnostics, already bounded/subsampled — always run as a single chunk regardless of `N`). The local `giant analyze render ` first joins every plot's chunk partials into `reduced/.json` (`merge_all`, a no-op join when `N=1`), then turns those into the styled PDF/gallery tree. See `giant/analysis/__init__.py`. **Shower rollout** (`giant/rollout.py`, `giant rollout` CLI): autoregressively steps the two-stage model into a full shower — each primary post-step becomes the next pre-step, secondaries are pushed as new tracks, and per-step `material`/`layer_id` come from a `GeometryOracle` (`giant/geometry.py`, built via `dwarf build-geometry-oracle`) that learns position → (material, layer_id) from data and flags detector escape by nearest-neighbour distance. Tracks terminate on energy cutoff, per-track max steps, escape, or natural end; energy is deposited locally on every stop except escape (leakage), so showers conserve energy by construction. diff --git a/giant/analysis/__init__.py b/giant/analysis/__init__.py index a646f80..8af48cb 100644 --- a/giant/analysis/__init__.py +++ b/giant/analysis/__init__.py @@ -2,7 +2,8 @@ Compares one autoregressive ``giant rollout`` against a held-out miniCaloSim reference file, producing publication-styled comparison plots generated in -parallel on HTCondor (one job per plot, compute/render split). +parallel on HTCondor (one job per plot x data chunk, compute/merge/render +split). Only ``render`` (and the ``render`` CLI path) imports plotstyle/LaTeX; everything re-exported here is plotstyle-free so it runs on a compute worker. Import @@ -17,11 +18,13 @@ from giant.analysis.condor import ( compute_reduced, derive_run_dir, load_rollout_yaml, + merge_all, + merge_one, prep, write_submit, ) from giant.analysis.context import Context, build_context -from giant.analysis.reduced import Reduced +from giant.analysis.reduced import Partial, Reduced from giant.analysis.sources import Side __all__ = [ @@ -34,10 +37,13 @@ __all__ = [ "compute_reduced", "derive_run_dir", "load_rollout_yaml", + "merge_all", + "merge_one", "prep", "write_submit", "Context", "build_context", + "Partial", "Reduced", "Side", ] diff --git a/giant/analysis/catalog.py b/giant/analysis/catalog.py index a0e5bc0..fc665e3 100644 --- a/giant/analysis/catalog.py +++ b/giant/analysis/catalog.py @@ -2,9 +2,22 @@ Each spec knows its stable ``id`` (used for the reduced-data filename, the PDF stem and the condor queue item), its gallery ``family`` (subdirectory), and a -``compute(bundle) -> Reduced`` that runs the streaming reduction. Rendering lives -in ``render.py`` and dispatches on ``Reduced.kind`` — the catalog itself never -imports plotstyle, so ``compute-one`` jobs stay LaTeX-free. +``compute_partial(bundle) -> dict`` / ``finalize(parts, ctx) -> Reduced`` pair +that together run the streaming reduction. ``compute_partial`` runs once per +``(plot, chunk)`` condor job against a ``Bundle`` whose four LazyFrames are +already filtered to that chunk (see ``Bundle.open``'s ``chunk`` argument); it +returns a small JSON-safe partial artifact — either a raw sum-mergeable count +dict (histograms/species sums against fixed edges) or a raw per-event/ +per-secondary array to be concatenated (anything that derives its own edges or +a mean/std from the full dataset). ``finalize`` merges the per-chunk partials +(in chunk order) and does the actual histogramming/edge-selection/mean-std +collapse, once, over the merged data — for ``n_chunks=1`` this reproduces +exactly what a single unchunked pass would produce. Specs marked +``chunkable=False`` (the router ones) always run as a single chunk regardless +of the configured chunk count. + +Rendering lives in ``render.py`` and dispatches on ``Reduced.kind`` — the +catalog itself never imports plotstyle, so ``compute-one`` jobs stay LaTeX-free. The registry is built by expanding parametric families (marginals over variable x grouping, secondaries, ...) into concrete specs. @@ -12,7 +25,7 @@ variable x grouping, secondaries, ...) into concrete specs. from __future__ import annotations -from dataclasses import dataclass +from dataclasses import asdict, dataclass from typing import Callable import numpy as np @@ -32,9 +45,11 @@ from giant.analysis.reduce import ( event_scalars, hist1d, leakage_fraction, + profile_finalize, + profile_partial, species_share, + sum_merge, transverse_expr, - weighted_profile, ) from giant.analysis.reduced import Reduced from giant.analysis.router_gating import ( @@ -58,9 +73,29 @@ class Bundle: checkpoint: str | None = None # from the rollout YAML; router_gating only @classmethod - def open(cls, rollout, reference, ctx: Context, checkpoint=None) -> "Bundle": + def open( + cls, + rollout, + reference, + ctx: Context, + checkpoint=None, + chunk: tuple[int, int] | None = None, + ) -> "Bundle": + """Open both sides, optionally restricted to one event-disjoint chunk. + + ``chunk = (chunk_index, n_chunks)`` filters both sides to + ``event_id % n_chunks == chunk_index`` *before* deriving the physical/ + secondary views, so every downstream reduction (which is either + row-local or a ``group_by("event_id")``) sees a self-contained, + event-disjoint slice — no cross-chunk lookups are ever needed. + """ r_all = open_side(rollout, Side.rollout) t_all = open_side(reference, Side.reference) + if chunk is not None: + idx, n = chunk + pred = pl.col("event_id") % n == idx + r_all = r_all.filter(pred) + t_all = t_all.filter(pred) return cls( ctx=ctx, r_all=r_all, @@ -75,7 +110,26 @@ class Bundle: class PlotSpec: id: str family: str - compute: Callable[[Bundle], Reduced] + compute_partial: Callable[[Bundle], dict] + finalize: Callable[[list[dict], Context], Reduced] + chunkable: bool = True + + +def _unchunkable( + compute: Callable[[Bundle], Reduced], +) -> tuple[Callable[[Bundle], dict], Callable[[list[dict], Context], Reduced]]: + """Wrap a whole-dataset ``compute(bundle) -> Reduced`` as a trivial + ``(compute_partial, finalize)`` pair, for specs marked ``chunkable=False`` + (which always run as a single chunk, so ``parts`` is always one element). + """ + + def partial(b: Bundle) -> dict: + return {"reduced": asdict(compute(b))} + + def finalize(parts: list[dict], ctx: Context) -> Reduced: + return Reduced(**parts[0]["reduced"]) + + return partial, finalize # --------------------------------------------------------------------------- @@ -90,6 +144,20 @@ def _counts(h: dict, key, nbins: int) -> list[int]: return h.get(key, np.zeros(nbins, dtype=np.int64)).astype(np.int64).tolist() +def _partial_hist( + lf: pl.LazyFrame, value: pl.Expr, edges: np.ndarray, group: pl.Expr | None = None +) -> dict[str, list[int]]: + """One chunk's raw ``hist1d`` result as a JSON-safe, sum-mergeable dict.""" + nb = len(edges) - 1 + h = hist1d(lf, value, edges, group=group) + return {str(k): _counts(h, k, nb) for k in h} + + +def _finalize_counts(merged: dict[str, list], key, nbins: int) -> list[int]: + """One group's merged counts (zero-filled if the group never appeared).""" + return list(merged.get(str(key), [0] * nbins)) + + def _np_hist_pair( r: np.ndarray, t: np.ndarray, nbins: int ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: @@ -132,12 +200,21 @@ def _marginal_edges(ctx: Context, var: str) -> np.ndarray: # --------------------------------------------------------------------------- -def _marginal_overall(b: Bundle, var: str) -> Reduced: - label, expr = _var(var) +def _marginal_overall_partial(b: Bundle, var: str) -> dict: + _, expr = _var(var) edges = _marginal_edges(b.ctx, var) + return { + "r": _partial_hist(b.r_phys, expr, edges), + "t": _partial_hist(b.t_phys, expr, edges), + } + + +def _marginal_overall_finalize(parts: list[dict], ctx: Context, var: str) -> Reduced: + label, _ = _var(var) + edges = _marginal_edges(ctx, var) nb = len(edges) - 1 - r = hist1d(b.r_phys, expr, edges) - t = hist1d(b.t_phys, expr, edges) + r = sum_merge([p["r"] for p in parts]) + t = sum_merge([p["t"] for p in parts]) return Reduced( id=f"marginal_{var}", family="marginals", @@ -146,8 +223,8 @@ def _marginal_overall(b: Bundle, var: str) -> Reduced: xlabel=label, payload={ "edges": edges.tolist(), - _ROLL: _counts(r, 0, nb), - _REF: _counts(t, 0, nb), + _ROLL: _finalize_counts(r, 0, nb), + _REF: _finalize_counts(t, 0, nb), "log_y": True, }, ) @@ -160,31 +237,55 @@ def _energy_group_expr(lf: pl.LazyFrame, edges: np.ndarray) -> pl.Expr: ) -def _marginal_grouped(b: Bundle, var: str, axis: str) -> Reduced: - label, expr = _var(var) +def _marginal_grouped_partial(b: Bundle, var: str, axis: str) -> dict: + _, expr = _var(var) edges = _marginal_edges(b.ctx, var) - nb = len(edges) - 1 - groups: dict[str, dict] = {} - if axis == "pdg": r = hist1d(b.r_phys, expr, edges, group=pl.col("pdg")) t = hist1d(b.t_phys, expr, edges, group=pl.col("pdg")) - for k in b.ctx.top_pdgs: - groups[pdg_label(k)] = {_ROLL: _counts(r, k, nb), _REF: _counts(t, k, nb)} elif axis == "material": r = hist1d(b.r_phys, expr, edges, group=pl.col("material")) t = hist1d(b.t_phys, expr, edges, group=pl.col("material")) - for m in b.ctx.materials: - groups[material_label(m)] = { - _ROLL: _counts(r, m, nb), - _REF: _counts(t, m, nb), - } else: # energy e_edges = np.asarray(b.ctx.energy_edges) r = hist1d(b.r_phys, expr, edges, group=_energy_group_expr(b.r_phys, e_edges)) t = hist1d(b.t_phys, expr, edges, group=_energy_group_expr(b.t_phys, e_edges)) + nb = len(edges) - 1 + return { + "r": {str(k): _counts(r, k, nb) for k in r}, + "t": {str(k): _counts(t, k, nb) for k in t}, + } + + +def _marginal_grouped_finalize( + parts: list[dict], ctx: Context, var: str, axis: str +) -> Reduced: + label, _ = _var(var) + edges = _marginal_edges(ctx, var) + nb = len(edges) - 1 + r = sum_merge([p["r"] for p in parts]) + t = sum_merge([p["t"] for p in parts]) + groups: dict[str, dict] = {} + + if axis == "pdg": + for k in ctx.top_pdgs: + groups[pdg_label(k)] = { + _ROLL: _finalize_counts(r, k, nb), + _REF: _finalize_counts(t, k, nb), + } + elif axis == "material": + for m in ctx.materials: + groups[material_label(m)] = { + _ROLL: _finalize_counts(r, m, nb), + _REF: _finalize_counts(t, m, nb), + } + else: # energy + e_edges = np.asarray(ctx.energy_edges) for bi, lbl in enumerate(energy_bin_labels(e_edges)): - groups[lbl] = {_ROLL: _counts(r, bi, nb), _REF: _counts(t, bi, nb)} + groups[lbl] = { + _ROLL: _finalize_counts(r, bi, nb), + _REF: _finalize_counts(t, bi, nb), + } return Reduced( id=f"marginal_{var}_by_{axis}", @@ -201,13 +302,19 @@ def _marginal_grouped(b: Bundle, var: str, axis: str) -> Reduced: # --------------------------------------------------------------------------- -def _event_scalar( - b: Bundle, spec_id: str, title: str, xlabel: str, col: str, use_all: bool -) -> Reduced: +def _event_scalar_partial(b: Bundle, col: str, use_all: bool) -> dict: r_lf, t_lf = (b.r_all, b.t_all) if use_all else (b.r_phys, b.t_phys) r = event_scalars(r_lf)[col].to_numpy() t = event_scalars(t_lf)[col].to_numpy() - edges, rc, tc = _np_hist_pair(r, t, b.ctx.n_marginal_bins) + return {"r": r.tolist(), "t": t.tolist()} + + +def _event_scalar_finalize( + parts: list[dict], ctx: Context, spec_id: str, title: str, xlabel: str +) -> Reduced: + r = np.concatenate([np.asarray(p["r"], dtype=float) for p in parts]) + t = np.concatenate([np.asarray(p["t"], dtype=float) for p in parts]) + edges, rc, tc = _np_hist_pair(r, t, ctx.n_marginal_bins) return Reduced( id=spec_id, family="event", @@ -223,18 +330,26 @@ def _event_scalar( ) -def _event_total_edep_by_energy(b: Bundle) -> Reduced: - e_edges = np.asarray(b.ctx.energy_edges) +def _event_total_edep_by_energy_partial(b: Bundle) -> dict: r = event_scalars(b.r_all) t = event_scalars(b.t_all) - r_bin = np.clip( - np.digitize(r["incident_E"].to_numpy(), e_edges[1:-1]), 0, len(e_edges) - 2 - ) - t_bin = np.clip( - np.digitize(t["incident_E"].to_numpy(), e_edges[1:-1]), 0, len(e_edges) - 2 - ) - r_val, t_val = r["total_edep"].to_numpy(), t["total_edep"].to_numpy() - edges, _, _ = _np_hist_pair(r_val, t_val, b.ctx.n_marginal_bins) + return { + "r_incident": r["incident_E"].to_list(), + "r_edep": r["total_edep"].to_list(), + "t_incident": t["incident_E"].to_list(), + "t_edep": t["total_edep"].to_list(), + } + + +def _event_total_edep_by_energy_finalize(parts: list[dict], ctx: Context) -> Reduced: + e_edges = np.asarray(ctx.energy_edges) + r_inc = np.concatenate([np.asarray(p["r_incident"], dtype=float) for p in parts]) + r_val = np.concatenate([np.asarray(p["r_edep"], dtype=float) for p in parts]) + t_inc = np.concatenate([np.asarray(p["t_incident"], dtype=float) for p in parts]) + t_val = np.concatenate([np.asarray(p["t_edep"], dtype=float) for p in parts]) + r_bin = np.clip(np.digitize(r_inc, e_edges[1:-1]), 0, len(e_edges) - 2) + t_bin = np.clip(np.digitize(t_inc, e_edges[1:-1]), 0, len(e_edges) - 2) + edges, _, _ = _np_hist_pair(r_val, t_val, ctx.n_marginal_bins) groups: dict[str, dict] = {} for bi, lbl in enumerate(energy_bin_labels(e_edges)): rc = np.histogram(r_val[r_bin == bi], edges)[0] @@ -258,15 +373,54 @@ def _event_total_edep_by_energy(b: Bundle) -> Reduced: # --------------------------------------------------------------------------- -def _profile( - b: Bundle, spec_id: str, title: str, xlabel: str, coord_fn, edges_key: str -) -> Reduced: +def _profile_partial(b: Bundle, coord_fn, edges_key: str) -> dict: edges = np.asarray(getattr(b.ctx, edges_key)) - r_ea, t_ea = entry_axis(b.r_all), entry_axis(b.t_all) - r_lf = attach_entry_axis(b.r_all, r_ea) - t_lf = attach_entry_axis(b.t_all, t_ea) - r_mean, r_std = weighted_profile(r_lf, coord_fn(), edges, pl.col("edep")) - t_mean, t_std = weighted_profile(t_lf, coord_fn(), edges, pl.col("edep")) + r_lf = attach_entry_axis(b.r_all, entry_axis(b.r_all)) + t_lf = attach_entry_axis(b.t_all, entry_axis(b.t_all)) + r_ids, r_mat = profile_partial(r_lf, coord_fn(), edges, pl.col("edep")) + t_ids, t_mat = profile_partial(t_lf, coord_fn(), edges, pl.col("edep")) + return { + "r_ids": r_ids.tolist(), + "r_mat": r_mat.tolist(), + "t_ids": t_ids.tolist(), + "t_mat": t_mat.tolist(), + } + + +def _assert_event_disjoint(id_lists: list[list[int]], spec_id: str, side: str) -> None: + """Guard the chunking invariant profiles depend on: no event in two chunks. + + A violation would silently double-count that event in the merged mean/RMS + with no other symptom, so this is worth a loud failure rather than a + quietly-wrong plot. + """ + seen: set[int] = set() + for ids in id_lists: + overlap = seen & set(ids) + if overlap: + raise ValueError( + f"{spec_id} ({side}): event_id(s) {sorted(overlap)[:5]} appear " + "in more than one chunk — chunking must be event-disjoint" + ) + seen.update(ids) + + +def _profile_finalize( + parts: list[dict], + ctx: Context, + spec_id: str, + title: str, + xlabel: str, + edges_key: str, +) -> Reduced: + edges = np.asarray(getattr(ctx, edges_key)) + nb = len(edges) - 1 + _assert_event_disjoint([p["r_ids"] for p in parts], spec_id, "rollout") + _assert_event_disjoint([p["t_ids"] for p in parts], spec_id, "reference") + r_mats = [np.asarray(p["r_mat"], dtype=float).reshape(-1, nb) for p in parts] + t_mats = [np.asarray(p["t_mat"], dtype=float).reshape(-1, nb) for p in parts] + r_mean, r_std = profile_finalize(r_mats) + t_mean, t_std = profile_finalize(t_mats) return Reduced( id=spec_id, family="shower", @@ -289,14 +443,21 @@ def _profile( # --------------------------------------------------------------------------- -def _species_share(b: Bundle) -> Reduced: +def _species_share_partial(b: Bundle) -> dict: r = species_share(b.r_all) t = species_share(b.t_all) - r_map = dict(zip(r["pdg"].to_list(), r["total_edep"].to_list())) - t_map = dict(zip(t["pdg"].to_list(), t["total_edep"].to_list())) + return { + "r": {str(k): v for k, v in zip(r["pdg"].to_list(), r["total_edep"].to_list())}, + "t": {str(k): v for k, v in zip(t["pdg"].to_list(), t["total_edep"].to_list())}, + } + + +def _species_share_finalize(parts: list[dict], ctx: Context) -> Reduced: + r_map = sum_merge([p["r"] for p in parts]) + t_map = sum_merge([p["t"] for p in parts]) r_tot = sum(r_map.values()) or 1.0 t_tot = sum(t_map.values()) or 1.0 - labels = [pdg_label(k) for k in b.ctx.top_pdgs] + labels = [pdg_label(k) for k in ctx.top_pdgs] return Reduced( id="species_edep_share", family="species", @@ -305,19 +466,22 @@ def _species_share(b: Bundle) -> Reduced: xlabel="species", payload={ "labels": labels, - _ROLL: [r_map.get(k, 0.0) / r_tot for k in b.ctx.top_pdgs], - _REF: [t_map.get(k, 0.0) / t_tot for k in b.ctx.top_pdgs], + _ROLL: [r_map.get(str(k), 0.0) / r_tot for k in ctx.top_pdgs], + _REF: [t_map.get(str(k), 0.0) / t_tot for k in ctx.top_pdgs], "ylabel": "fraction of total deposited energy", }, ) -def _leakage(b: Bundle) -> Reduced: +def _leakage_partial(b: Bundle) -> dict: frac = leakage_fraction(b.r_all) + return {"frac": frac.tolist()} + + +def _leakage_finalize(parts: list[dict], ctx: Context) -> Reduced: + frac = np.concatenate([np.asarray(p["frac"], dtype=float) for p in parts]) edges = np.linspace( - 0.0, - max(float(frac.max()) if len(frac) else 1.0, 1e-3), - b.ctx.n_marginal_bins + 1, + 0.0, max(float(frac.max()) if len(frac) else 1.0, 1e-3), ctx.n_marginal_bins + 1 ) counts = np.histogram(frac, edges)[0] return Reduced( @@ -347,7 +511,7 @@ def _sec_frames(b: Bundle): ) -def _sec_count_per_event(b: Bundle) -> Reduced: +def _sec_count_per_event_partial(b: Bundle) -> dict: r_sec, t_sec = _sec_frames(b) r = ( r_sec.group_by("event_id") @@ -361,9 +525,13 @@ def _sec_count_per_event(b: Bundle) -> Reduced: .collect(engine="streaming")["n"] .to_numpy() ) - edges, rc, tc = _np_hist_pair( - r.astype(float), t.astype(float), min(b.ctx.n_marginal_bins, 40) - ) + return {"r": r.tolist(), "t": t.tolist()} + + +def _sec_count_per_event_finalize(parts: list[dict], ctx: Context) -> Reduced: + r = np.concatenate([np.asarray(p["r"], dtype=float) for p in parts]) + t = np.concatenate([np.asarray(p["t"], dtype=float) for p in parts]) + edges, rc, tc = _np_hist_pair(r, t, min(ctx.n_marginal_bins, 40)) return Reduced( id="sec_count_per_event", family="secondaries", @@ -379,32 +547,21 @@ def _sec_count_per_event(b: Bundle) -> Reduced: ) -def _sec_count_per_species(b: Bundle) -> Reduced: +def _counts_by_pdg(sec_lf: pl.LazyFrame) -> dict[str, int]: + df = sec_lf.group_by("pdg").agg(pl.len().alias("n")).collect(engine="streaming") + return {str(k): v for k, v in zip(df["pdg"].to_list(), df["n"].to_list())} + + +def _sec_count_per_species_partial(b: Bundle) -> dict: r_sec, t_sec = _sec_frames(b) - r = dict( - zip( - *[ - r_sec.group_by("pdg") - .agg(pl.len().alias("n")) - .collect(engine="streaming")[c] - .to_list() - for c in ("pdg", "n") - ] - ) - ) - t = dict( - zip( - *[ - t_sec.group_by("pdg") - .agg(pl.len().alias("n")) - .collect(engine="streaming")[c] - .to_list() - for c in ("pdg", "n") - ] - ) - ) + return {"r": _counts_by_pdg(r_sec), "t": _counts_by_pdg(t_sec)} + + +def _sec_count_per_species_finalize(parts: list[dict], ctx: Context) -> Reduced: + r = sum_merge([p["r"] for p in parts]) + t = sum_merge([p["t"] for p in parts]) keys = sorted(set(r) | set(t), key=lambda k: -(r.get(k, 0) + t.get(k, 0)))[ - : len(b.ctx.top_pdgs) + : len(ctx.top_pdgs) ] return Reduced( id="sec_count_per_species", @@ -413,7 +570,7 @@ def _sec_count_per_species(b: Bundle) -> Reduced: title="Secondary count by species", xlabel="species", payload={ - "labels": [pdg_label(k) for k in keys], + "labels": [pdg_label(int(k)) for k in keys], _ROLL: [float(r.get(k, 0)) for k in keys], _REF: [float(t.get(k, 0)) for k in keys], "ylabel": "secondary count", @@ -421,12 +578,20 @@ def _sec_count_per_species(b: Bundle) -> Reduced: ) -def _sec_energy(b: Bundle) -> Reduced: +def _sec_energy_partial(b: Bundle) -> dict: r_sec, t_sec = _sec_frames(b) edges = np.linspace(*b.ctx.sec_energy_range, b.ctx.n_sec_bins + 1) - r = hist1d(r_sec, pl.col("energy"), edges) - t = hist1d(t_sec, pl.col("energy"), edges) + return { + "r": _partial_hist(r_sec, pl.col("energy"), edges), + "t": _partial_hist(t_sec, pl.col("energy"), edges), + } + + +def _sec_energy_finalize(parts: list[dict], ctx: Context) -> Reduced: + edges = np.linspace(*ctx.sec_energy_range, ctx.n_sec_bins + 1) nb = len(edges) - 1 + r = sum_merge([p["r"] for p in parts]) + t = sum_merge([p["t"] for p in parts]) return Reduced( id="sec_energy", family="secondaries", @@ -435,27 +600,34 @@ def _sec_energy(b: Bundle) -> Reduced: xlabel="secondary energy [MeV]", payload={ "edges": edges.tolist(), - _ROLL: _counts(r, 0, nb), - _REF: _counts(t, 0, nb), + _ROLL: _finalize_counts(r, 0, nb), + _REF: _finalize_counts(t, 0, nb), "log_y": True, }, ) -def _sec_cos_angle(b: Bundle) -> Reduced: +def _sec_cos_angle_partial(b: Bundle) -> dict: edges = np.linspace(-1.0, 1.0, b.ctx.n_sec_bins + 1) - nb = len(edges) - 1 cos = ( pl.col("sdx") * pl.col("axis_x") + pl.col("sdy") * pl.col("axis_y") + pl.col("sdz") * pl.col("axis_z") ).clip(-1.0, 1.0) - def _side(sec_lf: pl.LazyFrame, steps_lf: pl.LazyFrame) -> list[int]: + def _side(sec_lf: pl.LazyFrame, steps_lf: pl.LazyFrame) -> dict[str, list[int]]: ea = entry_axis(steps_lf) - return _counts(hist1d(attach_entry_axis(sec_lf, ea), cos, edges), 0, nb) + return _partial_hist(attach_entry_axis(sec_lf, ea), cos, edges) r_sec, t_sec = _sec_frames(b) + return {"r": _side(r_sec, b.r_phys), "t": _side(t_sec, b.t_all)} + + +def _sec_cos_angle_finalize(parts: list[dict], ctx: Context) -> Reduced: + edges = np.linspace(-1.0, 1.0, ctx.n_sec_bins + 1) + nb = len(edges) - 1 + r = sum_merge([p["r"] for p in parts]) + t = sum_merge([p["t"] for p in parts]) return Reduced( id="sec_cos_angle", family="secondaries", @@ -464,13 +636,30 @@ def _sec_cos_angle(b: Bundle) -> Reduced: xlabel="cos of emission angle", payload={ "edges": edges.tolist(), - _ROLL: _side(r_sec, b.r_phys), - _REF: _side(t_sec, b.t_all), + _ROLL: _finalize_counts(r, 0, nb), + _REF: _finalize_counts(t, 0, nb), "log_y": False, }, ) +# --------------------------------------------------------------------------- +# router diagnostics (not chunked — already bounded/subsampled) +# --------------------------------------------------------------------------- + +_router_gating_partial, _router_gating_finalize = _unchunkable( + lambda b: compute_router_gating(b.checkpoint, b.r_phys, b.t_phys) +) +_router_share_pdg_partial, _router_share_pdg_finalize = _unchunkable( + lambda b: compute_router_share_by_pdg( + b.checkpoint, b.r_phys, b.t_phys, b.ctx.top_pdgs + ) +) +_router_share_process_partial, _router_share_process_finalize = _unchunkable( + lambda b: compute_router_share_by_process(b.checkpoint, b.t_phys) +) + + # --------------------------------------------------------------------------- # registry assembly # --------------------------------------------------------------------------- @@ -486,7 +675,12 @@ def build_catalog() -> list[PlotSpec]: for var in MARGINAL_VARS: specs.append( PlotSpec( - f"marginal_{var}", "marginals", lambda b, v=var: _marginal_overall(b, v) + f"marginal_{var}", + "marginals", + compute_partial=lambda b, v=var: _marginal_overall_partial(b, v), + finalize=lambda parts, ctx, v=var: _marginal_overall_finalize( + parts, ctx, v + ), ) ) for axis in GROUPING_AXES: @@ -494,7 +688,12 @@ def build_catalog() -> list[PlotSpec]: PlotSpec( f"marginal_{var}_by_{axis}", "marginals", - lambda b, v=var, a=axis: _marginal_grouped(b, v, a), + compute_partial=lambda b, v=var, a=axis: _marginal_grouped_partial( + b, v, a + ), + finalize=lambda parts, ctx, v=var, a=axis: ( + _marginal_grouped_finalize(parts, ctx, v, a) + ), ) ) @@ -502,86 +701,135 @@ def build_catalog() -> list[PlotSpec]: PlotSpec( "event_total_edep", "event", - lambda b: _event_scalar( - b, + compute_partial=lambda b: _event_scalar_partial( + b, "total_edep", use_all=True + ), + finalize=lambda parts, ctx: _event_scalar_finalize( + parts, + ctx, "event_total_edep", "Total deposited energy per event", "total deposited energy [MeV]", - "total_edep", - use_all=True, ), ), - PlotSpec("event_total_edep_by_energy", "event", _event_total_edep_by_energy), + PlotSpec( + "event_total_edep_by_energy", + "event", + compute_partial=_event_total_edep_by_energy_partial, + finalize=_event_total_edep_by_energy_finalize, + ), PlotSpec( "event_mean_length", "event", - lambda b: _event_scalar( - b, + compute_partial=lambda b: _event_scalar_partial( + b, "mean_length", use_all=False + ), + finalize=lambda parts, ctx: _event_scalar_finalize( + parts, + ctx, "event_mean_length", "Mean step length per event", "mean step length [mm]", - "mean_length", - use_all=False, ), ), PlotSpec( "event_n_steps", "event", - lambda b: _event_scalar( - b, + compute_partial=lambda b: _event_scalar_partial( + b, "n_steps", use_all=False + ), + finalize=lambda parts, ctx: _event_scalar_finalize( + parts, + ctx, "event_n_steps", "Number of steps per event", "steps per event", - "n_steps", - use_all=False, ), ), PlotSpec( "shower_longitudinal", "shower", - lambda b: _profile( - b, + compute_partial=lambda b: _profile_partial(b, depth_expr, "depth_edges"), + finalize=lambda parts, ctx: _profile_finalize( + parts, + ctx, "shower_longitudinal", "Longitudinal shower profile", "depth along shower axis [mm]", - depth_expr, "depth_edges", ), ), PlotSpec( "shower_transverse", "shower", - lambda b: _profile( - b, + compute_partial=lambda b: _profile_partial( + b, transverse_expr, "transverse_edges" + ), + finalize=lambda parts, ctx: _profile_finalize( + parts, + ctx, "shower_transverse", "Transverse shower profile", "radius from shower axis [mm]", - transverse_expr, "transverse_edges", ), ), - PlotSpec("species_edep_share", "species", _species_share), - PlotSpec("leakage_fraction", "species", _leakage), - PlotSpec("sec_count_per_event", "secondaries", _sec_count_per_event), - PlotSpec("sec_count_per_species", "secondaries", _sec_count_per_species), - PlotSpec("sec_energy", "secondaries", _sec_energy), - PlotSpec("sec_cos_angle", "secondaries", _sec_cos_angle), + PlotSpec( + "species_edep_share", + "species", + compute_partial=_species_share_partial, + finalize=_species_share_finalize, + ), + PlotSpec( + "leakage_fraction", + "species", + compute_partial=_leakage_partial, + finalize=_leakage_finalize, + ), + PlotSpec( + "sec_count_per_event", + "secondaries", + compute_partial=_sec_count_per_event_partial, + finalize=_sec_count_per_event_finalize, + ), + PlotSpec( + "sec_count_per_species", + "secondaries", + compute_partial=_sec_count_per_species_partial, + finalize=_sec_count_per_species_finalize, + ), + PlotSpec( + "sec_energy", + "secondaries", + compute_partial=_sec_energy_partial, + finalize=_sec_energy_finalize, + ), + PlotSpec( + "sec_cos_angle", + "secondaries", + compute_partial=_sec_cos_angle_partial, + finalize=_sec_cos_angle_finalize, + ), PlotSpec( "router_gating", "model", - lambda b: compute_router_gating(b.checkpoint, b.r_phys, b.t_phys), + compute_partial=_router_gating_partial, + finalize=_router_gating_finalize, + chunkable=False, ), PlotSpec( "router_share_by_pdg", "model", - lambda b: compute_router_share_by_pdg( - b.checkpoint, b.r_phys, b.t_phys, b.ctx.top_pdgs - ), + compute_partial=_router_share_pdg_partial, + finalize=_router_share_pdg_finalize, + chunkable=False, ), PlotSpec( "router_share_by_process", "model", - lambda b: compute_router_share_by_process(b.checkpoint, b.t_phys), + compute_partial=_router_share_process_partial, + finalize=_router_share_process_finalize, + chunkable=False, ), ] return specs diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index b9ad621..606d93b 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -15,18 +15,25 @@ next to the rollout parquet, and lays everything out under it: /shared.json fixed bin edges / group sets (prep) /run_meta.json resolved rollout/reference paths + plot metadata - /reduced/.json one per compute job + /reduced_partial/__.json one per (plot, chunk) job + /reduced/.json merged, per plot /plots//.pdf rendered locally -Job model (one condor job per plot, compute/render split): +Job model (one condor job per (plot, chunk), compute/merge/render split): 1. ``prep`` runs once on the submit node — reads the YAML, resolves the shared - context from a subsample, writes ``shared.json`` + ``run_meta.json``. -2. one job per catalog id runs ``giant analyze compute-one --run-dir`` on a - worker — a single streaming pass writing ``reduced/.json`` (polars/numpy - only, no LaTeX). -3. a final *local* ``giant analyze render`` turns those into the styled PDF + - gallery tree (that step imports plotstyle/LaTeX). + context from a subsample, writes ``shared.json`` + ``run_meta.json`` + (including the run's configured ``n_chunks``). +2. one job per catalog id x chunk index runs ``giant analyze compute-one + --run-dir`` on a worker — a single streaming pass over that + ``event_id``-disjoint chunk, writing ``reduced_partial/__.json`` + (polars/numpy only, no LaTeX). Specs marked ``chunkable=False`` + (``PlotSpec``, ``catalog.py``) always run as a single chunk. +3. a *local* ``giant analyze render`` first merges every plot's chunk partials + (``merge_all`` — sums/concatenates them and re-derives any data-dependent + histogram edges or mean/std, per ``PlotSpec.finalize``) into + ``reduced/.json``, then renders those into the styled PDF + gallery tree + (that step imports plotstyle/LaTeX). Files on ``/ceph`` or ``/work`` are reached via ``ProvidesETPResources``; no HTCondor file transfer of the multi-GB inputs. @@ -42,6 +49,7 @@ import yaml from giant.analysis.catalog import Bundle, catalog_ids, get_spec from giant.analysis.context import Context, build_context +from giant.analysis.reduced import Partial # Keys copied verbatim from a rollout YAML into each plot's gallery metadata. _PLOT_META_KEYS = ( @@ -104,6 +112,7 @@ class RunMeta: run_dir: str title: str plot_meta: dict + n_chunks: int = 1 def save(self, path: str | Path) -> None: Path(path).write_text(json.dumps(self.__dict__, indent=2)) @@ -116,11 +125,16 @@ class RunMeta: def prep( rollout_yaml: str | Path, run_dir: str | Path | None = None, + n_chunks: int = 1, **ctx_kwargs, ) -> Path: """Read the rollout YAML, build the shared context, and lay out the run dir. Writes ``shared.json`` + ``run_meta.json`` and returns the run directory. + ``n_chunks`` is the run-level chunk count every ``compute-one``/``merge-one`` + job reads back out of ``run_meta.json`` (via ``RunMeta.n_chunks``), so it is + resolved once here rather than re-passed (and risking disagreement) at every + later step. """ y = load_rollout_yaml(rollout_yaml) run_path = derive_run_dir(y, run_dir) @@ -137,12 +151,13 @@ def prep( run_dir=str(run_path), title=f"GIANT rollout analysis — {ckpt}", plot_meta=_plot_meta(y), + n_chunks=n_chunks, ).save(run_path / "run_meta.json") return run_path # --------------------------------------------------------------------------- -# per-plot compute (what each condor job runs) +# per-(plot, chunk) compute (what each condor job runs) # --------------------------------------------------------------------------- @@ -153,18 +168,41 @@ def compute_reduced( shared: str | Path, out: str | Path, checkpoint: str | None = None, + chunk_index: int = 0, + n_chunks: int = 1, ) -> Path: - """Core: run one plot's reduction against explicit paths → ``Reduced`` JSON.""" + """Core: run one (plot, chunk)'s partial reduction against explicit paths. + + Writes a ``Partial`` JSON — the raw, not-yet-merged output of + ``PlotSpec.compute_partial`` — never a finished ``Reduced``; ``merge_one`` + is what combines every chunk's ``Partial`` for a plot into the final + ``Reduced``. Specs with ``chunkable=False`` always run as a single chunk + regardless of ``n_chunks``. + """ ctx = Context.load(shared) - bundle = Bundle.open(rollout, reference, ctx, checkpoint=checkpoint) - reduced = get_spec(spec_id).compute(bundle) + spec = get_spec(spec_id) + effective_n = n_chunks if spec.chunkable else 1 + if not (0 <= chunk_index < effective_n): + raise ValueError( + f"{spec_id}: chunk_index={chunk_index} out of range for " + f"n_chunks={effective_n} (chunkable={spec.chunkable})" + ) + bundle = Bundle.open( + rollout, reference, ctx, checkpoint=checkpoint, chunk=(chunk_index, effective_n) + ) + partial = Partial( + id=spec_id, + family=spec.family, + chunk=chunk_index, + data=spec.compute_partial(bundle), + ) out = Path(out) - reduced.save(out) + partial.save(out) return out -def compute_one(spec_id: str, run_dir: str | Path) -> Path: - """Run one plot's reduction from a prepped run directory.""" +def compute_one(spec_id: str, run_dir: str | Path, chunk_index: int = 0) -> Path: + """Run one (plot, chunk)'s partial reduction from a prepped run directory.""" run_path = Path(run_dir) meta = RunMeta.load(run_path / "run_meta.json") return compute_reduced( @@ -172,11 +210,56 @@ def compute_one(spec_id: str, run_dir: str | Path) -> Path: meta.rollout, meta.reference, run_path / "shared.json", - run_path / "reduced" / f"{spec_id}.json", + run_path / "reduced_partial" / f"{spec_id}__{chunk_index}.json", checkpoint=meta.plot_meta.get("checkpoint"), + chunk_index=chunk_index, + n_chunks=meta.n_chunks, ) +# --------------------------------------------------------------------------- +# per-plot merge (the join step ``render`` runs before rendering) +# --------------------------------------------------------------------------- + + +def merge_one(spec_id: str, run_dir: str | Path) -> Path: + """Merge every chunk's partial for one plot into the final ``Reduced`` JSON. + + Fails loudly if fewer partials exist than the run's configured chunk count + for this plot — that is what catches an incomplete/failed condor job + instead of silently rendering a plot from partial data. Idempotent: safe + to call again (e.g. from ``render_run``) once all chunks are in. + """ + run_path = Path(run_dir) + meta = RunMeta.load(run_path / "run_meta.json") + ctx = Context.load(run_path / "shared.json") + spec = get_spec(spec_id) + effective_n = meta.n_chunks if spec.chunkable else 1 + + partial_dir = run_path / "reduced_partial" + found = { + p.chunk: p + for p in (Partial.load(jf) for jf in partial_dir.glob(f"{spec_id}__*.json")) + } + missing = sorted(set(range(effective_n)) - set(found)) + if missing: + raise FileNotFoundError( + f"{spec_id}: missing chunk partial(s) {missing} of {effective_n} " + f"under {partial_dir} — did every compute-one job finish?" + ) + + parts = [found[k].data for k in range(effective_n)] + reduced = spec.finalize(parts, ctx) + out = run_path / "reduced" / f"{spec_id}.json" + reduced.save(out) + return out + + +def merge_all(run_dir: str | Path) -> list[Path]: + """Merge every catalog plot's chunk partials into ``reduced/.json``.""" + return [merge_one(spec_id, run_dir) for spec_id in catalog_ids()] + + # --------------------------------------------------------------------------- # submit description # --------------------------------------------------------------------------- @@ -192,16 +275,17 @@ class SubmitConfig: request_cpus: int = 1 request_walltime_s: int = 3600 remote: bool = False # +RemoteJob (grid I/O) vs ProvidesETPResources (local files) + n_chunks: int = 1 # per-plot data chunks; ignored for chunkable=False specs _WRAPPER = """#!/bin/bash set -euo pipefail cd {repo_dir} -exec uv run giant analyze compute-one --id "$1" --run-dir {run_dir} +exec uv run giant analyze compute-one --id "$1" --chunk "$2" --run-dir {run_dir} """ -def _submit_description(cfg: SubmitConfig, wrapper: Path, ids_file: Path) -> str: +def _submit_description(cfg: SubmitConfig, wrapper: Path, jobs_file: Path) -> str: reqs_attrs = ( "+RemoteJob = True\n" if cfg.remote @@ -211,7 +295,7 @@ def _submit_description(cfg: SubmitConfig, wrapper: Path, ids_file: Path) -> str "universe = docker\n" f"docker_image = {cfg.docker_image}\n" f"executable = {wrapper}\n" - "arguments = $(plotid)\n" + "arguments = $(plotid) $(chunk)\n" "should_transfer_files = YES\n" "when_to_transfer_output = ON_EXIT\n" f"request_memory = {cfg.request_memory_mb}\n" @@ -219,31 +303,41 @@ def _submit_description(cfg: SubmitConfig, wrapper: Path, ids_file: Path) -> str f"+RequestWalltime = {cfg.request_walltime_s}\n" f"accounting_group = {cfg.accounting_group}\n" f"{reqs_attrs}" - f"output = {cfg.run_dir}/logs/$(plotid).out\n" - f"error = {cfg.run_dir}/logs/$(plotid).err\n" + f"output = {cfg.run_dir}/logs/$(plotid)__$(chunk).out\n" + f"error = {cfg.run_dir}/logs/$(plotid)__$(chunk).err\n" f"log = {cfg.run_dir}/logs/condor.log\n" - f"queue plotid from {ids_file}\n" + f"queue plotid,chunk from {jobs_file}\n" ) def write_submit(cfg: SubmitConfig, ids: list[str] | None = None) -> Path: - """Write the wrapper script, plot-id list, and HTCondor submit description. + """Write the wrapper script, (plot, chunk) job list, and HTCondor submit + description. - Returns the submit description path (``/analyze.sub``). Does not - submit — call ``condor_submit`` on the returned file. + Each catalog id gets ``cfg.n_chunks`` jobs, except ``chunkable=False`` + specs (the router diagnostics), which always get exactly one regardless of + ``cfg.n_chunks``. Returns the submit description path + (``/analyze.sub``). Does not submit — call ``condor_submit`` on + the returned file. """ ids = ids or catalog_ids() run_dir = cfg.run_dir (run_dir / "logs").mkdir(parents=True, exist_ok=True) (run_dir / "reduced").mkdir(parents=True, exist_ok=True) + (run_dir / "reduced_partial").mkdir(parents=True, exist_ok=True) wrapper = run_dir / "run_compute.sh" wrapper.write_text(_WRAPPER.format(repo_dir=cfg.repo_dir, run_dir=run_dir)) wrapper.chmod(0o755) - ids_file = run_dir / "plotids.txt" - ids_file.write_text("\n".join(ids) + "\n") + jobs = [ + (spec_id, chunk) + for spec_id in ids + for chunk in range(cfg.n_chunks if get_spec(spec_id).chunkable else 1) + ] + jobs_file = run_dir / "jobs.txt" + jobs_file.write_text("\n".join(f"{i},{k}" for i, k in jobs) + "\n") sub = run_dir / "analyze.sub" - sub.write_text(_submit_description(cfg, wrapper, ids_file)) + sub.write_text(_submit_description(cfg, wrapper, jobs_file)) return sub diff --git a/giant/analysis/reduce.py b/giant/analysis/reduce.py index 51bf035..15089f6 100644 --- a/giant/analysis/reduce.py +++ b/giant/analysis/reduce.py @@ -16,6 +16,8 @@ efficiency" section): from __future__ import annotations +from typing import Any + import numpy as np import polars as pl @@ -58,6 +60,24 @@ def hist1d( return out +def sum_merge(dicts: list[dict[str, Any]]) -> dict[str, Any]: + """Elementwise-sum a list of sum-mergeable count/total dicts (JSON-safe keys). + + Used to merge chunked ``hist1d``/``species_share``-style partials, whose + values bin/group against edges or keys fixed by ``Context`` — a chunk's raw + count dict is exactly a partial sum, so merging is a plain elementwise sum + over the union of keys (a key absent from some chunk is all-zero there). + Values may be per-bin count lists or plain scalar totals; both round-trip + through ``np.asarray``/``.tolist()`` unchanged in shape. + """ + out: dict[str, np.ndarray] = {} + for d in dicts: + for k, v in d.items(): + arr = np.asarray(v) + out[k] = arr.copy() if k not in out else out[k] + arr + return {k: v.tolist() for k, v in out.items()} + + # --------------------------------------------------------------------------- # Per-event scalar observables (one bounded group_by pass) # --------------------------------------------------------------------------- @@ -149,18 +169,19 @@ def transverse_expr() -> pl.Expr: return (tx**2 + ty**2 + tz**2).sqrt() -def weighted_profile( +def profile_partial( lf: pl.LazyFrame, coord: pl.Expr, edges: np.ndarray, weight: pl.Expr, ) -> tuple[np.ndarray, np.ndarray]: - """Event-averaged, ``weight``-summed profile of ``coord``, with an event-RMS band. + """One chunk's per-event x bin ``weight``-sum matrix: ``(event_ids, matrix)``. - One streaming ``group_by(event_id, bin)`` sums ``weight`` per (event, bin); - collapsed in numpy to the per-bin mean over events and its event-to-event std - (the band). ``coord``/``weight`` require the entry/axis columns attached. - Returns ``(mean, std)``, each length ``len(edges)-1``. + One streaming ``group_by(event_id, bin)`` sums ``weight`` per (event, bin). + A chunk's matrix rows are only the events present in that chunk, so chunks' + matrices stack cleanly with no cross-chunk lookup — this requires chunking + to be event-disjoint (every row of an event lands in one chunk). + ``coord``/``weight`` require the entry/axis columns attached. """ lo, hi, nbins = float(edges[0]), float(edges[-1]), len(edges) - 1 grid = ( @@ -177,7 +198,35 @@ def weighted_profile( uniq, inv = np.unique(ev, return_inverse=True) mat = np.zeros((len(uniq), nbins), dtype=np.float64) np.add.at(mat, (inv, grid["_b"].to_numpy()), grid["_ws"].to_numpy()) - return mat.mean(axis=0), mat.std(axis=0) + return uniq, mat + + +def profile_finalize(mats: list[np.ndarray]) -> tuple[np.ndarray, np.ndarray]: + """Collapse per-chunk per-event x bin matrices into the final mean/std profile. + + Chunks are event-disjoint, so row-wise concatenation of their matrices + reconstructs the full per-event matrix; the mean/event-RMS collapse must + happen once over that full matrix — an average of per-chunk means/stds + would be wrong (chunks generally hold different numbers of events). + Returns ``(mean, std)``, each length ``nbins``. + """ + full = np.concatenate(mats, axis=0) + return full.mean(axis=0), full.std(axis=0) + + +def weighted_profile( + lf: pl.LazyFrame, + coord: pl.Expr, + edges: np.ndarray, + weight: pl.Expr, +) -> tuple[np.ndarray, np.ndarray]: + """Single-pass profile of ``coord`` (mean +/- event-RMS band over events). + + Convenience wrapper for the unchunked (whole-dataset) case; ``mean_std + + profile_partial`` is what a chunked compute/finalize split uses instead. + """ + _, mat = profile_partial(lf, coord, edges, weight) + return profile_finalize([mat]) # --------------------------------------------------------------------------- diff --git a/giant/analysis/reduced.py b/giant/analysis/reduced.py index 836c923..2188800 100644 --- a/giant/analysis/reduced.py +++ b/giant/analysis/reduced.py @@ -39,3 +39,27 @@ class Reduced: @classmethod def load(cls, path: str | Path) -> "Reduced": return cls(**json.loads(Path(path).read_text())) + + +@dataclass +class Partial: + """The raw, not-yet-finalized output of one ``(plot, chunk)`` compute job. + + ``data`` holds whatever shape that plot's ``PlotSpec.compute_partial`` + returns — a raw sum-mergeable count dict, or a raw per-event/per-secondary + array to be concatenated across chunks — never a finished histogram/profile. + ``PlotSpec.finalize`` is the only thing that knows how to interpret it. + """ + + id: str + family: str + chunk: int + data: dict + + def save(self, path: str | Path) -> None: + Path(path).parent.mkdir(parents=True, exist_ok=True) + Path(path).write_text(json.dumps(asdict(self))) + + @classmethod + def load(cls, path: str | Path) -> "Partial": + return cls(**json.loads(Path(path).read_text())) diff --git a/giant/analysis/render.py b/giant/analysis/render.py index 35cd3d6..76d2cd4 100644 --- a/giant/analysis/render.py +++ b/giant/analysis/render.py @@ -303,12 +303,16 @@ def render_all( def render_run(run_dir: str | Path, *, run_gallery: bool = False) -> list[Path]: """Render a prepped run directory: ``/reduced`` → ``/plots``. - Pulls the rollout provenance (checkpoint, paths, cutoffs) from - ``run_meta.json`` into every plot's gallery metadata. + First joins every plot's chunk partials (``reduced_partial/__*.json``) + into ``reduced/.json`` via ``merge_all`` — a no-op merge when the run + wasn't chunked (``n_chunks=1``) — then pulls the rollout provenance + (checkpoint, paths, cutoffs) from ``run_meta.json`` into every plot's + gallery metadata and renders. """ - from giant.analysis.condor import RunMeta + from giant.analysis.condor import RunMeta, merge_all run_dir = Path(run_dir) + merge_all(run_dir) meta = RunMeta.load(run_dir / "run_meta.json") run_meta = { "title": meta.title, diff --git a/giant/cli.py b/giant/cli.py index e371120..1f9769f 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -1150,6 +1150,12 @@ def analyze_prep( n_energy_bins: Annotated[int, typer.Option("--energy-bins")] = 4, n_marginal_bins: Annotated[int, typer.Option("--bins")] = 50, top_k_pdg: Annotated[int, typer.Option("--top-pdg")] = 6, + chunks: Annotated[ + int, + typer.Option( + "--chunks", help="Split each plot's data into this many event_id chunks" + ), + ] = 1, ) -> None: """Read the rollout YAML → shared.json + run_meta.json in the run directory.""" from giant.analysis import prep @@ -1157,6 +1163,7 @@ def analyze_prep( path = prep( rollout_yaml, run_dir, + n_chunks=chunks, n_energy_bins=n_energy_bins, n_marginal_bins=n_marginal_bins, top_k_pdg=top_k_pdg, @@ -1172,11 +1179,34 @@ def analyze_compute_one( run_dir: Annotated[ Path, typer.Option("--run-dir", help="Run directory from `analyze prep`") ], + chunk: Annotated[ + int, typer.Option("--chunk", help="Chunk index (see `analyze prep --chunks`)") + ] = 0, ) -> None: - """Run one plot's streaming reduction (this is what each condor job runs).""" + """Run one (plot, chunk)'s streaming reduction (this is what each condor job runs).""" from giant.analysis import compute_one - path = compute_one(id, run_dir) + path = compute_one(id, run_dir, chunk_index=chunk) + typer.echo(f"wrote {path}") + + +@analyze_app.command("merge-one") +def analyze_merge_one( + id: Annotated[ + str, typer.Option("--id", help="Catalog plot id (see `analyze list`)") + ], + run_dir: Annotated[ + Path, typer.Option("--run-dir", help="Run directory from `analyze prep`") + ], +) -> None: + """Merge one plot's chunk partials into its final reduced JSON. + + Runs automatically as part of `analyze render`; useful standalone to + debug a specific plot without re-rendering everything. + """ + from giant.analysis import merge_one + + path = merge_one(id, run_dir) typer.echo(f"wrote {path}") @@ -1224,16 +1254,23 @@ def analyze_submit( bool, typer.Option("--remote/--local", help="+RemoteJob vs ProvidesETPResources"), ] = False, + chunks: Annotated[ + int, + typer.Option( + "--chunks", + help="Split each plot's data into this many event_id chunks/jobs", + ), + ] = 1, dry_run: Annotated[ bool, typer.Option("--dry-run", help="Write files but don't condor_submit") ] = False, ) -> None: - """prep + write the HTCondor submit description (one job per plot), then submit.""" + """prep + write the HTCondor submit description (one job per plot x chunk), then submit.""" import subprocess from giant.analysis import SubmitConfig, prep, write_submit - path = prep(rollout_yaml, run_dir) + path = prep(rollout_yaml, run_dir, n_chunks=chunks) cfg = SubmitConfig( run_dir=path, accounting_group=accounting_group, @@ -1241,6 +1278,7 @@ def analyze_submit( docker_image=docker_image, request_memory_mb=request_memory, remote=remote, + n_chunks=chunks, ) sub = write_submit(cfg) typer.echo(f"run directory: {path}") diff --git a/tests/test_catalog.py b/tests/test_catalog.py index 90447d3..c8a47f0 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -2,21 +2,30 @@ from __future__ import annotations +import numpy as np import pytest from giant.analysis import build_catalog, catalog_ids, get_spec -from giant.analysis.catalog import Bundle -from giant.analysis.context import build_context +from giant.analysis.catalog import Bundle, PlotSpec +from giant.analysis.context import Context, build_context from tests.test_analysis_reduce import _reference_frame, _rollout_frame -@pytest.fixture(scope="module") -def bundle() -> Bundle: +def _build_ctx() -> Context: r, t = _rollout_frame(), _reference_frame() - ctx = build_context( + return build_context( r, t, n_energy_bins=2, n_marginal_bins=10, top_k_pdg=3, sample_rows=1000 ) - return Bundle.open(r, t, ctx) + + +@pytest.fixture(scope="module") +def ctx() -> Context: + return _build_ctx() + + +@pytest.fixture(scope="module") +def bundle(ctx: Context) -> Bundle: + return Bundle.open(_rollout_frame(), _reference_frame(), ctx) def test_catalog_ids_unique_and_nonempty(): @@ -36,7 +45,7 @@ def test_get_spec_roundtrip_and_unknown(): def test_every_spec_computes_valid_reduced(bundle: Bundle): for spec in build_catalog(): - r = spec.compute(bundle) + r = spec.finalize([spec.compute_partial(bundle)], bundle.ctx) assert r.id == spec.id assert r.kind in { "overlay_hist", @@ -81,3 +90,64 @@ def _validate_payload(r) -> None: for side in ("rollout", "reference"): if side in p: assert cat in p[side] + + +# --------------------------------------------------------------------------- +# chunked (compute_partial x N -> finalize) must match the unchunked (N=1) result +# --------------------------------------------------------------------------- + +# One representative id per merge shape: sum-mergeable (marginal_edep, +# sec_count_per_species via pdg-keyed sums), concat-then-finalize with +# data-dependent edges (event_total_edep), concat-then-mean/std (shower_ +# longitudinal), concat-then-max-edge (leakage_fraction), pdg-keyed sum with a +# ratio (species_edep_share), and a chunkable=False passthrough (router_gating). +_CHUNK_EQUIVALENCE_IDS = [ + "marginal_edep", + "species_edep_share", + "event_total_edep", + "shower_longitudinal", + "leakage_fraction", + "sec_count_per_species", + "router_gating", +] + + +def _assert_payload_close(a, b, path: str = "payload") -> None: + """Recursively compare two JSON-shaped payloads (float-tolerant).""" + assert type(a) is type(b), f"{path}: {type(a)} != {type(b)}" + if isinstance(a, dict): + assert set(a) == set(b), f"{path}: key mismatch {set(a)} != {set(b)}" + for k in a: + _assert_payload_close(a[k], b[k], f"{path}.{k}") + elif isinstance(a, list): + assert len(a) == len(b), f"{path}: length mismatch" + for i, (x, y) in enumerate(zip(a, b)): + _assert_payload_close(x, y, f"{path}[{i}]") + elif isinstance(a, float): + assert np.isclose(a, b, atol=1e-9), f"{path}: {a} != {b}" + else: + assert a == b, f"{path}: {a} != {b}" + + +@pytest.mark.parametrize("spec_id", _CHUNK_EQUIVALENCE_IDS) +def test_chunked_matches_unchunked(ctx: Context, spec_id: str): + """A plot computed over N event-disjoint chunks then merged must equal the + same plot computed in one unchunked pass — the core chunking correctness + guarantee (see the analysis-rollout-plots chunking plan).""" + spec: PlotSpec = get_spec(spec_id) + r, t = _rollout_frame(), _reference_frame() + + unchunked_bundle = Bundle.open(r, t, ctx) + unchunked = spec.finalize([spec.compute_partial(unchunked_bundle)], ctx) + + # 4 chunks over only 2 distinct event_ids also exercises empty chunks. + n_chunks = 4 if spec.chunkable else 1 + parts = [ + spec.compute_partial(Bundle.open(r, t, ctx, chunk=(k, n_chunks))) + for k in range(n_chunks) + ] + chunked = spec.finalize(parts, ctx) + + assert chunked.id == unchunked.id + assert chunked.kind == unchunked.kind + _assert_payload_close(unchunked.payload, chunked.payload) diff --git a/tests/test_condor.py b/tests/test_condor.py index 1d6ff3e..6634bd8 100644 --- a/tests/test_condor.py +++ b/tests/test_condor.py @@ -16,11 +16,13 @@ from giant.analysis import ( compute_reduced, derive_run_dir, load_rollout_yaml, + merge_one, prep, write_submit, ) +from giant.analysis.catalog import get_spec from giant.analysis.condor import Context -from giant.analysis.reduced import Reduced +from giant.analysis.reduced import Partial, Reduced from giant.constants import PREDICT_COORD_METADATA_KEY, ROLLOUT_COORD_VALUE from tests.test_analysis_reduce import _reference_frame, _rollout_frame @@ -51,11 +53,14 @@ def _write_inputs(tmp_path: Path) -> Path: return yaml_path -def _prep(rollout_yaml: Path, run_dir: str | Path | None = None) -> Path: +def _prep( + rollout_yaml: Path, run_dir: str | Path | None = None, chunks: int = 1 +) -> Path: """``prep`` with small test-sized context bins/sampling.""" return prep( rollout_yaml, run_dir, + n_chunks=chunks, n_energy_bins=2, n_marginal_bins=8, top_k_pdg=3, @@ -87,15 +92,16 @@ def test_prep_lays_out_run_dir(tmp_path: Path): assert meta.reference.endswith("reference.parquet") assert meta.plot_meta["checkpoint"] == "/ckpt/best.pt" assert "best.pt" in meta.title + assert meta.n_chunks == 1 def test_compute_one_from_run_dir(tmp_path: Path): run_dir = _prep(_write_inputs(tmp_path)) out = compute_one("marginal_edep", run_dir) - assert out == run_dir / "reduced" / "marginal_edep.json" - reduced = Reduced.load(out) - assert reduced.id == "marginal_edep" - assert len(reduced.payload["rollout"]) == len(reduced.payload["edges"]) - 1 + assert out == run_dir / "reduced_partial" / "marginal_edep__0.json" + partial = Partial.load(out) + assert partial.id == "marginal_edep" and partial.chunk == 0 + assert "r" in partial.data and "t" in partial.data def test_compute_reduced_explicit_paths(tmp_path: Path): @@ -108,7 +114,45 @@ def test_compute_reduced_explicit_paths(tmp_path: Path): run_dir / "shared.json", tmp_path / "r.json", ) - assert Reduced.load(out).id == "marginal_step_length" + assert Partial.load(out).id == "marginal_step_length" + + +def test_merge_one_produces_reduced(tmp_path: Path): + run_dir = _prep(_write_inputs(tmp_path)) + compute_one("marginal_edep", run_dir) + out = merge_one("marginal_edep", run_dir) + assert out == run_dir / "reduced" / "marginal_edep.json" + reduced = Reduced.load(out) + assert reduced.id == "marginal_edep" + assert len(reduced.payload["rollout"]) == len(reduced.payload["edges"]) - 1 + + +def test_merge_one_fails_loudly_on_missing_chunk(tmp_path: Path): + run_dir = _prep(_write_inputs(tmp_path), chunks=2) + compute_one("marginal_edep", run_dir, chunk_index=0) # chunk 1 never computed + with pytest.raises(FileNotFoundError, match="missing chunk"): + merge_one("marginal_edep", run_dir) + + +def test_chunked_compute_and_merge_matches_unchunked(tmp_path: Path): + (tmp_path / "a").mkdir() + (tmp_path / "b").mkdir() + unchunked_dir = _prep(_write_inputs(tmp_path / "a")) + compute_one("marginal_step_length", unchunked_dir) + unchunked = Reduced.load(merge_one("marginal_step_length", unchunked_dir)) + + chunked_dir = _prep(_write_inputs(tmp_path / "b"), chunks=2) + for k in range(2): + compute_one("marginal_step_length", chunked_dir, chunk_index=k) + chunked = Reduced.load(merge_one("marginal_step_length", chunked_dir)) + + assert chunked.payload == unchunked.payload + + +def test_compute_reduced_rejects_out_of_range_chunk(tmp_path: Path): + run_dir = _prep(_write_inputs(tmp_path)) # n_chunks=1 (default) + with pytest.raises(ValueError, match="out of range"): + compute_one("marginal_edep", run_dir, chunk_index=1) def test_write_submit_description(tmp_path: Path): @@ -119,12 +163,15 @@ def test_write_submit_description(tmp_path: Path): assert "docker_image = mschnepf/slc7-condocker" in txt assert "requirements = TARGET.ProvidesETPResources" in txt assert "accounting_group = cms" in txt - assert "queue plotid from" in txt - assert (run_dir / "plotids.txt").read_text().split() == catalog_ids() + assert "queue plotid,chunk from" in txt + jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()] + assert [i for i, _ in jobs] == catalog_ids() + assert all(k == "0" for _, k in jobs) # n_chunks=1 default wrapper = run_dir / "run_compute.sh" assert wrapper.exists() and (wrapper.stat().st_mode & 0o111) body = wrapper.read_text() - assert "giant analyze compute-one --id" in body and "--run-dir" in body + assert "giant analyze compute-one --id" in body + assert "--chunk" in body and "--run-dir" in body def test_write_submit_remote_flag(tmp_path: Path): @@ -135,3 +182,18 @@ def test_write_submit_remote_flag(tmp_path: Path): txt = write_submit(cfg).read_text() assert "+RemoteJob = True" in txt assert "ProvidesETPResources" not in txt + + +def test_write_submit_chunks_respect_chunkable(tmp_path: Path): + assert get_spec("router_gating").chunkable is False + run_dir = _prep(_write_inputs(tmp_path)) + cfg = SubmitConfig( + run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4 + ) + write_submit(cfg) + jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()] + counts: dict[str, int] = {} + for spec_id, _ in jobs: + counts[spec_id] = counts.get(spec_id, 0) + 1 + assert counts["marginal_edep"] == 4 + assert counts["router_gating"] == 1 # chunkable=False, ignores n_chunks From 85d3914a4d794270268490f9bb811c7015d2761d Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 09:30:54 +0200 Subject: [PATCH 07/24] analyze: expose bin/pdg options on `analyze submit` `submit` calls `prep` internally but only forwarded --chunks, so a condor run could never use non-default energy-bins/bins/top-pdg. --- giant/cli.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/giant/cli.py b/giant/cli.py index 1f9769f..48b738a 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -1261,6 +1261,9 @@ def analyze_submit( help="Split each plot's data into this many event_id chunks/jobs", ), ] = 1, + n_energy_bins: Annotated[int, typer.Option("--energy-bins")] = 4, + n_marginal_bins: Annotated[int, typer.Option("--bins")] = 50, + top_k_pdg: Annotated[int, typer.Option("--top-pdg")] = 6, dry_run: Annotated[ bool, typer.Option("--dry-run", help="Write files but don't condor_submit") ] = False, @@ -1270,7 +1273,14 @@ def analyze_submit( from giant.analysis import SubmitConfig, prep, write_submit - path = prep(rollout_yaml, run_dir, n_chunks=chunks) + path = prep( + rollout_yaml, + run_dir, + n_chunks=chunks, + n_energy_bins=n_energy_bins, + n_marginal_bins=n_marginal_bins, + top_k_pdg=top_k_pdg, + ) cfg = SubmitConfig( run_dir=path, accounting_group=accounting_group, From e380400fe9acd6784c4c73b96ac4f3d1375ca39a Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 09:47:44 +0200 Subject: [PATCH 08/24] analyze: estimate per-job HTCondor walltime from chunk row count MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Each condor job's +RequestWalltime used to be one flat 3600s default for every (plot, chunk), regardless of how much data it actually streams over. `prep` now records each chunk's rollout+reference row count, and `giant/analysis/runtime_estimate.py` turns that into a per-job estimate: a per-spec (intercept, seconds/row) cost model fit by `scripts/profile_analysis_costs.py` against synthetic mock data on this machine, plus a fixed overhead placeholder (docker/uv/shared-fs startup — unmeasurable here, no /ceph access) and a single RUNTIME_SAFETY_MARGIN multiplier. jobs.txt gains a walltime column and the submit description references it via $(walltime) instead of a constant. --- giant/analysis/__init__.py | 3 + giant/analysis/condor.py | 77 ++++++++-- giant/analysis/runtime_estimate.py | 102 +++++++++++++ scripts/profile_analysis_costs.py | 231 +++++++++++++++++++++++++++++ tests/test_condor.py | 53 ++++++- 5 files changed, 448 insertions(+), 18 deletions(-) create mode 100644 giant/analysis/runtime_estimate.py create mode 100644 scripts/profile_analysis_costs.py diff --git a/giant/analysis/__init__.py b/giant/analysis/__init__.py index 8af48cb..48c4343 100644 --- a/giant/analysis/__init__.py +++ b/giant/analysis/__init__.py @@ -25,6 +25,7 @@ from giant.analysis.condor import ( ) from giant.analysis.context import Context, build_context from giant.analysis.reduced import Partial, Reduced +from giant.analysis.runtime_estimate import RUNTIME_SAFETY_MARGIN, estimate_runtime_s from giant.analysis.sources import Side __all__ = [ @@ -46,4 +47,6 @@ __all__ = [ "Partial", "Reduced", "Side", + "RUNTIME_SAFETY_MARGIN", + "estimate_runtime_s", ] diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 606d93b..d349117 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -42,14 +42,17 @@ HTCondor file transfer of the multi-GB inputs. from __future__ import annotations import json -from dataclasses import dataclass +from dataclasses import dataclass, field from pathlib import Path +import polars as pl import yaml from giant.analysis.catalog import Bundle, catalog_ids, get_spec from giant.analysis.context import Context, build_context from giant.analysis.reduced import Partial +from giant.analysis.runtime_estimate import estimate_runtime_s +from giant.analysis.sources import Side, open_side # Keys copied verbatim from a rollout YAML into each plot's gallery metadata. _PLOT_META_KEYS = ( @@ -113,6 +116,11 @@ class RunMeta: title: str plot_meta: dict n_chunks: int = 1 + # rollout+reference row count of each event_id-disjoint chunk, and the + # dataset total — inputs to `runtime_estimate.estimate_runtime_s`. Empty/0 + # on run directories written before this field existed. + rows_per_chunk: list[int] = field(default_factory=list) + total_rows: int = 0 def save(self, path: str | Path) -> None: Path(path).write_text(json.dumps(self.__dict__, indent=2)) @@ -122,6 +130,30 @@ class RunMeta: return cls(**json.loads(Path(path).read_text())) +def _rows_per_chunk(rollout: str | Path, reference: str | Path, n_chunks: int) -> list[int]: + """Rollout+reference row count of each ``event_id % n_chunks`` chunk. + + One cheap streaming ``group_by`` per side (just the ``event_id`` column) — + the sizing input every job's estimated walltime + (``runtime_estimate.estimate_runtime_s``) is computed from. + """ + + def counts(lf: pl.LazyFrame) -> pl.DataFrame: + return ( + lf.select((pl.col("event_id") % n_chunks).alias("_c")) + .group_by("_c") + .agg(pl.len().alias("n")) + .collect(engine="streaming") + ) + + out = [0] * n_chunks + for lf in (open_side(rollout, Side.rollout), open_side(reference, Side.reference)): + df = counts(lf) + for c, n in zip(df["_c"].to_list(), df["n"].to_list()): + out[c] += n + return out + + def prep( rollout_yaml: str | Path, run_dir: str | Path | None = None, @@ -144,6 +176,8 @@ def prep( ctx = build_context(rollout, reference, **ctx_kwargs) ctx.save(run_path / "shared.json") + rows_per_chunk = _rows_per_chunk(rollout, reference, n_chunks) + ckpt = Path(y.get("checkpoint", "")).name or "rollout" RunMeta( rollout=str(rollout), @@ -152,6 +186,8 @@ def prep( title=f"GIANT rollout analysis — {ckpt}", plot_meta=_plot_meta(y), n_chunks=n_chunks, + rows_per_chunk=rows_per_chunk, + total_rows=sum(rows_per_chunk), ).save(run_path / "run_meta.json") return run_path @@ -273,7 +309,6 @@ class SubmitConfig: docker_image: str = "mschnepf/slc7-condocker" request_memory_mb: int = 4096 request_cpus: int = 1 - request_walltime_s: int = 3600 remote: bool = False # +RemoteJob (grid I/O) vs ProvidesETPResources (local files) n_chunks: int = 1 # per-plot data chunks; ignored for chunkable=False specs @@ -300,25 +335,45 @@ def _submit_description(cfg: SubmitConfig, wrapper: Path, jobs_file: Path) -> st "when_to_transfer_output = ON_EXIT\n" f"request_memory = {cfg.request_memory_mb}\n" f"request_cpus = {cfg.request_cpus}\n" - f"+RequestWalltime = {cfg.request_walltime_s}\n" + "+RequestWalltime = $(walltime)\n" f"accounting_group = {cfg.accounting_group}\n" f"{reqs_attrs}" f"output = {cfg.run_dir}/logs/$(plotid)__$(chunk).out\n" f"error = {cfg.run_dir}/logs/$(plotid)__$(chunk).err\n" f"log = {cfg.run_dir}/logs/condor.log\n" - f"queue plotid,chunk from {jobs_file}\n" + f"queue plotid,chunk,walltime from {jobs_file}\n" ) +def _job_walltimes(run_dir: Path, ids: list[str], n_chunks: int) -> list[tuple[str, int, int]]: + """``(spec_id, chunk, walltime_s)`` for every job, sized from ``run_meta.json``. + + Row counts come from ``prep``'s ``RunMeta.rows_per_chunk``/``total_rows``; + ``chunkable=False`` specs (router diagnostics) always use the dataset + total since they run as a single job regardless of ``n_chunks``. + """ + meta = RunMeta.load(run_dir / "run_meta.json") + jobs: list[tuple[str, int, int]] = [] + for spec_id in ids: + chunkable = get_spec(spec_id).chunkable + chunks = range(n_chunks) if chunkable else [0] + for chunk in chunks: + n_rows = meta.rows_per_chunk[chunk] if chunkable else meta.total_rows + jobs.append((spec_id, chunk, estimate_runtime_s(spec_id, n_rows))) + return jobs + + def write_submit(cfg: SubmitConfig, ids: list[str] | None = None) -> Path: """Write the wrapper script, (plot, chunk) job list, and HTCondor submit description. Each catalog id gets ``cfg.n_chunks`` jobs, except ``chunkable=False`` specs (the router diagnostics), which always get exactly one regardless of - ``cfg.n_chunks``. Returns the submit description path - (``/analyze.sub``). Does not submit — call ``condor_submit`` on - the returned file. + ``cfg.n_chunks``. Every job's ``+RequestWalltime`` is estimated from its + chunk's row count (``runtime_estimate.estimate_runtime_s``, requires + ``run_meta.json`` from ``prep`` to already carry ``rows_per_chunk``). + Returns the submit description path (``/analyze.sub``). Does not + submit — call ``condor_submit`` on the returned file. """ ids = ids or catalog_ids() run_dir = cfg.run_dir @@ -330,13 +385,9 @@ def write_submit(cfg: SubmitConfig, ids: list[str] | None = None) -> Path: wrapper.write_text(_WRAPPER.format(repo_dir=cfg.repo_dir, run_dir=run_dir)) wrapper.chmod(0o755) - jobs = [ - (spec_id, chunk) - for spec_id in ids - for chunk in range(cfg.n_chunks if get_spec(spec_id).chunkable else 1) - ] + jobs = _job_walltimes(run_dir, ids, cfg.n_chunks) jobs_file = run_dir / "jobs.txt" - jobs_file.write_text("\n".join(f"{i},{k}" for i, k in jobs) + "\n") + jobs_file.write_text("\n".join(f"{i},{k},{w}" for i, k, w in jobs) + "\n") sub = run_dir / "analyze.sub" sub.write_text(_submit_description(cfg, wrapper, jobs_file)) diff --git a/giant/analysis/runtime_estimate.py b/giant/analysis/runtime_estimate.py new file mode 100644 index 0000000..29a7f95 --- /dev/null +++ b/giant/analysis/runtime_estimate.py @@ -0,0 +1,102 @@ +"""Per-(plot, chunk) HTCondor walltime estimates for `giant analyze submit`. + +Each catalog spec's compute cost is close to linear in the number of input +rows a `compute-one` job streams over — every spec is one (or a couple of) +streaming `group_by` pass(es) over the chunk (see `catalog.py`/`reduce.py`). +`_COST_MODEL` below is ``spec_id -> (intercept_s, seconds_per_row)``, fit by +least squares against wall-clock timings of `compute_reduced` on synthetic +mock data of increasing size, run on a local dev machine (see +`scripts/profile_analysis_costs.py` — rerun it and paste the new numbers in +here if the catalog changes or this needs recalibrating). ``n_rows`` is the +combined rollout+reference row count of the job's input: the chunk's row +count for `chunkable=True` specs, the whole dataset's for the three +`chunkable=False` router specs (they always run as a single job regardless of +chunk count). + +The fitted numbers only capture *local, in-memory compute* — they don't (and +from a laptop with no `/ceph` access, can't) capture the real condor job's +docker pull, `uv run` cold start, or shared-filesystem read latency, which in +practice likely dominate total wall time for anything but a huge single-chunk +job. `_FIXED_OVERHEAD_S` is a deliberately generous placeholder for all of +that combined; recalibrate it from real `condor_q`/log timings once some are +available, rather than trusting it as measured. +""" + +from __future__ import annotations + +import math + +# Multiplicative pad applied to every job's estimated walltime. The one knob +# this feature was asked to expose. +RUNTIME_SAFETY_MARGIN = 1.00 + +# Docker image pull + `uv run` startup + shared (/ceph, ETP) filesystem read +# latency — not measurable on a machine with no /ceph access, so this is a +# conservative placeholder rather than a fit. Recalibrate from real job logs. +_FIXED_OVERHEAD_S = 600.0 + +# Router diagnostics need a live torch checkpoint to do any real work; this +# machine has none, so their cost (torch.load + a bounded inference pass over +# <= 200k subsampled rows, independent of chunk size) couldn't be profiled +# either. Fixed budget, on top of _FIXED_OVERHEAD_S, instead of a row-based fit. +_ROUTER_FIXED_S = 300.0 +_ROUTER_IDS = frozenset({"router_gating", "router_share_by_pdg", "router_share_by_process"}) + +# Conservative fallback for any catalog id not in _COST_MODEL (e.g. a plot +# added after the last profiling run) — the most expensive fitted (intercept, +# seconds/row) pair observed, rounded up. +_DEFAULT_COST = (0.02, 2.0e-6) + +# spec_id -> (intercept_s, seconds_per_row), fit on this machine 2026-07-27 +# via `scripts/profile_analysis_costs.py` against SIDE_ROW_COUNTS up to 2M +# rows/side (4M combined). +_COST_MODEL: dict[str, tuple[float, float]] = { + "marginal_step_length": (0.003581, 0.000000042), + "marginal_step_length_by_energy": (0.010336, 0.000000081), + "marginal_step_length_by_pdg": (0.007231, 0.000000042), + "marginal_step_length_by_material": (0.004662, 0.000000048), + "marginal_edep": (0.003996, 0.000000042), + "marginal_edep_by_energy": (0.007977, 0.000000085), + "marginal_edep_by_pdg": (0.003535, 0.000000042), + "marginal_edep_by_material": (0.003893, 0.000000047), + "marginal_delta_e": (0.001380, 0.000000053), + "marginal_delta_e_by_energy": (0.006543, 0.000000093), + "marginal_delta_e_by_pdg": (0.001761, 0.000000054), + "marginal_delta_e_by_material": (0.000000, 0.000000168), + "marginal_post_E": (0.000000, 0.000000146), + "marginal_post_E_by_energy": (0.000000, 0.000000509), + "marginal_post_E_by_pdg": (0.000000, 0.000000149), + "marginal_post_E_by_material": (0.000000, 0.000000168), + "marginal_cos_scatter": (0.000000, 0.000000249), + "marginal_cos_scatter_by_energy": (0.000000, 0.000000553), + "marginal_cos_scatter_by_pdg": (0.000000, 0.000000270), + "marginal_cos_scatter_by_material": (0.000000, 0.000000260), + "event_total_edep": (0.000000, 0.000000512), + "event_total_edep_by_energy": (0.000000, 0.000000241), + "event_mean_length": (0.000000, 0.000000090), + "event_n_steps": (0.002021, 0.000000066), + "shower_longitudinal": (0.000000, 0.000001804), + "shower_transverse": (0.000000, 0.000001731), + "species_edep_share": (0.007505, 0.000000017), + "leakage_fraction": (0.003798, 0.000000026), + "sec_count_per_event": (0.006551, 0.000000054), + "sec_count_per_species": (0.006708, 0.000000042), + "sec_energy": (0.006856, 0.000000047), + "sec_cos_angle": (0.001780, 0.000000234), +} + + +def estimate_runtime_s(spec_id: str, n_rows: int) -> int: + """Estimated `+RequestWalltime` (seconds) for one (plot, chunk) job. + + ``n_rows`` is the rollout+reference row count of that job's input slice. + Includes `_FIXED_OVERHEAD_S`/`_ROUTER_FIXED_S` and `RUNTIME_SAFETY_MARGIN` + — callers should pass this straight through to the submit description. + """ + if spec_id in _ROUTER_IDS: + compute_s = _ROUTER_FIXED_S + else: + intercept, per_row = _COST_MODEL.get(spec_id, _DEFAULT_COST) + compute_s = intercept + per_row * n_rows + total = _FIXED_OVERHEAD_S + compute_s + return math.ceil(total * (1 + RUNTIME_SAFETY_MARGIN)) diff --git a/scripts/profile_analysis_costs.py b/scripts/profile_analysis_costs.py new file mode 100644 index 0000000..bbb0a98 --- /dev/null +++ b/scripts/profile_analysis_costs.py @@ -0,0 +1,231 @@ +"""Benchmark `giant analyze compute-one`'s per-job cost against synthetic data. + +Generates mock rollout+reference parquet files at a few row counts, times +`compute_reduced` for every chunkable catalog spec at each size (a single +chunk covering the whole mock file), fits a straight line (intercept, seconds +per row) through the timings, and prints the result as a Python dict literal +ready to paste into `giant/analysis/runtime_estimate.py::_COST_MODEL`. + +The three `chunkable=False` router specs (`router_gating`, +`router_share_by_pdg`, `router_share_by_process`) need a live MoE checkpoint +to do any real work; without one (this machine has no `/ceph` access, so no +real checkpoint) they short-circuit almost instantly and are excluded here — +see `runtime_estimate.py`'s `_ROUTER_FIXED_S` for how those are handled +instead. + +Usage: ``uv run python scripts/profile_analysis_costs.py`` +""" + +from __future__ import annotations + +import time +from pathlib import Path +from tempfile import TemporaryDirectory + +import numpy as np +import polars as pl + +from giant.analysis.catalog import catalog_ids, get_spec +from giant.analysis.condor import compute_reduced +from giant.analysis.context import build_context + +# Row counts (per side) to benchmark at. Kept in local memory/CPU range so the +# whole sweep finishes in about a minute; the fit is linear so it extrapolates +# fine to real multi-GB rollouts. +SIDE_ROW_COUNTS = [20_000, 100_000, 500_000, 2_000_000] + +_MATERIALS = ["G4_PbWO4", "G4_Pb", "G4_lAr", "G4_Si"] +_PDGS = [11, -11, 22, 2112, 2212, 211, -211, 13] +_ROUTER_IDS = {"router_gating", "router_share_by_pdg", "router_share_by_process"} + + +def _unit_vectors(n: int, rng: np.random.Generator) -> np.ndarray: + v = rng.normal(size=(n, 3)) + return v / np.linalg.norm(v, axis=1, keepdims=True) + + +def _ragged_lists(k: np.ndarray, rng: np.random.Generator, lo: float, hi: float): + total = int(k.sum()) + flat = rng.uniform(lo, hi, size=total) + idx = np.cumsum(k)[:-1] + return [arr.tolist() for arr in np.split(flat, idx)] + + +def _make_rollout(n: int, n_events: int, seed: int) -> pl.DataFrame: + rng = np.random.default_rng(seed) + event_id = rng.integers(0, n_events, size=n) + is_secondary = rng.random(n) < 0.15 # generation>0, step_no==0 birth rows + is_synthetic = rng.random(n) < 0.05 # bookkeeping termination rows + + pre_E = rng.lognormal(mean=3.0, sigma=1.5, size=n) + edep = rng.uniform(0, 1, size=n) * pre_E * 0.3 + post_E = np.clip(pre_E - edep, 0.0, None) + pre_dir = _unit_vectors(n, rng) + post_dir = _unit_vectors(n, rng) + pos = rng.uniform(-50, 300, size=(n, 3)) + step_length = rng.uniform(0.1, 10.0, size=n) + post_pos = pos + pre_dir * step_length[:, None] + + reasons = np.where( + is_synthetic, + rng.choice( + ["escaped", "energy_cutoff", "max_steps", "unknown_pdg"], size=n + ), + "natural_end", + ) + + return pl.DataFrame( + { + "event_id": event_id, + "track_id": rng.integers(0, 5, size=n), + "parent_id": np.where(is_secondary, 0, -1), + "generation": is_secondary.astype(np.int64), + "step_no": np.where(is_secondary, 0, rng.integers(0, 20, size=n)), + "pdg": rng.choice(_PDGS, size=n), + "pre_x": pos[:, 0], + "pre_y": pos[:, 1], + "pre_z": pos[:, 2], + "pre_E": pre_E, + "pre_dx": pre_dir[:, 0], + "pre_dy": pre_dir[:, 1], + "pre_dz": pre_dir[:, 2], + "post_x": post_pos[:, 0], + "post_y": post_pos[:, 1], + "post_z": post_pos[:, 2], + "post_E": post_E, + "post_dx": post_dir[:, 0], + "post_dy": post_dir[:, 1], + "post_dz": post_dir[:, 2], + "edep": np.where(is_synthetic, np.where(reasons == "escaped", 0.0, pre_E), edep), + "step_length": np.where(is_synthetic, 0.0, step_length), + "material": rng.choice(_MATERIALS, size=n), + "layer_id": rng.integers(0, 30, size=n), + "n_sec_pred": rng.integers(0, 4, size=n), + "termination_reason": reasons, + } + ) + + +def _make_reference(n: int, n_events: int, seed: int) -> pl.DataFrame: + rng = np.random.default_rng(seed + 1) + event_id = rng.integers(0, n_events, size=n) + pre_E = rng.lognormal(mean=3.0, sigma=1.5, size=n) + edep = rng.uniform(0, 1, size=n) * pre_E * 0.3 + post_E = np.clip(pre_E - edep, 0.0, None) + pre_dir = _unit_vectors(n, rng) + post_dir = _unit_vectors(n, rng) + pos = rng.uniform(-50, 300, size=(n, 3)) + step_length = rng.uniform(0.1, 10.0, size=n) + post_pos = pos + pre_dir * step_length[:, None] + + k = rng.poisson(0.3, size=n).clip(max=5).astype(np.int64) + sec_pdg = _ragged_lists(k, rng, 0, 1) # placeholder, overwritten below + sec_E = _ragged_lists(k, rng, 0.1, 50.0) + sec_dx = _ragged_lists(k, rng, -1.0, 1.0) + sec_dy = _ragged_lists(k, rng, -1.0, 1.0) + sec_dz = _ragged_lists(k, rng, -1.0, 1.0) + total = int(k.sum()) + flat_pdg = rng.choice(_PDGS, size=total).tolist() + idx = np.cumsum(k)[:-1] + sec_pdg = [list(x) for x in np.split(np.array(flat_pdg), idx)] + + return pl.DataFrame( + { + "event_id": event_id, + "track_id": rng.integers(0, 5, size=n), + "step_no": rng.integers(0, 20, size=n), + "pdg": rng.choice(_PDGS, size=n), + "pre_x": pos[:, 0], + "pre_y": pos[:, 1], + "pre_z": pos[:, 2], + "pre_E": pre_E, + "pre_dx": pre_dir[:, 0], + "pre_dy": pre_dir[:, 1], + "pre_dz": pre_dir[:, 2], + "post_x": post_pos[:, 0], + "post_y": post_pos[:, 1], + "post_z": post_pos[:, 2], + "post_E": post_E, + "post_dx": post_dir[:, 0], + "post_dy": post_dir[:, 1], + "post_dz": post_dir[:, 2], + "edep": edep, + "step_length": step_length, + "material": rng.choice(_MATERIALS, size=n), + "layer_id": rng.integers(0, 30, size=n), + "process": rng.choice(["compt", "phot", "eBrem", "eIoni", "conv"], size=n), + "sec_E_list": sec_E, + "sec_pdg_list": sec_pdg, + "sec_dx_list": sec_dx, + "sec_dy_list": sec_dy, + "sec_dz_list": sec_dz, + } + ) + + +def _time(spec_id: str, rollout: Path, reference: Path, shared: Path, out: Path) -> float: + t0 = time.perf_counter() + compute_reduced( + spec_id, rollout, reference, shared, out, checkpoint=None, + chunk_index=0, n_chunks=1, + ) + return time.perf_counter() - t0 + + +def main() -> None: + ids = [i for i in catalog_ids() if get_spec(i).chunkable] + timings: dict[str, list[tuple[int, float]]] = {i: [] for i in ids} + + with TemporaryDirectory(prefix="giant-profile-") as tmp: + tmp_path = Path(tmp) + for n_side in SIDE_ROW_COUNTS: + n_events = max(n_side // 20, 10) + rollout = tmp_path / f"rollout_{n_side}.parquet" + reference = tmp_path / f"reference_{n_side}.parquet" + _make_rollout(n_side, n_events, seed=0).write_parquet(rollout) + _make_reference(n_side, n_events, seed=0).write_parquet(reference) + + shared = tmp_path / f"shared_{n_side}.json" + ctx = build_context( + rollout, reference, + n_energy_bins=4, n_marginal_bins=50, top_k_pdg=6, + sample_rows=min(n_side, 200_000), + ) + ctx.save(shared) + + # warm the OS page cache so the timed pass measures compute, not + # the one-time cold read of a freshly-written file. + pl.scan_parquet(rollout).select(pl.len()).collect() + pl.scan_parquet(reference).select(pl.len()).collect() + + n_rows = 2 * n_side # rollout + reference rows in this "chunk" + for spec_id in ids: + out = tmp_path / f"{spec_id}_{n_side}.json" + dt = _time(spec_id, rollout, reference, shared, out) + timings[spec_id].append((n_rows, dt)) + print(f"{spec_id:35s} n_rows={n_rows:>9d} time={dt:7.3f}s") + + rollout.unlink() + reference.unlink() + shared.unlink() + + print("\n# spec_id -> (intercept_s, seconds_per_row), fit by least squares") + print("_COST_MODEL: dict[str, tuple[float, float]] = {") + for spec_id in ids: + xs = np.array([n for n, _ in timings[spec_id]], dtype=float) + ys = np.array([t for _, t in timings[spec_id]], dtype=float) + slope, intercept = np.polyfit(xs, ys, 1) + intercept = max(intercept, 0.0) + slope = max(slope, 0.0) + print(f' "{spec_id}": ({intercept:.6f}, {slope:.9f}),') + print("}") + + if _ROUTER_IDS: + print( + "\n# router_* specs excluded: need a live MoE checkpoint to do real\n" + "# work, none available on this machine — see _ROUTER_FIXED_S instead." + ) + + +if __name__ == "__main__": + main() diff --git a/tests/test_condor.py b/tests/test_condor.py index 6634bd8..1dc5ad2 100644 --- a/tests/test_condor.py +++ b/tests/test_condor.py @@ -93,6 +93,15 @@ def test_prep_lays_out_run_dir(tmp_path: Path): assert meta.plot_meta["checkpoint"] == "/ckpt/best.pt" assert "best.pt" in meta.title assert meta.n_chunks == 1 + assert meta.rows_per_chunk == [meta.total_rows] # single chunk holds everything + assert meta.total_rows == 8 # 5 rollout rows + 3 reference rows + + +def test_prep_splits_rows_per_chunk(tmp_path: Path): + run_dir = _prep(_write_inputs(tmp_path), chunks=2) + meta = RunMeta.load(run_dir / "run_meta.json") + assert len(meta.rows_per_chunk) == 2 + assert sum(meta.rows_per_chunk) == meta.total_rows == 8 def test_compute_one_from_run_dir(tmp_path: Path): @@ -163,10 +172,12 @@ def test_write_submit_description(tmp_path: Path): assert "docker_image = mschnepf/slc7-condocker" in txt assert "requirements = TARGET.ProvidesETPResources" in txt assert "accounting_group = cms" in txt - assert "queue plotid,chunk from" in txt + assert "+RequestWalltime = $(walltime)" in txt + assert "queue plotid,chunk,walltime from" in txt jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()] - assert [i for i, _ in jobs] == catalog_ids() - assert all(k == "0" for _, k in jobs) # n_chunks=1 default + assert [i for i, _, _ in jobs] == catalog_ids() + assert all(k == "0" for _, k, _ in jobs) # n_chunks=1 default + assert all(int(w) > 0 for _, _, w in jobs) wrapper = run_dir / "run_compute.sh" assert wrapper.exists() and (wrapper.stat().st_mode & 0o111) body = wrapper.read_text() @@ -186,14 +197,46 @@ def test_write_submit_remote_flag(tmp_path: Path): def test_write_submit_chunks_respect_chunkable(tmp_path: Path): assert get_spec("router_gating").chunkable is False - run_dir = _prep(_write_inputs(tmp_path)) + run_dir = _prep(_write_inputs(tmp_path), chunks=4) cfg = SubmitConfig( run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4 ) write_submit(cfg) jobs = [line.split(",") for line in (run_dir / "jobs.txt").read_text().split()] counts: dict[str, int] = {} - for spec_id, _ in jobs: + for spec_id, _, _ in jobs: counts[spec_id] = counts.get(spec_id, 0) + 1 assert counts["marginal_edep"] == 4 assert counts["router_gating"] == 1 # chunkable=False, ignores n_chunks + + +def test_estimate_runtime_s_scales_with_rows_and_margin(): + from giant.analysis import RUNTIME_SAFETY_MARGIN, estimate_runtime_s + from giant.analysis.runtime_estimate import _FIXED_OVERHEAD_S + + assert RUNTIME_SAFETY_MARGIN > 0 + small = estimate_runtime_s("marginal_edep", 1_000) + large = estimate_runtime_s("marginal_edep", 100_000_000) + assert small >= (1 + RUNTIME_SAFETY_MARGIN) * _FIXED_OVERHEAD_S + assert large > small # bigger chunk -> longer estimate + + +def test_write_submit_walltime_grows_with_chunk_rows(tmp_path: Path): + """A chunked run's later job walltimes track that chunk's row count.""" + from giant.analysis.runtime_estimate import estimate_runtime_s + + run_dir = _prep(_write_inputs(tmp_path), chunks=2) + meta = RunMeta.load(run_dir / "run_meta.json") + cfg = SubmitConfig( + run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=2 + ) + write_submit(cfg) + jobs = { + (i, int(k)): int(w) + for i, k, w in ( + line.split(",") for line in (run_dir / "jobs.txt").read_text().split() + ) + } + for chunk in range(2): + expected = estimate_runtime_s("marginal_edep", meta.rows_per_chunk[chunk]) + assert jobs[("marginal_edep", chunk)] == expected From fa594433395dc05ae8026fb6f94faa289deba7e2 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 10:03:02 +0200 Subject: [PATCH 09/24] analyze: run condor compute jobs via .venv/bin/giant, not uv run uv isn't installed on the HTCondor worker docker image, so `uv run` fails there. giant is already an installed console script in the repo's uv-synced .venv, so exec it directly instead. write_submit now fails fast with a clear message if .venv/bin/giant is missing. --- giant/analysis/condor.py | 10 +++++++++- tests/test_condor.py | 19 +++++++++++++++++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index d349117..91c0023 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -316,7 +316,7 @@ class SubmitConfig: _WRAPPER = """#!/bin/bash set -euo pipefail cd {repo_dir} -exec uv run giant analyze compute-one --id "$1" --chunk "$2" --run-dir {run_dir} +exec {repo_dir}/.venv/bin/giant analyze compute-one --id "$1" --chunk "$2" --run-dir {run_dir} """ @@ -375,6 +375,14 @@ def write_submit(cfg: SubmitConfig, ids: list[str] | None = None) -> Path: Returns the submit description path (``/analyze.sub``). Does not submit — call ``condor_submit`` on the returned file. """ + venv_giant = cfg.repo_dir / ".venv" / "bin" / "giant" + if not venv_giant.exists(): + raise FileNotFoundError( + f"{venv_giant} not found — condor jobs run it directly (no `uv` on " + f"the worker image), so run `uv sync --extra cpu` in {cfg.repo_dir} " + "before submitting." + ) + ids = ids or catalog_ids() run_dir = cfg.run_dir (run_dir / "logs").mkdir(parents=True, exist_ok=True) diff --git a/tests/test_condor.py b/tests/test_condor.py index 1dc5ad2..89838a8 100644 --- a/tests/test_condor.py +++ b/tests/test_condor.py @@ -53,6 +53,14 @@ def _write_inputs(tmp_path: Path) -> Path: return yaml_path +def _fake_venv(repo_dir: Path) -> None: + """Stand in for a `uv sync`'d venv: write_submit checks `.venv/bin/giant` exists.""" + giant = repo_dir / ".venv" / "bin" / "giant" + giant.parent.mkdir(parents=True, exist_ok=True) + giant.write_text("#!/bin/bash\n") + giant.chmod(0o755) + + def _prep( rollout_yaml: Path, run_dir: str | Path | None = None, chunks: int = 1 ) -> Path: @@ -166,6 +174,7 @@ def test_compute_reduced_rejects_out_of_range_chunk(tmp_path: Path): def test_write_submit_description(tmp_path: Path): run_dir = _prep(_write_inputs(tmp_path)) + _fake_venv(tmp_path) cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path) txt = write_submit(cfg).read_text() assert "universe = docker" in txt @@ -185,8 +194,16 @@ def test_write_submit_description(tmp_path: Path): assert "--chunk" in body and "--run-dir" in body +def test_write_submit_requires_synced_venv(tmp_path: Path): + run_dir = _prep(_write_inputs(tmp_path)) + cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path) + with pytest.raises(FileNotFoundError, match="uv sync"): + write_submit(cfg) + + def test_write_submit_remote_flag(tmp_path: Path): run_dir = _prep(_write_inputs(tmp_path)) + _fake_venv(tmp_path) cfg = SubmitConfig( run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, remote=True ) @@ -198,6 +215,7 @@ def test_write_submit_remote_flag(tmp_path: Path): def test_write_submit_chunks_respect_chunkable(tmp_path: Path): assert get_spec("router_gating").chunkable is False run_dir = _prep(_write_inputs(tmp_path), chunks=4) + _fake_venv(tmp_path) cfg = SubmitConfig( run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=4 ) @@ -227,6 +245,7 @@ def test_write_submit_walltime_grows_with_chunk_rows(tmp_path: Path): run_dir = _prep(_write_inputs(tmp_path), chunks=2) meta = RunMeta.load(run_dir / "run_meta.json") + _fake_venv(tmp_path) cfg = SubmitConfig( run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path, n_chunks=2 ) From dbd5c7e083edfc5ed998ba827612fb901180e2bc Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 10:14:15 +0200 Subject: [PATCH 10/24] analyze: default condor docker image to alma9-gridjob mschnepf/slc7-condocker's ancient glibc/libstdc++ can't load current numpy/polars wheels from a uv-synced .venv (ImportError: CXXABI_1.3.9 not found). Switch the default to cverstege/alma9-gridjob, a modern EL9-based image. --- giant/analysis/condor.py | 2 +- giant/cli.py | 2 +- tests/test_condor.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 91c0023..85ded33 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -306,7 +306,7 @@ class SubmitConfig: run_dir: Path accounting_group: str repo_dir: Path - docker_image: str = "mschnepf/slc7-condocker" + docker_image: str = "cverstege/alma9-gridjob" request_memory_mb: int = 4096 request_cpus: int = 1 remote: bool = False # +RemoteJob (grid I/O) vs ProvidesETPResources (local files) diff --git a/giant/cli.py b/giant/cli.py index 48b738a..fca9ef2 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -1248,7 +1248,7 @@ def analyze_submit( ] = None, docker_image: Annotated[ str, typer.Option("--docker-image") - ] = "mschnepf/slc7-condocker", + ] = "cverstege/alma9-gridjob", request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 4096, remote: Annotated[ bool, diff --git a/tests/test_condor.py b/tests/test_condor.py index 89838a8..fd175b6 100644 --- a/tests/test_condor.py +++ b/tests/test_condor.py @@ -178,7 +178,7 @@ def test_write_submit_description(tmp_path: Path): cfg = SubmitConfig(run_dir=run_dir, accounting_group="cms", repo_dir=tmp_path) txt = write_submit(cfg).read_text() assert "universe = docker" in txt - assert "docker_image = mschnepf/slc7-condocker" in txt + assert "docker_image = cverstege/alma9-gridjob" in txt assert "requirements = TARGET.ProvidesETPResources" in txt assert "accounting_group = cms" in txt assert "+RequestWalltime = $(walltime)" in txt From c7194701f6649dcaf8a9dfa470650fbb23303b3a Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 10:31:46 +0200 Subject: [PATCH 11/24] analyze: raise default condor job memory request to 8192 MB 4096 MB was too tight: a resubmitted run held ~31 jobs spread evenly across nearly every plot family and chunk index with "Docker job has gone over memory limit of 4224 Mb", not one specific spec, so the generic per-chunk data footprint needed more headroom. --- giant/analysis/condor.py | 2 +- giant/cli.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 85ded33..914c8ab 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -307,7 +307,7 @@ class SubmitConfig: accounting_group: str repo_dir: Path docker_image: str = "cverstege/alma9-gridjob" - request_memory_mb: int = 4096 + request_memory_mb: int = 8192 request_cpus: int = 1 remote: bool = False # +RemoteJob (grid I/O) vs ProvidesETPResources (local files) n_chunks: int = 1 # per-plot data chunks; ignored for chunkable=False specs diff --git a/giant/cli.py b/giant/cli.py index fca9ef2..9958157 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -1249,7 +1249,7 @@ def analyze_submit( docker_image: Annotated[ str, typer.Option("--docker-image") ] = "cverstege/alma9-gridjob", - request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 4096, + request_memory: Annotated[int, typer.Option("--request-memory", help="MB")] = 8192, remote: Annotated[ bool, typer.Option("--remote/--local", help="+RemoteJob vs ProvidesETPResources"), From 4d6101dcd70389ee67a55014ea3f80298e05b628 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 11:06:40 +0200 Subject: [PATCH 12/24] analyze: recalibrate condor walltime model from real cluster timings The prior _COST_MODEL/_FIXED_OVERHEAD_S were fit only against local synthetic benchmarks (up to 2M rows/side), which can't see docker pull or real /ceph read latency and wildly overestimated real jobs (~1200-1800s predicted vs 50-320s median actual, from condor_history on production run 563f5ee3, --chunks 4, ~254M total rows). Refit each spec's per-row rate through the origin against its median real wall-clock time (not max, to avoid baking a few /ceph-contention spikes into a rate that would then wrongly scale with dataset size), and raised RUNTIME_SAFETY_MARGIN to compensate for that same contention risk instead. --- giant/analysis/runtime_estimate.py | 143 ++++++++++++++++------------- 1 file changed, 78 insertions(+), 65 deletions(-) diff --git a/giant/analysis/runtime_estimate.py b/giant/analysis/runtime_estimate.py index 29a7f95..7f6cc37 100644 --- a/giant/analysis/runtime_estimate.py +++ b/giant/analysis/runtime_estimate.py @@ -3,23 +3,34 @@ Each catalog spec's compute cost is close to linear in the number of input rows a `compute-one` job streams over — every spec is one (or a couple of) streaming `group_by` pass(es) over the chunk (see `catalog.py`/`reduce.py`). -`_COST_MODEL` below is ``spec_id -> (intercept_s, seconds_per_row)``, fit by -least squares against wall-clock timings of `compute_reduced` on synthetic -mock data of increasing size, run on a local dev machine (see -`scripts/profile_analysis_costs.py` — rerun it and paste the new numbers in -here if the catalog changes or this needs recalibrating). ``n_rows`` is the -combined rollout+reference row count of the job's input: the chunk's row -count for `chunkable=True` specs, the whole dataset's for the three -`chunkable=False` router specs (they always run as a single job regardless of -chunk count). +`_COST_MODEL` below is ``spec_id -> (intercept_s, seconds_per_row)``. +``n_rows`` is the combined rollout+reference row count of the job's input: +the chunk's row count for `chunkable=True` specs, the whole dataset's for the +three `chunkable=False` router specs (they always run as a single job +regardless of chunk count). -The fitted numbers only capture *local, in-memory compute* — they don't (and -from a laptop with no `/ceph` access, can't) capture the real condor job's -docker pull, `uv run` cold start, or shared-filesystem read latency, which in -practice likely dominate total wall time for anything but a huge single-chunk -job. `_FIXED_OVERHEAD_S` is a deliberately generous placeholder for all of -that combined; recalibrate it from real `condor_q`/log timings once some are -available, rather than trusting it as measured. +Calibrated 2026-07-27 from real HTCondor timings (`condor_history` +``RemoteWallClockTime``) of a production run: prediction ``563f5ee3`` +(PbWO4, 50 GeV) analyzed with ``--chunks 4`` against +``giant/analysis/runtime_estimate.py``'s prior (local-synthetic-only) model — +see the ``analysis-rollout-plots`` branch history for the raw data. That run's +4 chunks came out at nearly identical row counts (~63-64M rows each, ~254M +total), so this real data has no genuine row-count spread to fit a slope +against — instead each spec's ``per_row`` here is a single line through the +origin (``intercept=0``) hitting that spec's *median* wall-clock time across +its 4 chunks at that run's row count. A handful of (spec, chunk) pairs showed +3-8x spikes in one chunk only (e.g. ``marginal_edep_by_material``: 88, 88, 90, +722s) — almost certainly shared ``/ceph`` contention from ~130 jobs landing on +the filesystem at once right after submission, not a real per-row cost, so +the median (not the max) was fit to avoid baking that noise into a rate that +would then wrongly scale up with a bigger dataset. `RUNTIME_SAFETY_MARGIN` is +deliberately generous (4x total) specifically to absorb that kind of +contention spike instead. Rerun this calibration (pull fresh +`condor_history`/`run_meta.json`, refit) if the catalog changes or timings +drift — a synthetic local rebaseline via `scripts/profile_analysis_costs.py` +is a reasonable fallback when no real cluster data is available yet, but +undershoots real wall time badly (it can't see docker pull / `/ceph` I/O +latency), which is exactly why this file moved off it. """ from __future__ import annotations @@ -27,62 +38,64 @@ from __future__ import annotations import math # Multiplicative pad applied to every job's estimated walltime. The one knob -# this feature was asked to expose. -RUNTIME_SAFETY_MARGIN = 1.00 +# this feature was asked to expose. Set generously (4x total, i.e. 3.0 here) +# to absorb the shared-/ceph-contention spikes described above rather than +# encoding them into individual specs' per-row rates. +RUNTIME_SAFETY_MARGIN = 3.00 -# Docker image pull + `uv run` startup + shared (/ceph, ETP) filesystem read -# latency — not measurable on a machine with no /ceph access, so this is a -# conservative placeholder rather than a fit. Recalibrate from real job logs. -_FIXED_OVERHEAD_S = 600.0 +# Fixed per-job overhead (docker start, `.venv/bin/giant` startup, initial +# `/ceph` read latency) — calibrated as the fastest observed real spec +# (`leakage_fraction`, median 51s) rounded up, since even the cheapest spec +# streams the whole chunk once. +_FIXED_OVERHEAD_S = 60.0 -# Router diagnostics need a live torch checkpoint to do any real work; this -# machine has none, so their cost (torch.load + a bounded inference pass over -# <= 200k subsampled rows, independent of chunk size) couldn't be profiled -# either. Fixed budget, on top of _FIXED_OVERHEAD_S, instead of a row-based fit. -_ROUTER_FIXED_S = 300.0 +# Router diagnostics run a live torch checkpoint (bounded inference over +# <=200k subsampled rows, independent of chunk size) instead of a row-based +# scan. Calibrated from the 3 real router jobs' observed wall times (119, 66, +# 124s) — max minus _FIXED_OVERHEAD_S, on top of it. +_ROUTER_FIXED_S = 64.0 _ROUTER_IDS = frozenset({"router_gating", "router_share_by_pdg", "router_share_by_process"}) # Conservative fallback for any catalog id not in _COST_MODEL (e.g. a plot -# added after the last profiling run) — the most expensive fitted (intercept, -# seconds/row) pair observed, rounded up. -_DEFAULT_COST = (0.02, 2.0e-6) +# added after the last calibration run) — the most expensive fitted per-row +# rate observed, plus a small constant pad. +_DEFAULT_COST = (5.0, 3.0e-6) -# spec_id -> (intercept_s, seconds_per_row), fit on this machine 2026-07-27 -# via `scripts/profile_analysis_costs.py` against SIDE_ROW_COUNTS up to 2M -# rows/side (4M combined). +# spec_id -> (intercept_s, seconds_per_row), fit 2026-07-27 from real +# HTCondor `RemoteWallClockTime` (see module docstring for methodology). _COST_MODEL: dict[str, tuple[float, float]] = { - "marginal_step_length": (0.003581, 0.000000042), - "marginal_step_length_by_energy": (0.010336, 0.000000081), - "marginal_step_length_by_pdg": (0.007231, 0.000000042), - "marginal_step_length_by_material": (0.004662, 0.000000048), - "marginal_edep": (0.003996, 0.000000042), - "marginal_edep_by_energy": (0.007977, 0.000000085), - "marginal_edep_by_pdg": (0.003535, 0.000000042), - "marginal_edep_by_material": (0.003893, 0.000000047), - "marginal_delta_e": (0.001380, 0.000000053), - "marginal_delta_e_by_energy": (0.006543, 0.000000093), - "marginal_delta_e_by_pdg": (0.001761, 0.000000054), - "marginal_delta_e_by_material": (0.000000, 0.000000168), - "marginal_post_E": (0.000000, 0.000000146), - "marginal_post_E_by_energy": (0.000000, 0.000000509), - "marginal_post_E_by_pdg": (0.000000, 0.000000149), - "marginal_post_E_by_material": (0.000000, 0.000000168), - "marginal_cos_scatter": (0.000000, 0.000000249), - "marginal_cos_scatter_by_energy": (0.000000, 0.000000553), - "marginal_cos_scatter_by_pdg": (0.000000, 0.000000270), - "marginal_cos_scatter_by_material": (0.000000, 0.000000260), - "event_total_edep": (0.000000, 0.000000512), - "event_total_edep_by_energy": (0.000000, 0.000000241), - "event_mean_length": (0.000000, 0.000000090), - "event_n_steps": (0.002021, 0.000000066), - "shower_longitudinal": (0.000000, 0.000001804), - "shower_transverse": (0.000000, 0.000001731), - "species_edep_share": (0.007505, 0.000000017), - "leakage_fraction": (0.003798, 0.000000026), - "sec_count_per_event": (0.006551, 0.000000054), - "sec_count_per_species": (0.006708, 0.000000042), - "sec_energy": (0.006856, 0.000000047), - "sec_cos_angle": (0.001780, 0.000000234), + "marginal_step_length": (0.0, 5.199e-07), + "marginal_step_length_by_energy": (0.0, 1.678e-06), + "marginal_step_length_by_pdg": (0.0, 4.569e-07), + "marginal_step_length_by_material": (0.0, 2.269e-06), + "marginal_edep": (0.0, 2.804e-06), + "marginal_edep_by_energy": (0.0, 1.386e-06), + "marginal_edep_by_pdg": (0.0, 4.490e-07), + "marginal_edep_by_material": (0.0, 4.333e-07), + "marginal_delta_e": (0.0, 5.042e-07), + "marginal_delta_e_by_energy": (0.0, 1.678e-06), + "marginal_delta_e_by_pdg": (0.0, 4.727e-07), + "marginal_delta_e_by_material": (0.0, 4.490e-07), + "marginal_post_E": (0.0, 4.805e-07), + "marginal_post_E_by_energy": (0.0, 1.284e-06), + "marginal_post_E_by_pdg": (0.0, 4.569e-07), + "marginal_post_E_by_material": (0.0, 4.490e-07), + "marginal_cos_scatter": (0.0, 5.436e-07), + "marginal_cos_scatter_by_energy": (0.0, 1.363e-06), + "marginal_cos_scatter_by_pdg": (0.0, 4.727e-07), + "marginal_cos_scatter_by_material": (0.0, 4.727e-07), + "event_total_edep": (0.0, 4.490e-07), + "event_total_edep_by_energy": (0.0, 4.411e-07), + "event_mean_length": (0.0, 4.333e-07), + "event_n_steps": (0.0, 4.569e-07), + "shower_longitudinal": (0.0, 2.348e-06), + "shower_transverse": (0.0, 2.899e-06), + "species_edep_share": (0.0, 4.333e-07), + "leakage_fraction": (0.0, 0.0), + "sec_count_per_event": (0.0, 4.727e-07), + "sec_count_per_species": (0.0, 4.963e-07), + "sec_energy": (0.0, 4.727e-07), + "sec_cos_angle": (0.0, 2.749e-06), } From eb751d968d262df8da3bb23cbada671619c0833f Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 11:06:48 +0200 Subject: [PATCH 13/24] transforms: pad legacy cond normalizers for pre-physical-conditioning checkpoints Checkpoints trained before commit 68fb99b (physical-property conditioning, COND_DIM 8->15) saved a COND_DIM_BASE-wide cond normalizer, fit before build_cond_features grew the extra physical columns. Any inference against such a checkpoint under current code (predict/rollout/router_gating) crashed broadcasting a 15-wide cond_cont against an 8-wide mean/std. In "embedding" mode those physical columns are never read by ConditionEncoder, so padding the missing entries with mean=0/std=1 is a safe no-op. "physical" mode reads them directly, so a mismatch there still raises instead of silently normalizing garbage. --- giant/data/transforms.py | 35 +++++++++++++++++++++++++++++++- tests/test_transforms.py | 43 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 77 insertions(+), 1 deletion(-) diff --git a/giant/data/transforms.py b/giant/data/transforms.py index 9b74ea7..d83148c 100644 --- a/giant/data/transforms.py +++ b/giant/data/transforms.py @@ -530,11 +530,44 @@ def build_cond_features( cond_cat = np.column_stack([pdg_idx, mat_idx]) if cond_normalizer is not None: - cond_cont = cond_normalizer.transform(cond_cont) + cond_cont = _cond_normalizer_transform(cond_cont, cond_normalizer, conditioning) return cond_cont, cond_cat +def _cond_normalizer_transform( + cond_cont: np.ndarray, cond_normalizer: "Normalizer", conditioning: str +) -> np.ndarray: + """Apply ``cond_normalizer``, padding a legacy narrower normalizer if needed. + + Checkpoints trained before physical-property conditioning (``COND_DIM`` + 8->15, ``giant/constants.py``) saved a ``COND_DIM_BASE``-wide (8) cond + normalizer, fit before ``build_cond_features`` grew the extra physical + columns. In "embedding" mode those columns are never read by + ``ConditionEncoder`` (``giant/model/network.py``), so padding the missing + entries with mean=0/std=1 is a safe no-op that keeps such checkpoints + usable under the current, always-``COND_DIM``-wide contract. In + "physical" mode the physical columns are load-bearing, so a mismatch + there is a real incompatibility, not something to paper over. + """ + mean, std = cond_normalizer.mean, cond_normalizer.std + assert mean is not None and std is not None, "Normalizer not fitted" + width = cond_cont.shape[-1] + if mean.shape[-1] < width: + if conditioning != "embedding": + raise ValueError( + f"cond normalizer has {mean.shape[-1]} columns, expected " + f"{width}, and conditioning={conditioning!r} reads the " + "physical columns directly — this checkpoint predates " + "physical-property conditioning and can't be safely padded; " + "retrain it under the current code." + ) + pad = width - mean.shape[-1] + mean = np.concatenate([mean, np.zeros(pad, dtype=mean.dtype)]) + std = np.concatenate([std, np.ones(pad, dtype=std.dtype)]) + return ((cond_cont - mean) / std).astype(np.float32) + + def build_features( data: dict[str, np.ndarray], pdg_map: dict[int, int], diff --git a/tests/test_transforms.py b/tests/test_transforms.py index a7af975..c9bc7ac 100644 --- a/tests/test_transforms.py +++ b/tests/test_transforms.py @@ -397,3 +397,46 @@ def test_build_cond_features_mass_charge_override(fake_material_props): cond_cont[:, COND_DIM_BASE], log_transform(np.array([123.0, 456.0])) ) np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE + 1], [2.0, -2.0]) + + +def test_build_cond_features_pads_legacy_normalizer_in_embedding_mode(): + """A pre-physical-conditioning checkpoint's cond normalizer is COND_DIM_BASE + (8) wide, fit before build_cond_features grew the extra physical columns. + In "embedding" mode those columns are never read downstream, so a legacy + normalizer should be usable as-is (padded, not rejected).""" + data = _minimal_step_data(3) + pdg_map, mat_map = {11: 0}, {"PbWO4": 0} + legacy_norm = Normalizer() + legacy_norm.mean = np.zeros(COND_DIM_BASE, dtype=np.float32) + legacy_norm.std = np.ones(COND_DIM_BASE, dtype=np.float32) + + cond_cont, _ = build_cond_features( + data, pdg_map, mat_map, cond_normalizer=legacy_norm, conditioning="embedding" + ) + + assert cond_cont.shape[-1] == COND_DIM + # padded physical columns are zero-filled pre-normalization and + # mean=0/std=1 post-normalization, so they should come out as zero + np.testing.assert_allclose(cond_cont[:, COND_DIM_BASE:], 0.0) + + +def test_build_cond_features_rejects_legacy_normalizer_in_physical_mode( + fake_material_props, +): + """Unlike "embedding" mode, "physical" mode actually reads the physical + columns, so a legacy 8-wide normalizer can't be silently padded — that + would silently feed the network un-normalized physical properties.""" + data = _minimal_step_data(3) + pdg_map, mat_map = {11: 0}, {"PbWO4": 0} + legacy_norm = Normalizer() + legacy_norm.mean = np.zeros(COND_DIM_BASE, dtype=np.float32) + legacy_norm.std = np.ones(COND_DIM_BASE, dtype=np.float32) + + with pytest.raises(ValueError, match="predates physical-property conditioning"): + build_cond_features( + data, + pdg_map, + mat_map, + cond_normalizer=legacy_norm, + conditioning="physical", + ) From a88b21ef70f6a84c76e85b19a2eae0a01cfcfc7f Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 11:19:06 +0200 Subject: [PATCH 14/24] style: ruff format --- giant/analysis/condor.py | 8 ++++++-- giant/analysis/runtime_estimate.py | 4 +++- scripts/profile_analysis_costs.py | 29 ++++++++++++++++++++--------- 3 files changed, 29 insertions(+), 12 deletions(-) diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 914c8ab..13c5183 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -130,7 +130,9 @@ class RunMeta: return cls(**json.loads(Path(path).read_text())) -def _rows_per_chunk(rollout: str | Path, reference: str | Path, n_chunks: int) -> list[int]: +def _rows_per_chunk( + rollout: str | Path, reference: str | Path, n_chunks: int +) -> list[int]: """Rollout+reference row count of each ``event_id % n_chunks`` chunk. One cheap streaming ``group_by`` per side (just the ``event_id`` column) — @@ -345,7 +347,9 @@ def _submit_description(cfg: SubmitConfig, wrapper: Path, jobs_file: Path) -> st ) -def _job_walltimes(run_dir: Path, ids: list[str], n_chunks: int) -> list[tuple[str, int, int]]: +def _job_walltimes( + run_dir: Path, ids: list[str], n_chunks: int +) -> list[tuple[str, int, int]]: """``(spec_id, chunk, walltime_s)`` for every job, sized from ``run_meta.json``. Row counts come from ``prep``'s ``RunMeta.rows_per_chunk``/``total_rows``; diff --git a/giant/analysis/runtime_estimate.py b/giant/analysis/runtime_estimate.py index 7f6cc37..f5279f7 100644 --- a/giant/analysis/runtime_estimate.py +++ b/giant/analysis/runtime_estimate.py @@ -54,7 +54,9 @@ _FIXED_OVERHEAD_S = 60.0 # scan. Calibrated from the 3 real router jobs' observed wall times (119, 66, # 124s) — max minus _FIXED_OVERHEAD_S, on top of it. _ROUTER_FIXED_S = 64.0 -_ROUTER_IDS = frozenset({"router_gating", "router_share_by_pdg", "router_share_by_process"}) +_ROUTER_IDS = frozenset( + {"router_gating", "router_share_by_pdg", "router_share_by_process"} +) # Conservative fallback for any catalog id not in _COST_MODEL (e.g. a plot # added after the last calibration run) — the most expensive fitted per-row diff --git a/scripts/profile_analysis_costs.py b/scripts/profile_analysis_costs.py index bbb0a98..de97a2b 100644 --- a/scripts/profile_analysis_costs.py +++ b/scripts/profile_analysis_costs.py @@ -68,9 +68,7 @@ def _make_rollout(n: int, n_events: int, seed: int) -> pl.DataFrame: reasons = np.where( is_synthetic, - rng.choice( - ["escaped", "energy_cutoff", "max_steps", "unknown_pdg"], size=n - ), + rng.choice(["escaped", "energy_cutoff", "max_steps", "unknown_pdg"], size=n), "natural_end", ) @@ -96,7 +94,9 @@ def _make_rollout(n: int, n_events: int, seed: int) -> pl.DataFrame: "post_dx": post_dir[:, 0], "post_dy": post_dir[:, 1], "post_dz": post_dir[:, 2], - "edep": np.where(is_synthetic, np.where(reasons == "escaped", 0.0, pre_E), edep), + "edep": np.where( + is_synthetic, np.where(reasons == "escaped", 0.0, pre_E), edep + ), "step_length": np.where(is_synthetic, 0.0, step_length), "material": rng.choice(_MATERIALS, size=n), "layer_id": rng.integers(0, 30, size=n), @@ -163,11 +163,19 @@ def _make_reference(n: int, n_events: int, seed: int) -> pl.DataFrame: ) -def _time(spec_id: str, rollout: Path, reference: Path, shared: Path, out: Path) -> float: +def _time( + spec_id: str, rollout: Path, reference: Path, shared: Path, out: Path +) -> float: t0 = time.perf_counter() compute_reduced( - spec_id, rollout, reference, shared, out, checkpoint=None, - chunk_index=0, n_chunks=1, + spec_id, + rollout, + reference, + shared, + out, + checkpoint=None, + chunk_index=0, + n_chunks=1, ) return time.perf_counter() - t0 @@ -187,8 +195,11 @@ def main() -> None: shared = tmp_path / f"shared_{n_side}.json" ctx = build_context( - rollout, reference, - n_energy_bins=4, n_marginal_bins=50, top_k_pdg=6, + rollout, + reference, + n_energy_bins=4, + n_marginal_bins=50, + top_k_pdg=6, sample_rows=min(n_side, 200_000), ) ctx.save(shared) From 61410ddee37831c4e83be2fc42ba8b2d1011fa7a Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 11:29:35 +0200 Subject: [PATCH 15/24] analyze: default run directory to /analysis_runs, gitignored MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit giant analyze prep/submit previously defaulted the run directory to next to the rollout parquet on /ceph. Default it instead to /analysis_runs/analysis_ so it lands inside the portal repo checkout (/work) — gitignored, --run-dir still overrides it. derive_run_dir/prep gained a default_base param; library callers that don't pass one keep the old parquet-relative fallback. --- .gitignore | 3 +++ giant/analysis/condor.py | 25 ++++++++++++++++++++----- giant/cli.py | 10 ++++++++-- tests/test_condor.py | 9 +++++++++ 4 files changed, 40 insertions(+), 7 deletions(-) diff --git a/.gitignore b/.gitignore index 377b0b7..95aeaf8 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,6 @@ checkpoints/ # Scratch working directory /scratchpad/ + +# giant analyze run directories (shared.json, reduced/, plots/, condor logs) +/analysis_runs/ diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 13c5183..039c6e9 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -93,13 +93,26 @@ def load_rollout_yaml(path: str | Path) -> dict: return d -def derive_run_dir(rollout_yaml: dict, run_dir: str | Path | None = None) -> Path: - """Analysis output directory, next to the rollout parquet unless overridden.""" +def derive_run_dir( + rollout_yaml: dict, + run_dir: str | Path | None = None, + default_base: str | Path | None = None, +) -> Path: + """Analysis output directory. + + Precedence: an explicit ``run_dir`` always wins. Otherwise + ``default_base / analysis_`` if ``default_base`` is given (the CLI + passes the repo's gitignored ``analysis_runs/``, so run directories don't + pile up on ``/ceph`` next to the rollout parquet). Falls back to next to + the rollout parquet — the original convention — for callers that don't + care where the run directory lives. + """ if run_dir is not None: return Path(run_dir) rollout = Path(rollout_yaml["output"]) tag = str(rollout_yaml.get("prediction_id") or rollout.stem)[:8] - return rollout.parent / f"analysis_{tag}" + base = Path(default_base) if default_base is not None else rollout.parent + return base / f"analysis_{tag}" def _plot_meta(rollout_yaml: dict) -> dict: @@ -160,6 +173,7 @@ def prep( rollout_yaml: str | Path, run_dir: str | Path | None = None, n_chunks: int = 1, + default_base: str | Path | None = None, **ctx_kwargs, ) -> Path: """Read the rollout YAML, build the shared context, and lay out the run dir. @@ -168,10 +182,11 @@ def prep( ``n_chunks`` is the run-level chunk count every ``compute-one``/``merge-one`` job reads back out of ``run_meta.json`` (via ``RunMeta.n_chunks``), so it is resolved once here rather than re-passed (and risking disagreement) at every - later step. + later step. See ``derive_run_dir`` for how ``run_dir``/``default_base`` + resolve the actual directory. """ y = load_rollout_yaml(rollout_yaml) - run_path = derive_run_dir(y, run_dir) + run_path = derive_run_dir(y, run_dir, default_base=default_base) run_path.mkdir(parents=True, exist_ok=True) rollout, reference = y["output"], y["dataset"] diff --git a/giant/cli.py b/giant/cli.py index 9958157..511bad2 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -1144,7 +1144,7 @@ def analyze_prep( typer.Option( "--run-dir", "-o", - help="Override the run directory (default: next to the rollout parquet)", + help="Override the run directory (default: /analysis_runs/analysis_)", ), ] = None, n_energy_bins: Annotated[int, typer.Option("--energy-bins")] = 4, @@ -1164,6 +1164,7 @@ def analyze_prep( rollout_yaml, run_dir, n_chunks=chunks, + default_base=Path.cwd() / "analysis_runs", n_energy_bins=n_energy_bins, n_marginal_bins=n_marginal_bins, top_k_pdg=top_k_pdg, @@ -1244,7 +1245,11 @@ def analyze_submit( accounting_group: Annotated[str, typer.Option("--accounting-group")], run_dir: Annotated[ Optional[Path], - typer.Option("--run-dir", "-o", help="Override the run directory"), + typer.Option( + "--run-dir", + "-o", + help="Override the run directory (default: /analysis_runs/analysis_)", + ), ] = None, docker_image: Annotated[ str, typer.Option("--docker-image") @@ -1277,6 +1282,7 @@ def analyze_submit( rollout_yaml, run_dir, n_chunks=chunks, + default_base=Path.cwd() / "analysis_runs", n_energy_bins=n_energy_bins, n_marginal_bins=n_marginal_bins, top_k_pdg=top_k_pdg, diff --git a/tests/test_condor.py b/tests/test_condor.py index fd175b6..c4259ac 100644 --- a/tests/test_condor.py +++ b/tests/test_condor.py @@ -89,6 +89,15 @@ def test_derive_run_dir_next_to_rollout(): assert derive_run_dir(y, "/somewhere") == Path("/somewhere") +def test_derive_run_dir_default_base(): + y = {"output": "/data/roll.parquet", "prediction_id": "abcd1234ef", "dataset": "d"} + assert derive_run_dir(y, default_base="/work/lbogner/giant2/analysis_runs") == Path( + "/work/lbogner/giant2/analysis_runs/analysis_abcd1234" + ) + # an explicit run_dir still wins over default_base + assert derive_run_dir(y, "/somewhere", default_base="/other") == Path("/somewhere") + + def test_prep_lays_out_run_dir(tmp_path: Path): yaml_path = _write_inputs(tmp_path) run_dir = _prep(yaml_path) From 641bbb0a682b61b493e8e1a4272a91a907740ef9 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 12:02:59 +0200 Subject: [PATCH 16/24] docs: record first MoE router rollout benchmark result in the roadmap The 10-expert EnergyRouter checkpoint (lambda_balance=0.0) diverges badly from Geant4 on rollout (step granularity, secondary species, shower shape), and the router_gating diagnostic shows the experts heavily overlap rather than partitioning the pre-step energy domain. Recorded as "needs retraining with a different router config" rather than an abandoned direction. Full analysis in the knowledge base at experiments/giant-router-energy-rollout-validation.md. Co-Authored-By: Claude Sonnet 5 --- CLAUDE.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index bbeb296..7167339 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -13,7 +13,7 @@ pytest # run tests giant train path/to/steps.parquet --mode flow # train (flow matching) giant train path/to/steps.parquet --mode ddpm # train (DDPM baseline) giant train path/to/steps.parquet --mode wgan # train (WGAN-GP, single-pass eval; implemented, not yet tested) -giant train path/to/steps.parquet --router --router-type energy # MoE routing trunk (implemented, currently under testing) +giant train path/to/steps.parquet --router --router-type energy # MoE routing trunk (implemented; first rollout benchmark failed with lambda_balance=0, retrain needed — see Roadmap) giant predict path/to/steps.parquet --checkpoint ckpt/best.pt # per-step predictions giant rollout path/to/steps.parquet --checkpoint ckpt/best.pt --geometry oracle.pkl # full showers giant analyze submit rollout.yaml --accounting-group cms # parallel rollout-vs-reference analysis on HTCondor @@ -68,7 +68,7 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep **WGAN-GP mode (`--mode wgan`, implemented, not yet tested):** a throwaway fast-eval alternative to the flow/DDPM samplers above — single forward pass instead of ~10 ODE steps. Dedicated noise-conditioned generators (`WGANGenerator`/`WGANSecondaryGenerator`, `giant/model/network.py`) stand in for `DenoisingMLP`/`SecondaryDecoder`, trained against `Critic`/`SecondaryCritic` discriminators with the gradient-penalty loss in `giant/model/wgan.py` (Gulrajani et al. 2017); `sample_wgan` (`giant/sample.py`) does the single-pass draw at inference. Not yet validated against the flow-matching baseline. -**MoE routing trunk (`--router`, implemented, currently under testing):** an alternative to `DenoisingMLP`'s monolithic `ResBlock` trunk — a `Router` (`giant/model/network.py`, `ROUTER_REGISTRY`/`build_router`) gates between small per-expert `ResBlock` stacks (`Expert`), soft-mixed over all experts at train time but **top-1 dispatched at eval time** (each row runs exactly one small expert), which is the actual inference-speed win. Router types gate on different conditioning axes: `EnergyRouter`/`PdgRouter` read a quantity already known at inference time, `ProcessRouter` runs its own small classifier over pre-step conditioning (since process isn't known upfront); `ComposedRouter` gates jointly over multiple axes (outer-product expert cells) via repeated `--router-axis "type:key=val,..."` flags. Config lives under `model.router` (`giant/config.py`), deep-merged one level so `router.enabled` alone doesn't drop the rest of the defaults. +**MoE routing trunk (`--router`, implemented; first rollout benchmark shows the experts don't specialize — see Roadmap):** an alternative to `DenoisingMLP`'s monolithic `ResBlock` trunk — a `Router` (`giant/model/network.py`, `ROUTER_REGISTRY`/`build_router`) gates between small per-expert `ResBlock` stacks (`Expert`), soft-mixed over all experts at train time but **top-1 dispatched at eval time** (each row runs exactly one small expert), which is the actual inference-speed win. Router types gate on different conditioning axes: `EnergyRouter`/`PdgRouter` read a quantity already known at inference time, `ProcessRouter` runs its own small classifier over pre-step conditioning (since process isn't known upfront); `ComposedRouter` gates jointly over multiple axes (outer-product expert cells) via repeated `--router-axis "type:key=val,..."` flags. Config lives under `model.router` (`giant/config.py`), deep-merged one level so `router.enabled` alone doesn't drop the rest of the defaults. **Validation** (`giant/validate.py`): step-level marginal comparisons. @@ -86,7 +86,7 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep **Faster-eval architectures (implemented, validation in progress):** both tracks below target a ~10× native-Geant4 eval budget and are now wired into `giant train`/`giant/model/network.py`, but neither has a validated result yet — treat both as unproven until the corresponding analysis run says otherwise: - **WGAN-GP** (`--mode wgan`, see Architecture above): implemented, **not yet tested** — no rollout-vs-reference analysis run against it yet. -- **MoE routing trunk** (`--router`, see Architecture above): implemented, **currently undergoing testing** — this is what the `giant analyze` MoE router gating/share diagnostic plots (`giant/analysis/router_gating.py`) were built to evaluate. +- **MoE routing trunk** (`--router`, see Architecture above): implemented, **first rollout benchmark done (2026-07-22), result: needs retraining with a different router config, not abandoned.** A 10-expert `EnergyRouter` run (`n_experts=10`, `temperature=0.5`, `learn_centers=true`, **`lambda_balance=0.0`**, only 20 fine-tuning epochs resumed from a non-routed checkpoint) diverged badly from Geant4 on step granularity, secondary species, and shower shape, despite roughly matching bulk total deposited energy. The `router_gating` diagnostic plot points at the likely cause: the ten experts overlap heavily across ~5 decades of pre-step energy instead of partitioning it — even the top-energy expert only reaches ~60–65% gate weight at the highest energies plotted — so eval-time top-1 (Voronoi) dispatch is choosing among near-ties rather than real specialists. Leading suspect is the missing load-balancing loss (`lambda_balance=0.0`); the routing *strategy* itself may still be sound, but this specific config isn't. **Next step before further evaluation: retrain with `lambda_balance > 0` (and consider more epochs / a from-scratch run rather than a short fine-tune) and re-check whether `router_gating` sharpens up.** Full writeup: `/home/lars/knowledge-base/experiments/giant-router-energy-rollout-validation.md`. A sampling-calorimeter (multi-material) dataset is still a planned future direction, not yet built. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`). From ee29b9a30394aae3837ee22e89c5a2ee40ce931d Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 13:11:16 +0200 Subject: [PATCH 17/24] router: seed EnergyRouter centers from data quantiles instead of a fixed linspace The 2026-07-22 rollout benchmark's router_gating diagnostic showed the 10-expert EnergyRouter's default linspace(-2, 2, n_experts) init assumes a roughly uniform z-normalized energy distribution, leaving experts heavily overlapping instead of partitioning the range. Add an optional centers_init kwarg (backward compatible, defaults to the old linspace) and have giant train estimate it from a reservoir sample of the real energy column, collected during the existing normalizer-fitting pass. --- giant/data/transforms.py | 48 ++++++++++++++++++++++++++++++++++++++++ giant/model/network.py | 24 +++++++++++++++----- giant/pipeline.py | 28 ++++++++++++++++++++++- tests/test_router.py | 36 ++++++++++++++++++++++++++++++ 4 files changed, 130 insertions(+), 6 deletions(-) diff --git a/giant/data/transforms.py b/giant/data/transforms.py index d83148c..a4a5222 100644 --- a/giant/data/transforms.py +++ b/giant/data/transforms.py @@ -228,6 +228,54 @@ class _WelfordAccumulator: return norm +class _ReservoirSampler: + """Uniform random sample of a fixed capacity drawn from a data stream. + + Algorithm R (Vitter 1985), vectorized per chunk so it stays cheap over + hundreds of millions of rows: use to get a representative subsample of + a column for a distribution estimate (e.g. quantiles) without + materializing the full column. + + sampler = _ReservoirSampler(capacity=100_000) + for chunk in data: + sampler.update(chunk) + sample = sampler.sample + """ + + def __init__(self, capacity: int, seed: int = 0) -> None: + self.capacity = capacity + self.n_seen = 0 + self._rng = np.random.default_rng(seed) + self._reservoir = np.empty(0, dtype=np.float64) + + def update(self, values: np.ndarray) -> None: + values = np.asarray(values, dtype=np.float64).reshape(-1) + if values.size == 0: + return + n_before = self.n_seen + if n_before < self.capacity: + take = min(values.size, self.capacity - n_before) + self._reservoir = np.concatenate([self._reservoir, values[:take]]) + values = values[take:] + n_before += take + self.n_seen = n_before + values.size + if values.size == 0 or self.capacity == 0: + return + # remaining elements are past the fill phase: element at 1-based + # stream position j replaces a uniformly random reservoir slot with + # probability capacity/j, which yields a uniform sample overall. + positions = n_before + np.arange(1, values.size + 1) + accept = self._rng.random(values.size) < (self.capacity / positions) + accept_idx = np.nonzero(accept)[0] + if accept_idx.size > 0: + slots = self._rng.integers(0, self.capacity, size=accept_idx.size) + self._reservoir[slots] = values[accept_idx] + + @property + def sample(self) -> np.ndarray: + return self._reservoir.astype(np.float32) + + def travel_direction(pre_pos: np.ndarray, post_pos: np.ndarray) -> np.ndarray: """World-frame unit vector pointing from pre_pos to post_pos. diff --git a/giant/model/network.py b/giant/model/network.py index 5596a17..5345747 100644 --- a/giant/model/network.py +++ b/giant/model/network.py @@ -1,6 +1,7 @@ import inspect import math import re +from collections.abc import Sequence import torch import torch.nn as nn @@ -600,10 +601,14 @@ class EnergyRouter(Router): """Soft turn-on gate over normalized pre-step log-energy. Reads `cond_cont[:, energy_idx]` (ignores cond_cat). Learnable (or - fixed) 1-D centers, initialized spread across [-2, 2] — roughly the - z-normalized energy range. `gate(e) = softmax_i(-(e - c_i)^2 / tau)`, - differentiable in e; as tau -> 0 this hardens to nearest-center - (Voronoi) selection, which is exactly what `top1` uses at eval. + fixed) 1-D centers. By default initialized spread evenly across + [-2, 2] — an assumed-uniform z-normalized energy range that may not + match the true (often skewed) distribution and can leave experts + overlapping instead of partitioning the range; pass `centers_init` to + seed them from data (e.g. energy quantiles) instead. + `gate(e) = softmax_i(-(e - c_i)^2 / tau)`, differentiable in e; as + tau -> 0 this hardens to nearest-center (Voronoi) selection, which is + exactly what `top1` uses at eval. """ def __init__( @@ -612,11 +617,20 @@ class EnergyRouter(Router): temperature: float = 0.5, learn_centers: bool = True, energy_idx: int = 3, + centers_init: Sequence[float] | None = None, ) -> None: super().__init__(n_experts) self.temperature = temperature self.energy_idx = energy_idx - centers = torch.linspace(-2.0, 2.0, n_experts) + if centers_init is None: + centers = torch.linspace(-2.0, 2.0, n_experts) + else: + if len(centers_init) != n_experts: + raise ValueError( + f"centers_init has {len(centers_init)} values, " + f"expected n_experts={n_experts}" + ) + centers = torch.tensor(list(centers_init), dtype=torch.float32) if learn_centers: self.centers = nn.Parameter(centers) else: diff --git a/giant/pipeline.py b/giant/pipeline.py index c06a0d0..6518059 100644 --- a/giant/pipeline.py +++ b/giant/pipeline.py @@ -20,7 +20,7 @@ from giant.data.loader import ( build_index_maps_from_files, build_process_map_from_files, ) -from giant.data.transforms import build_features, _WelfordAccumulator +from giant.data.transforms import build_features, _WelfordAccumulator, _ReservoirSampler from giant.data.dataset import make_event_split, StreamingStepsDataset from giant.model.network import build_models, build_critics from giant.train import train as run_training @@ -83,6 +83,18 @@ def run_train_job( cond_acc = _WelfordAccumulator(COND_DIM) tgt_acc = _WelfordAccumulator(X_DIM) sec_phys_acc = _WelfordAccumulator(PARTICLE_PHYS_DIM) + # EnergyRouter's default center spread (linspace over [-2, 2]) assumes + # the z-normalized energy column is roughly uniform, which real energy + # spectra rarely are — collect a reservoir sample here (reusing this + # same pass, not a second scan) so centers can instead be seeded from + # actual data quantiles below. + energy_router_active = ( + router_cfg.get("enabled") and router_cfg.get("type") == "energy" + ) + energy_idx = router_cfg.get("energy_idx", 3) + energy_sampler = ( + _ReservoirSampler(capacity=100_000) if energy_router_active else None + ) for path in files: for chunk in iter_file_chunks(path): mask = np.isin(chunk["event_id"], events_arr) @@ -99,6 +111,8 @@ def run_train_job( ) cond_acc.update(cond_cont) tgt_acc.update(target_s1) + if energy_sampler is not None: + energy_sampler.update(cond_cont[:, energy_idx]) sec_valid = np.arange(K_MAX)[None, :] < n_sec[:, None] sec_phys = sec_cont[:, :, 4:6][sec_valid] if len(sec_phys) > 0: @@ -107,6 +121,18 @@ def run_train_job( tgt_norm = tgt_acc.to_normalizer() sec_phys_norm = sec_phys_acc.to_normalizer() + if energy_sampler is not None and energy_sampler.n_seen > 0: + assert cond_norm.mean is not None and cond_norm.std is not None + normalized_sample = ( + energy_sampler.sample - cond_norm.mean[energy_idx] + ) / cond_norm.std[energy_idx] + quantiles = np.linspace(0.0, 1.0, router_cfg["n_experts"]) + centers_init = np.quantile(normalized_sample, quantiles).astype(np.float32) + router_cfg["centers_init"] = centers_init.tolist() + echo( + f" seeded EnergyRouter centers from data quantiles: {router_cfg['centers_init']}" + ) + train_ds = StreamingStepsDataset( files=files, split_events=train_events, diff --git a/tests/test_router.py b/tests/test_router.py index 8f3fa2a..62a8c86 100644 --- a/tests/test_router.py +++ b/tests/test_router.py @@ -97,6 +97,42 @@ def test_build_router_ignores_unrecognized_kwargs(): assert router.temperature == 0.3 +def test_energy_router_default_centers_are_linspace(): + router = EnergyRouter(n_experts=4) + torch.testing.assert_close(router.centers, torch.linspace(-2.0, 2.0, 4)) + + +def test_energy_router_centers_init_overrides_default(): + centers_init = [-1.0, 0.0, 0.5, 3.0] + router = EnergyRouter(n_experts=4, centers_init=centers_init) + torch.testing.assert_close(router.centers, torch.tensor(centers_init)) + + +def test_energy_router_centers_init_wrong_length_raises(): + try: + EnergyRouter(n_experts=4, centers_init=[0.0, 1.0]) + except ValueError: + return + raise AssertionError("expected ValueError for centers_init length mismatch") + + +def test_energy_router_centers_init_respects_learn_centers_flag(): + learned = EnergyRouter( + n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=True + ) + fixed = EnergyRouter( + n_experts=3, centers_init=[-1.0, 0.0, 1.0], learn_centers=False + ) + assert isinstance(learned.centers, torch.nn.Parameter) + assert not isinstance(fixed.centers, torch.nn.Parameter) + + +def test_build_router_threads_centers_init_through_energy_router(): + centers_init = [-1.5, -0.5, 0.5, 1.5] + router = build_router("energy", 4, centers_init=centers_init) + torch.testing.assert_close(router.centers, torch.tensor(centers_init)) + + def test_build_router_unknown_type_raises(): try: build_router("nonexistent", 4) From 4feb9007f3ea4c2b25a704c481d61a061534121e Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 13:14:49 +0200 Subject: [PATCH 18/24] docs: note the EnergyRouter centers_init fix in the roadmap Records both identified contributors to the 2026-07-22 router divergence (missing lambda_balance and the uniform-linspace center init) and that the latter is now fixed, with next steps covering both for the retrain. --- CLAUDE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CLAUDE.md b/CLAUDE.md index 7167339..27c19c2 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -86,7 +86,7 @@ GIANT is a conditional generative surrogate for the Geant4 step function. It rep **Faster-eval architectures (implemented, validation in progress):** both tracks below target a ~10× native-Geant4 eval budget and are now wired into `giant train`/`giant/model/network.py`, but neither has a validated result yet — treat both as unproven until the corresponding analysis run says otherwise: - **WGAN-GP** (`--mode wgan`, see Architecture above): implemented, **not yet tested** — no rollout-vs-reference analysis run against it yet. -- **MoE routing trunk** (`--router`, see Architecture above): implemented, **first rollout benchmark done (2026-07-22), result: needs retraining with a different router config, not abandoned.** A 10-expert `EnergyRouter` run (`n_experts=10`, `temperature=0.5`, `learn_centers=true`, **`lambda_balance=0.0`**, only 20 fine-tuning epochs resumed from a non-routed checkpoint) diverged badly from Geant4 on step granularity, secondary species, and shower shape, despite roughly matching bulk total deposited energy. The `router_gating` diagnostic plot points at the likely cause: the ten experts overlap heavily across ~5 decades of pre-step energy instead of partitioning it — even the top-energy expert only reaches ~60–65% gate weight at the highest energies plotted — so eval-time top-1 (Voronoi) dispatch is choosing among near-ties rather than real specialists. Leading suspect is the missing load-balancing loss (`lambda_balance=0.0`); the routing *strategy* itself may still be sound, but this specific config isn't. **Next step before further evaluation: retrain with `lambda_balance > 0` (and consider more epochs / a from-scratch run rather than a short fine-tune) and re-check whether `router_gating` sharpens up.** Full writeup: `/home/lars/knowledge-base/experiments/giant-router-energy-rollout-validation.md`. +- **MoE routing trunk** (`--router`, see Architecture above): implemented, **first rollout benchmark done (2026-07-22), result: needs retraining with a different router config, not abandoned.** A 10-expert `EnergyRouter` run (`n_experts=10`, `temperature=0.5`, `learn_centers=true`, **`lambda_balance=0.0`**, only 20 fine-tuning epochs resumed from a non-routed checkpoint) diverged badly from Geant4 on step granularity, secondary species, and shower shape, despite roughly matching bulk total deposited energy. The `router_gating` diagnostic plot points at the likely cause: the ten experts overlap heavily across ~5 decades of pre-step energy instead of partitioning it — even the top-energy expert only reaches ~60–65% gate weight at the highest energies plotted — so eval-time top-1 (Voronoi) dispatch is choosing among near-ties rather than real specialists. Two contributors were identified: the missing load-balancing loss (`lambda_balance=0.0`), and `EnergyRouter`'s center init (`torch.linspace(-2, 2, n_experts)`) assuming a roughly uniform z-normalized energy distribution, which real energy spectra don't match. **Fixed (2026-07-27):** `EnergyRouter` now accepts an optional `centers_init` (backward compatible — omitting it keeps the old linspace), and `giant train` auto-populates it from real data quantiles via a reservoir sample collected during the existing normalizer-fitting pass in `giant/pipeline.py` (no extra file scan), for `--router-type energy` only. The routing *strategy* itself may still be sound, but the specific benchmarked config wasn't. **Next step before further evaluation: retrain with `lambda_balance > 0` and the new quantile-seeded centers (and consider more epochs / a from-scratch run rather than a short fine-tune), then re-check whether `router_gating` sharpens up.** Full writeup: `/home/lars/knowledge-base/experiments/giant-router-energy-rollout-validation.md`. A sampling-calorimeter (multi-material) dataset is still a planned future direction, not yet built. See the knowledge base (`/home/lars/knowledge-base/meta/roadmap.md`). From b8a4dc7d63f45dece5c1424281d551df11c3e033 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 13:35:02 +0200 Subject: [PATCH 19/24] analyze: thread full model/training/rollout/dataset params to plots giant rollout now writes the checkpoint's complete model_config (incl. the router sub-dict), the sibling config.toml's [train]/[meta] sections, and every rollout CLI knob (weights, batch_size, escape_threshold, n_events, device, seed) into the YAML sidecar instead of a hand-picked subset. All of it flows through run_meta.json into each plot's own metadata.yaml for later comparison, while the figure subtitle itself shows a curated slice (hidden_dim, n_blocks, mode, conditioning, router, epoch, best_val_loss, steps/noise_dim) via new_figure's params option. Co-Authored-By: Claude Sonnet 5 --- giant/analysis/condor.py | 10 +++++++++ giant/analysis/render.py | 48 +++++++++++++++++++++++++++++++++++----- giant/cli.py | 28 ++++++++++++++++------- giant/config.py | 15 +++++++++++++ 4 files changed, 87 insertions(+), 14 deletions(-) diff --git a/giant/analysis/condor.py b/giant/analysis/condor.py index 039c6e9..60e1424 100644 --- a/giant/analysis/condor.py +++ b/giant/analysis/condor.py @@ -65,12 +65,22 @@ _PLOT_META_KEYS = ( "max_steps", "steps", "max_tracks_per_event", + "escape_threshold", + "n_events", "n_seed_events", "timestamp", "comment", + "weights", + "batch_size", + "device", + "rollout_seed", + "n_rows", + "termination_reason_counts", "model_config", "training_epoch", "best_val_loss", + "training_config", + "training_meta", ) diff --git a/giant/analysis/render.py b/giant/analysis/render.py index 76d2cd4..3941981 100644 --- a/giant/analysis/render.py +++ b/giant/analysis/render.py @@ -41,15 +41,46 @@ def _overlay(ax, edges: np.ndarray, series: dict[str, list], log_y: bool) -> Non ax.set_yscale("log") -def _nn_params(run_meta: dict) -> dict: - """Flatten the rollout's model/training provenance for the figure subtitle.""" - params = { - k: v for k, v in (run_meta.get("model_config") or {}).items() if v is not None - } +def _router_summary(model_config: dict) -> str: + r = model_config.get("router") or {} + if not r.get("enabled"): + return "off" + return f"{r.get('type', '?')}×{r.get('n_experts', '?')}" + + +def _figure_params(run_meta: dict) -> dict: + """Curated run identity for the figure subtitle (``new_figure(params=...)``). + + ``run_meta``/each plot's own ``.yaml`` (see ``_plot_metadata``) already + carry every threaded model/training/rollout/dataset parameter for + after-the-fact lookup — this picks only the handful that matter for + telling figures apart at a glance while flipping through a gallery, since + the subtitle is one unwrapped line of text. The last slot is + architecture-conditional: flow/ddpm runs show the ODE ``steps`` used for + this rollout, wgan runs show ``noise_dim`` instead since wgan sampling is + single-pass and has no ODE step count. + """ + mc = run_meta.get("model_config") or {} + mode = mc.get("mode") + params: dict = {} + if mc.get("hidden_dim") is not None: + params["hidden_dim"] = mc["hidden_dim"] + if mc.get("n_blocks") is not None: + params["n_blocks"] = mc["n_blocks"] + if mode is not None: + params["mode"] = mode + if mc.get("conditioning") is not None: + params["conditioning"] = mc["conditioning"] + params["router"] = _router_summary(mc) if run_meta.get("training_epoch") is not None: params["epoch"] = run_meta["training_epoch"] if run_meta.get("best_val_loss") is not None: params["best_val_loss"] = round(run_meta["best_val_loss"], 4) + if mode == "wgan": + if mc.get("noise_dim") is not None: + params["noise_dim"] = mc["noise_dim"] + elif run_meta.get("steps") is not None: + params["steps"] = run_meta["steps"] return params @@ -225,7 +256,7 @@ _RENDERERS = { def render(r: Reduced, run_meta: dict | None = None): """Build the matplotlib figure for one reduced artifact (dispatch on kind).""" - return _RENDERERS[r.kind](r, _nn_params(run_meta or {})) + return _RENDERERS[r.kind](r, _figure_params(run_meta or {})) def _plot_metadata(r: Reduced, run_meta: dict) -> dict: @@ -238,6 +269,11 @@ def _plot_metadata(r: Reduced, run_meta: dict) -> dict: meta.update(r.meta) if "note" in r.payload: meta["note"] = r.payload["note"] + if run_meta: + # Every threaded model/training/rollout/dataset parameter, so a + # single plot's metadata is self-contained for later comparison + # without cross-referencing the run's root metadata.yaml. + meta["parameters"] = {k: v for k, v in run_meta.items() if k != "title"} return meta diff --git a/giant/cli.py b/giant/cli.py index 511bad2..9ba234f 100644 --- a/giant/cli.py +++ b/giant/cli.py @@ -1024,6 +1024,9 @@ def rollout( ) raise typer.Exit(1) + gconfig.warn_if_checkpoint_config_mismatch(checkpoint) + training_cfg = gconfig.load_checkpoint_config(checkpoint) + model_cfg = ckpt["model_config"] conditioning = model_cfg.get("conditioning", "embedding") pdg_map = {int(k): v for k, v in ckpt["pdg_map"].items()} @@ -1104,17 +1107,26 @@ def rollout( "max_steps": max_steps, "steps": steps, "max_tracks_per_event": max_tracks_per_event, + "escape_threshold": escape_threshold, + "n_events": n_events, "n_seed_events": int(len(seeds["event_id"])), - "model_config": { - "mode": model_cfg.get("mode", "flow"), - "hidden_dim": model_cfg.get("hidden_dim"), - "n_blocks": model_cfg.get("n_blocks"), - "emb_dim": model_cfg.get("emb_dim"), - "dropout": model_cfg.get("dropout"), - "conditioning": conditioning, - }, + "weights": weights.value, + "batch_size": batch_size, + "device": str(_device), + "rollout_seed": seed, + "n_rows": summary["n_rows"], + "termination_reason_counts": summary["termination_reason_counts"], + # Full architecture spec baked into the checkpoint — includes the + # entire router sub-dict, not just a hand-picked subset, so any + # model knob (router type/n_experts, noise_dim, vocab sizes, ...) + # is available downstream without touching this command again. + "model_config": dict(model_cfg), "training_epoch": ckpt.get("epoch"), "best_val_loss": ckpt.get("best_val_loss"), + # [train]/[meta] from the sibling config.toml (giant.config.save_config) + # — empty dicts if the checkpoint has no config.toml next to it. + "training_config": dict(training_cfg.get("train", {})), + "training_meta": dict(training_cfg.get("meta", {})), } ) ref_path.write_text(yaml.dump(ref, default_flow_style=False, sort_keys=False)) diff --git a/giant/config.py b/giant/config.py index cfd7a1b..2de0df5 100644 --- a/giant/config.py +++ b/giant/config.py @@ -188,6 +188,21 @@ def warn_if_git_hash_mismatch(file_cfg: dict, config_path: Path) -> None: ) +def load_checkpoint_config(ckpt_path: str | Path) -> dict: + """Load the full ``[train]``/``[model]``/``[meta]`` config.toml written + alongside a checkpoint by ``save_config``. + + Returns ``{}`` if no config.toml sits next to the checkpoint (older runs, + or a checkpoint moved without its sidecar) — this is best-effort + provenance for threading into a rollout's YAML sidecar, not a hard + requirement for using the checkpoint itself. + """ + config_path = Path(ckpt_path).parent / "config.toml" + if not config_path.exists(): + return {} + return load_toml(config_path) + + def warn_if_checkpoint_config_mismatch(ckpt_path: str | Path) -> None: """Look for a config.toml next to a checkpoint and warn on a git_hash mismatch. From 08c76a9614a12d01cd1558cb6aa1d6ac17a667cf Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 14:00:59 +0200 Subject: [PATCH 20/24] ci: share one uv sync across jobs, gate tests on lint+type-check, sync tag/version on release tags MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Uploads the synced .venv as an artifact from a single setup job instead of re-running uv sync (and re-downloading torch) in every job. Drops the build/publish job in favor of a lighter job that, on a pushed tag, checks the tag against the uv project version and — if they differ — bumps the version, commits it to master, and recreates the tag on the new commit. --- .gitea/workflows/ci.yml | 80 +++++++++++++++++++++++++++++------------ 1 file changed, 57 insertions(+), 23 deletions(-) diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml index c2c0765..25534bf 100644 --- a/.gitea/workflows/ci.yml +++ b/.gitea/workflows/ci.yml @@ -3,12 +3,13 @@ name: CI "on": push: branches: ["**"] + tags: ["**"] pull_request: branches: [master] jobs: - ruff-check: - name: Lint (ruff check) + setup: + name: Setup environment runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -16,63 +17,96 @@ jobs: with: enable-cache: true - run: uv sync --extra cpu --extra dev + - run: tar -czf venv.tar.gz .venv + - uses: actions/upload-artifact@v4 + with: + name: venv + path: venv.tar.gz + + ruff-check: + name: Lint (ruff check) + needs: setup + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + - uses: actions/download-artifact@v4 + with: + name: venv + - run: tar -xzf venv.tar.gz - run: uv run ruff check . ruff-format: name: Format (ruff format) + needs: setup runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 with: enable-cache: true - - run: uv sync --extra cpu --extra dev + - uses: actions/download-artifact@v4 + with: + name: venv + - run: tar -xzf venv.tar.gz - run: uv run ruff format --check . type-check: name: Type check (ty) + needs: setup runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 with: enable-cache: true - - run: uv sync --extra cpu --extra dev + - uses: actions/download-artifact@v4 + with: + name: venv + - run: tar -xzf venv.tar.gz - run: uv run ty check . test: name: Tests + needs: [setup, ruff-check, type-check] runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 with: enable-cache: true - - run: uv sync --extra cpu --extra dev + - uses: actions/download-artifact@v4 + with: + name: venv + - run: tar -xzf venv.tar.gz - run: uv run pytest - build: - name: Bump version, build & publish wheel - needs: [ruff-check, ruff-format, type-check, test] - if: github.event_name == 'push' && github.ref == 'refs/heads/master' + sync-version-on-tag: + name: Sync project version with tag + if: startsWith(github.ref, 'refs/tags/') runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: token: ${{ secrets.CI_TOKEN }} - uses: astral-sh/setup-uv@v5 - - name: Bump patch version + - name: Check tag against project version, update if they differ run: | - git config user.name "gitea-actions" - git config user.email "actions@git.larsbogner.de" - uv version --bump patch --no-sync - NEW_VERSION=$(uv version --short) - git add pyproject.toml uv.lock - git commit -m "chore: bump version to ${NEW_VERSION} [skip ci]" - git push - - run: uv build - - name: Publish to Gitea package registry - env: - TWINE_USERNAME: ${{ secrets.PACKAGE_USERNAME }} - TWINE_PASSWORD: ${{ secrets.CI_TOKEN }} - run: uvx twine upload --repository-url https://git.larsbogner.de/api/packages/lars/pypi dist/* + TAG_VERSION="${GITHUB_REF_NAME#v}" + CURRENT_VERSION=$(uv version --short) + if [ "$TAG_VERSION" != "$CURRENT_VERSION" ]; then + echo "Tag version ($TAG_VERSION) != project version ($CURRENT_VERSION); updating pyproject.toml" + uv version "$TAG_VERSION" --no-sync + git config user.name "gitea-actions" + git config user.email "actions@git.larsbogner.de" + git add pyproject.toml uv.lock + git commit -m "chore: sync project version to tag ${GITHUB_REF_NAME} [skip ci]" + git push origin HEAD:master + git push origin ":refs/tags/${GITHUB_REF_NAME}" + git tag -f "${GITHUB_REF_NAME}" HEAD + git push origin "refs/tags/${GITHUB_REF_NAME}" + else + echo "Tag version matches project version ($CURRENT_VERSION)" + fi From 0afa75ee3017985faab7aaf64d124242e4752e6b Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 14:18:54 +0200 Subject: [PATCH 21/24] ci: replace unsupported artifact sharing with a bind-mounted uv cache The self-hosted act_runner doesn't support upload/download-artifact, so drop that plumbing and instead run jobs in an explicit container with a persistent host directory mounted at /uv-cache (UV_CACHE_DIR), backed by valid_volumes on the runner. uv sync still runs per job but hits a warm local cache instead of re-downloading/building packages every time. --- .gitea/workflows/ci.yml | 67 +++++++++++++++-------------------------- 1 file changed, 24 insertions(+), 43 deletions(-) diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml index 25534bf..f466acb 100644 --- a/.gitea/workflows/ci.yml +++ b/.gitea/workflows/ci.yml @@ -7,80 +7,61 @@ name: CI pull_request: branches: [master] -jobs: - setup: - name: Setup environment - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: astral-sh/setup-uv@v5 - with: - enable-cache: true - - run: uv sync --extra cpu --extra dev - - run: tar -czf venv.tar.gz .venv - - uses: actions/upload-artifact@v4 - with: - name: venv - path: venv.tar.gz +env: + UV_CACHE_DIR: /uv-cache +jobs: ruff-check: name: Lint (ruff check) - needs: setup runs-on: ubuntu-latest + container: + image: docker.gitea.com/runner-images:ubuntu-latest + volumes: + - /srv/act-runner-cache/uv:/uv-cache steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 - with: - enable-cache: true - - uses: actions/download-artifact@v4 - with: - name: venv - - run: tar -xzf venv.tar.gz + - run: uv sync --extra cpu --extra dev - run: uv run ruff check . ruff-format: name: Format (ruff format) - needs: setup runs-on: ubuntu-latest + container: + image: docker.gitea.com/runner-images:ubuntu-latest + volumes: + - /srv/act-runner-cache/uv:/uv-cache steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 - with: - enable-cache: true - - uses: actions/download-artifact@v4 - with: - name: venv - - run: tar -xzf venv.tar.gz + - run: uv sync --extra cpu --extra dev - run: uv run ruff format --check . type-check: name: Type check (ty) - needs: setup runs-on: ubuntu-latest + container: + image: docker.gitea.com/runner-images:ubuntu-latest + volumes: + - /srv/act-runner-cache/uv:/uv-cache steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 - with: - enable-cache: true - - uses: actions/download-artifact@v4 - with: - name: venv - - run: tar -xzf venv.tar.gz + - run: uv sync --extra cpu --extra dev - run: uv run ty check . test: name: Tests - needs: [setup, ruff-check, type-check] + needs: [ruff-check, type-check] runs-on: ubuntu-latest + container: + image: docker.gitea.com/runner-images:ubuntu-latest + volumes: + - /srv/act-runner-cache/uv:/uv-cache steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 - with: - enable-cache: true - - uses: actions/download-artifact@v4 - with: - name: venv - - run: tar -xzf venv.tar.gz + - run: uv sync --extra cpu --extra dev - run: uv run pytest sync-version-on-tag: From 1787d26d1c6cf0cddacef8052d3813b52dfe9908 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 14:22:49 +0200 Subject: [PATCH 22/24] ci: stop setup-uv from overriding UV_CACHE_DIR setup-uv's default enable-cache: auto sets its own UV_CACHE_DIR (a tool-cache tmp path) as a later step, clobbering the workflow-level UV_CACHE_DIR that points at the bind-mounted persistent cache. Disable setup-uv's own cache handling so our mount stays in effect. --- .gitea/workflows/ci.yml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml index f466acb..b42c4b2 100644 --- a/.gitea/workflows/ci.yml +++ b/.gitea/workflows/ci.yml @@ -21,6 +21,8 @@ jobs: steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 + with: + enable-cache: false - run: uv sync --extra cpu --extra dev - run: uv run ruff check . @@ -34,6 +36,8 @@ jobs: steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 + with: + enable-cache: false - run: uv sync --extra cpu --extra dev - run: uv run ruff format --check . @@ -47,6 +51,8 @@ jobs: steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 + with: + enable-cache: false - run: uv sync --extra cpu --extra dev - run: uv run ty check . @@ -61,6 +67,8 @@ jobs: steps: - uses: actions/checkout@v4 - uses: astral-sh/setup-uv@v5 + with: + enable-cache: false - run: uv sync --extra cpu --extra dev - run: uv run pytest From 19be455346d7ef68f8a5e987df9f7f1f2ac741c1 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 14:31:22 +0200 Subject: [PATCH 23/24] ci: re-pin UV_CACHE_DIR after setup-uv, which exports its own value regardless of enable-cache Log evidence showed setup-uv still sets UV_CACHE_DIR to a tool-cache tmp path (/tmp/setup-uv-cache) even with enable-cache: false, clobbering the workflow env pointing at the bind-mounted cache. Re-export it via GITHUB_ENV in a step right after setup-uv so it wins for the rest of the job. --- .gitea/workflows/ci.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml index b42c4b2..49b36e7 100644 --- a/.gitea/workflows/ci.yml +++ b/.gitea/workflows/ci.yml @@ -23,6 +23,7 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false + - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run ruff check . @@ -38,6 +39,7 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false + - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run ruff format --check . @@ -53,6 +55,7 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false + - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run ty check . @@ -69,6 +72,7 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false + - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run pytest From 0f7febfd021711a58d8d1b5ed84aac01976385c0 Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Mon, 27 Jul 2026 14:40:01 +0200 Subject: [PATCH 24/24] ci: set UV_LINK_MODE=copy to silence the cross-filesystem hardlink warning The bind-mounted uv cache and the job workspace are on different filesystems, so uv already falls back to copying installed files; this just tells it to do so directly instead of logging a hardlink-failed warning every job. --- .gitea/workflows/ci.yml | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/.gitea/workflows/ci.yml b/.gitea/workflows/ci.yml index 49b36e7..e69fc65 100644 --- a/.gitea/workflows/ci.yml +++ b/.gitea/workflows/ci.yml @@ -23,7 +23,9 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false - - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + - run: | + echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run ruff check . @@ -39,7 +41,9 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false - - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + - run: | + echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run ruff format --check . @@ -55,7 +59,9 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false - - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + - run: | + echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run ty check . @@ -72,7 +78,9 @@ jobs: - uses: astral-sh/setup-uv@v5 with: enable-cache: false - - run: echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + - run: | + echo "UV_CACHE_DIR=/uv-cache" >> "$GITHUB_ENV" + echo "UV_LINK_MODE=copy" >> "$GITHUB_ENV" - run: uv sync --extra cpu --extra dev - run: uv run pytest