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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"source": [
"!pip -q install chess pygame numpy torch matplotlib pandas"
],
"metadata": {
"id": "DAV6zrHmztwq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "43KPUdCMgdyR",
"outputId": "b6095333-3653-4823-bd03-383cf8a80ecf"
},
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Compiling JIT model for Tensor Core reduction...\n",
"\n",
"🚀 Optimized Pipeline | Envs: 256 | BZ: 8192 | Device: CUDA\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"W0421 02:50:31.332000 39915 torch/_inductor/utils.py:1679] [0/0] Not enough SMs to use max_autotune_gemm mode\n",
"/usr/local/lib/python3.12/dist-packages/torch/_inductor/select_algorithm.py:3464: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
" current_size = base.storage().size()\n",
"Autotune Choices Stats:\n",
"{\"num_choices\": 2, \"num_triton_choices\": 0, \"best_kernel\": \"bias_addmm\", \"best_time\": 0.4034560024738312}\n",
"AUTOTUNE addmm(256x5376, 256x2048, 2048x5376)\n",
"strides: [0, 1], [2048, 1], [1, 2048]\n",
"dtypes: torch.float16, torch.float16, torch.float16\n",
" bias_addmm 0.4035 ms 100.0% \n",
" addmm 0.4853 ms 83.1% \n",
"SingleProcess AUTOTUNE benchmarking takes 0.2749 seconds and 0.0003 seconds precompiling for 2 choices\n",
"Autotune Choices Stats:\n",
"{\"num_choices\": 2, \"num_triton_choices\": 0, \"best_kernel\": \"bias_addmm\", \"best_time\": 8.788448333740234}\n",
"AUTOTUNE addmm(8192x5376, 8192x2048, 2048x5376)\n",
"strides: [0, 1], [2048, 1], [1, 2048]\n",
"dtypes: torch.float16, torch.float16, torch.float16\n",
" bias_addmm 8.7884 ms 100.0% \n",
" addmm 9.4638 ms 92.9% \n",
"SingleProcess AUTOTUNE benchmarking takes 0.1625 seconds and 0.0004 seconds precompiling for 2 choices\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"[Iter 0001] P: 0.1286 | V: 0.0680 | V_Mean: 0.022 | Win%: 0.04 | FPS: 260\n",
"[Iter 0002] P: 0.2187 | V: 0.0789 | V_Mean: 0.052 | Win%: 0.09 | FPS: 2161\n",
"[Iter 0003] P: 0.2295 | V: 0.0712 | V_Mean: 0.077 | Win%: 0.15 | FPS: 2109\n",
"[Iter 0004] P: 0.2508 | V: 0.0857 | V_Mean: 0.104 | Win%: 0.21 | FPS: 2105\n",
"[Iter 0005] P: 0.2623 | V: 0.0932 | V_Mean: 0.124 | Win%: 0.27 | FPS: 2146\n",
"[Iter 0006] P: 0.2850 | V: 0.1089 | V_Mean: 0.158 | Win%: 0.33 | FPS: 2153\n",
"[Iter 0007] P: 0.2834 | V: 0.1107 | V_Mean: 0.167 | Win%: 0.37 | FPS: 2184\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"CUDAGraph supports dynamic shapes by recording a new graph for each distinct input size. Recording too many CUDAGraphs may lead to extra overhead. We have observed 9 distinct sizes. Please consider the following options for better performance: a) padding inputs to a few fixed number of shapes; or b) set torch._inductor.config.triton.cudagraph_skip_dynamic_graphs=True. Set torch._inductor.config.triton.cudagraph_dynamic_shape_warn_limit=None to silence this warning.\n",
"CUDAGraph supports dynamic shapes by recording a new graph for each distinct input size. Recording too many CUDAGraphs may lead to extra overhead. We have observed 9 distinct sizes. Please consider the following options for better performance: a) padding inputs to a few fixed number of shapes; or b) set torch._inductor.config.triton.cudagraph_skip_dynamic_graphs=True. Set torch._inductor.config.triton.cudagraph_dynamic_shape_warn_limit=None to silence this warning.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"[Iter 0008] P: 0.3000 | V: 0.1349 | V_Mean: 0.208 | Win%: 0.45 | FPS: 2042\n",
"[Iter 0009] P: 0.3013 | V: 0.1323 | V_Mean: 0.221 | Win%: 0.50 | FPS: 2040\n",
"[Iter 0010] P: 0.3028 | V: 0.1476 | V_Mean: 0.244 | Win%: 0.54 | FPS: 2153\n",
"[Iter 0011] P: 0.2981 | V: 0.1452 | V_Mean: 0.251 | Win%: 0.58 | FPS: 2124\n",
"[Iter 0012] P: 0.3019 | V: 0.1464 | V_Mean: 0.269 | Win%: 0.62 | FPS: 2126\n",
"[Iter 0013] P: 0.3031 | V: 0.1572 | V_Mean: 0.304 | Win%: 0.70 | FPS: 2075\n",
"[Iter 0014] P: 0.2985 | V: 0.1593 | V_Mean: 0.323 | Win%: 0.76 | FPS: 1984\n",
"[Iter 0015] P: 0.3137 | V: 0.1541 | V_Mean: 0.346 | Win%: 0.82 | FPS: 2111\n",
"[Iter 0016] P: 0.3117 | V: 0.1767 | V_Mean: 0.352 | Win%: 0.88 | FPS: 2117\n",
"[Iter 0017] P: 0.3176 | V: 0.1587 | V_Mean: 0.376 | Win%: 0.99 | FPS: 2062\n",
"[Iter 0018] P: 0.2984 | V: 0.1580 | V_Mean: 0.392 | Win%: 1.05 | FPS: 2038\n",
"[Iter 0019] P: 0.2929 | V: 0.1633 | V_Mean: 0.407 | Win%: 1.11 | FPS: 2102\n",
"[Iter 0020] P: 0.2834 | V: 0.1586 | V_Mean: 0.422 | Win%: 1.17 | FPS: 2098\n",
"[Iter 0021] P: 0.2636 | V: 0.1636 | V_Mean: 0.437 | Win%: 1.24 | FPS: 2080\n",
"[Iter 0022] P: 0.2738 | V: 0.1558 | V_Mean: 0.455 | Win%: 1.30 | FPS: 2009\n",
"[Iter 0023] P: 0.2464 | V: 0.1615 | V_Mean: 0.465 | Win%: 1.34 | FPS: 2066\n",
"[Iter 0024] P: 0.2225 | V: 0.1590 | V_Mean: 0.472 | Win%: 1.37 | FPS: 2124\n",
"[Iter 0025] P: 0.1825 | V: 0.1552 | V_Mean: 0.465 | Win%: 1.38 | FPS: 2089\n",
"[Iter 0026] P: 0.1481 | V: 0.1455 | V_Mean: 0.470 | Win%: 1.38 | FPS: 2041\n",
"[Iter 0027] P: 0.1393 | V: 0.1555 | V_Mean: 0.467 | Win%: 1.38 | FPS: 2054\n",
"[Iter 0028] P: 0.1221 | V: 0.1437 | V_Mean: 0.463 | Win%: 1.39 | FPS: 2085\n",
"[Iter 0029] P: 0.1266 | V: 0.1563 | V_Mean: 0.470 | Win%: 1.41 | FPS: 2020\n",
"[Iter 0030] P: 0.1303 | V: 0.1570 | V_Mean: 0.469 | Win%: 1.41 | FPS: 2051\n",
"[Iter 0031] P: 0.1044 | V: 0.1498 | V_Mean: 0.469 | Win%: 1.42 | FPS: 2091\n",
"[Iter 0032] P: 0.1057 | V: 0.1553 | V_Mean: 0.470 | Win%: 1.42 | FPS: 2140\n",
"[Iter 0033] P: 0.0951 | V: 0.1499 | V_Mean: 0.466 | Win%: 1.43 | FPS: 2140\n",
"[Iter 0034] P: 0.0978 | V: 0.1557 | V_Mean: 0.465 | Win%: 1.43 | FPS: 2017\n",
"[Iter 0035] P: 0.0871 | V: 0.1510 | V_Mean: 0.464 | Win%: 1.43 | FPS: 2059\n",
"[Iter 0036] P: 0.0862 | V: 0.1585 | V_Mean: 0.464 | Win%: 1.43 | FPS: 2195\n",
"[Iter 0037] P: 0.0885 | V: 0.1616 | V_Mean: 0.462 | Win%: 1.44 | FPS: 2146\n",
"[Iter 0038] P: 0.0935 | V: 0.1661 | V_Mean: 0.462 | Win%: 1.44 | FPS: 2263\n",
"[Iter 0039] P: 0.0738 | V: 0.1652 | V_Mean: 0.462 | Win%: 1.44 | FPS: 2044\n",
"[Iter 0040] P: 0.0709 | V: 0.1533 | V_Mean: 0.463 | Win%: 1.45 | FPS: 2123\n",
"[Iter 0041] P: 0.0790 | V: 0.1694 | V_Mean: 0.461 | Win%: 1.45 | FPS: 2068\n",
"[Iter 0042] P: 0.0785 | V: 0.1665 | V_Mean: 0.462 | Win%: 1.45 | FPS: 2194\n",
"[Iter 0043] P: 0.0702 | V: 0.1611 | V_Mean: 0.462 | Win%: 1.45 | FPS: 2064\n",
"[Iter 0044] P: 0.0843 | V: 0.1657 | V_Mean: 0.462 | Win%: 1.46 | FPS: 2053\n",
"[Iter 0045] P: 0.0843 | V: 0.1642 | V_Mean: 0.462 | Win%: 1.47 | FPS: 2094\n",
"[Iter 0046] P: 0.1080 | V: 0.1737 | V_Mean: 0.464 | Win%: 1.47 | FPS: 2142\n",
"[Iter 0047] P: 0.0711 | V: 0.1611 | V_Mean: 0.461 | Win%: 1.47 | FPS: 2063\n",
"[Iter 0048] P: 0.0578 | V: 0.1708 | V_Mean: 0.462 | Win%: 1.47 | FPS: 2096\n",
"[Iter 0049] P: 0.0475 | V: 0.1717 | V_Mean: 0.461 | Win%: 1.48 | FPS: 2218\n",
"[Iter 0050] P: 0.0519 | V: 0.1686 | V_Mean: 0.461 | Win%: 1.48 | FPS: 2159\n",
"[Iter 0051] P: 0.0469 | V: 0.1664 | V_Mean: 0.465 | Win%: 1.48 | FPS: 2237\n",
"[Iter 0052] P: 0.0601 | V: 0.1657 | V_Mean: 0.465 | Win%: 1.49 | FPS: 2017\n",
"[Iter 0053] P: 0.0508 | V: 0.1662 | V_Mean: 0.464 | Win%: 1.49 | FPS: 2101\n",
"[Iter 0054] P: 0.0509 | V: 0.1744 | V_Mean: 0.466 | Win%: 1.49 | FPS: 2147\n",
"[Iter 0055] P: 0.0572 | V: 0.1731 | V_Mean: 0.465 | Win%: 1.49 | FPS: 2216\n",
"[Iter 0056] P: 0.0658 | V: 0.1690 | V_Mean: 0.465 | Win%: 1.50 | FPS: 2040\n",
"[Iter 0057] P: 0.0747 | V: 0.1656 | V_Mean: 0.467 | Win%: 1.50 | FPS: 2028\n",
"[Iter 0058] P: 0.0716 | V: 0.1629 | V_Mean: 0.468 | Win%: 1.50 | FPS: 2443\n",
"[Iter 0059] P: 0.0899 | V: 0.1675 | V_Mean: 0.467 | Win%: 1.51 | FPS: 2088\n",
"[Iter 0060] P: 0.0715 | V: 0.1681 | V_Mean: 0.470 | Win%: 1.52 | FPS: 2086\n",
"[Iter 0061] P: 0.0632 | V: 0.1708 | V_Mean: 0.470 | Win%: 1.52 | FPS: 1988\n",
"[Iter 0062] P: 0.0448 | V: 0.1645 | V_Mean: 0.470 | Win%: 1.53 | FPS: 2322\n",
"[Iter 0063] P: 0.0616 | V: 0.1659 | V_Mean: 0.470 | Win%: 1.54 | FPS: 2115\n",
"[Iter 0064] P: 0.0617 | V: 0.1574 | V_Mean: 0.472 | Win%: 1.55 | FPS: 2031\n",
"[Iter 0065] P: 0.0685 | V: 0.1673 | V_Mean: 0.472 | Win%: 1.56 | FPS: 2006\n",
"[Iter 0066] P: 0.0879 | V: 0.1718 | V_Mean: 0.478 | Win%: 1.57 | FPS: 2105\n",
"[Iter 0067] P: 0.0512 | V: 0.1713 | V_Mean: 0.477 | Win%: 1.58 | FPS: 2080\n",
"[Iter 0068] P: 0.0420 | V: 0.1658 | V_Mean: 0.480 | Win%: 1.59 | FPS: 2047\n",
"[Iter 0069] P: 0.0491 | V: 0.1746 | V_Mean: 0.477 | Win%: 1.60 | FPS: 2051\n",
"[Iter 0070] P: 0.0446 | V: 0.1726 | V_Mean: 0.480 | Win%: 1.61 | FPS: 2110\n",
"[Iter 0071] P: 0.0578 | V: 0.1723 | V_Mean: 0.479 | Win%: 1.61 | FPS: 2336\n",
"[Iter 0072] P: 0.0640 | V: 0.1782 | V_Mean: 0.478 | Win%: 1.61 | FPS: 2390\n",
"[Iter 0073] P: 0.0538 | V: 0.1642 | V_Mean: 0.478 | Win%: 1.61 | FPS: 2090\n",
"[Iter 0074] P: 0.0606 | V: 0.1565 | V_Mean: 0.478 | Win%: 1.61 | FPS: 1965\n",
"[Iter 0075] P: 0.0765 | V: 0.1714 | V_Mean: 0.481 | Win%: 1.62 | FPS: 2292\n",
"[Iter 0076] P: 0.0741 | V: 0.1734 | V_Mean: 0.480 | Win%: 1.62 | FPS: 2147\n",
"[Iter 0077] P: 0.0552 | V: 0.1661 | V_Mean: 0.477 | Win%: 1.63 | FPS: 2432\n",
"[Iter 0078] P: 0.0394 | V: 0.1631 | V_Mean: 0.475 | Win%: 1.64 | FPS: 2068\n",
"[Iter 0079] P: 0.0580 | V: 0.1743 | V_Mean: 0.477 | Win%: 1.65 | FPS: 1951\n",
"[Iter 0080] P: 0.0578 | V: 0.1717 | V_Mean: 0.477 | Win%: 1.65 | FPS: 2240\n",
"[Iter 0081] P: 0.0589 | V: 0.1668 | V_Mean: 0.477 | Win%: 1.66 | FPS: 2130\n",
"[Iter 0082] P: 0.0610 | V: 0.1791 | V_Mean: 0.479 | Win%: 1.67 | FPS: 2139\n",
"[Iter 0083] P: 0.0680 | V: 0.1686 | V_Mean: 0.478 | Win%: 1.67 | FPS: 2225\n",
"[Iter 0084] P: 0.0686 | V: 0.1749 | V_Mean: 0.478 | Win%: 1.68 | FPS: 2085\n",
"[Iter 0085] P: 0.0647 | V: 0.1701 | V_Mean: 0.479 | Win%: 1.68 | FPS: 2275\n",
"[Iter 0086] P: 0.0759 | V: 0.1697 | V_Mean: 0.478 | Win%: 1.68 | FPS: 2202\n",
"[Iter 0087] P: 0.0674 | V: 0.1715 | V_Mean: 0.479 | Win%: 1.68 | FPS: 2114\n",
"[Iter 0088] P: 0.0643 | V: 0.1571 | V_Mean: 0.478 | Win%: 1.69 | FPS: 2039\n",
"[Iter 0089] P: 0.0943 | V: 0.1723 | V_Mean: 0.482 | Win%: 1.70 | FPS: 2065\n",
"[Iter 0090] P: 0.0731 | V: 0.1765 | V_Mean: 0.481 | Win%: 1.70 | FPS: 2255\n",
"[Iter 0091] P: 0.0513 | V: 0.1742 | V_Mean: 0.481 | Win%: 1.70 | FPS: 2539\n",
"[Iter 0092] P: 0.0365 | V: 0.1763 | V_Mean: 0.478 | Win%: 1.71 | FPS: 2235\n",
"[Iter 0093] P: 0.0482 | V: 0.1748 | V_Mean: 0.481 | Win%: 1.72 | FPS: 2224\n",
"[Iter 0094] P: 0.0473 | V: 0.1725 | V_Mean: 0.483 | Win%: 1.72 | FPS: 2367\n",
"[Iter 0095] P: 0.0444 | V: 0.1706 | V_Mean: 0.484 | Win%: 1.73 | FPS: 2155\n",
"[Iter 0096] P: 0.0426 | V: 0.1665 | V_Mean: 0.483 | Win%: 1.73 | FPS: 2360\n",
"[Iter 0097] P: 0.0509 | V: 0.1737 | V_Mean: 0.483 | Win%: 1.73 | FPS: 2372\n",
"[Iter 0098] P: 0.0545 | V: 0.1708 | V_Mean: 0.482 | Win%: 1.74 | FPS: 2247\n",
"[Iter 0099] P: 0.0633 | V: 0.1774 | V_Mean: 0.486 | Win%: 1.75 | FPS: 2227\n",
"[Iter 0100] P: 0.0608 | V: 0.1698 | V_Mean: 0.490 | Win%: 1.76 | FPS: 2245\n",
"[Iter 0101] P: 0.0511 | V: 0.1672 | V_Mean: 0.488 | Win%: 1.77 | FPS: 2175\n",
"[Iter 0102] P: 0.0476 | V: 0.1688 | V_Mean: 0.488 | Win%: 1.77 | FPS: 2186\n",
"[Iter 0103] P: 0.0571 | V: 0.1702 | V_Mean: 0.489 | Win%: 1.77 | FPS: 2469\n",
"[Iter 0104] P: 0.0621 | V: 0.1689 | V_Mean: 0.488 | Win%: 1.78 | FPS: 2281\n",
"[Iter 0105] P: 0.0579 | V: 0.1773 | V_Mean: 0.494 | Win%: 1.79 | FPS: 2339\n",
"[Iter 0106] P: 0.0578 | V: 0.1714 | V_Mean: 0.497 | Win%: 1.80 | FPS: 2331\n",
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]
}
],
"source": [
"# NOTE FOR COLAB USERS: Run the following line in a separate cell before running this script:\n",
"# !pip -q install chess numpy torch matplotlib pandas\n",
"\n",
"\"\"\"\n",
"Hyper-Optimized GRPO Chess Agent – T4/Colab Targeted\n",
"Engineered for strict 14GB VRAM limit, maximized Tensor Core utilization, and minimal CPU-GPU latency.\n",
"\n",
"Architectural Enhancements:\n",
"- FP16 Stable Masking (-60000.0 instead of -1e9)\n",
"- Fused AdamW Kernels & Channels Last Memory Format\n",
"- TF32 Matmul Precision Enabled\n",
"- Int8 GPU Buffer Allocation (Reduces VRAM bandwidth by 75% during rollouts)\n",
"- GPU-Accelerated Advantage Normalization & Return Calculation\n",
"\"\"\"\n",
"\n",
"import os\n",
"import sys\n",
"import csv\n",
"import time\n",
"import argparse\n",
"import random\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib\n",
"matplotlib.use('Agg')\n",
"import matplotlib.pyplot as plt\n",
"\n",
"try:\n",
" import chess\n",
"except ImportError:\n",
" os.system(\"pip install -q chess\")\n",
" import chess\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"# ----------------------------------------------------------------------\n",
"# Core High-Performance Flags\n",
"# ----------------------------------------------------------------------\n",
"torch.backends.cudnn.benchmark = True\n",
"torch.backends.cuda.matmul.allow_tf32 = True\n",
"torch.backends.cudnn.allow_tf32 = True\n",
"if hasattr(torch, 'set_float32_matmul_precision'):\n",
" torch.set_float32_matmul_precision('high')\n",
"\n",
"CONFIG = {\n",
" \"num_envs\": 256, # Maximize batch size for GPU starvation\n",
" \"grpo_group_size\": 8,\n",
" \"ppo_epochs\": 4,\n",
" \"mini_batch_size\": 8192, # Pushed to limits for T4 VRAM throughput\n",
" \"learning_rate\": 3e-4,\n",
" \"weight_decay\": 1e-4,\n",
" \"gamma\": 0.995,\n",
" \"clip_epsilon\": 0.2,\n",
" \"entropy_coef\": 0.05,\n",
" \"value_coef\": 0.5,\n",
" \"max_steps\": 128, # Power of 2 for better memory alignment\n",
" \"checkpoint_dir\": \"./checkpoints\",\n",
" \"save_interval\": 50,\n",
" \"log_interval\": 1,\n",
" \"device\": \"cuda\" if torch.cuda.is_available() else \"cpu\",\n",
" \"seed\": 42,\n",
"}\n",
"\n",
"# ----------------------------------------------------------------------\n",
"# Action Space Mapper (Optimized Lookup)\n",
"# ----------------------------------------------------------------------\n",
"class ActionMapper:\n",
" __slots__ = ['move_to_idx', 'idx_to_move', 'num_actions']\n",
" def __init__(self):\n",
" self.move_to_idx = {}\n",
" self.idx_to_move = []\n",
" idx = 0\n",
" for f in range(64):\n",
" for t in range(64):\n",
" if f == t: continue\n",
" uci = chess.SQUARE_NAMES[f] + chess.SQUARE_NAMES[t]\n",
" self.move_to_idx[uci] = idx\n",
" self.idx_to_move.append(uci)\n",
" idx += 1\n",
" if chess.square_rank(f) in (1, 6) and abs(chess.square_file(f) - chess.square_file(t)) <= 1:\n",
" for promo in \"nbrq\":\n",
" promo_uci = uci + promo\n",
" self.move_to_idx[promo_uci] = idx\n",
" self.idx_to_move.append(promo_uci)\n",
" idx += 1\n",
" self.num_actions = idx\n",
"\n",
"ACTION_MAPPER = ActionMapper()\n",
"\n",
"# ----------------------------------------------------------------------\n",
"# Ultra-Fast CPU Vectorization\n",
"# ----------------------------------------------------------------------\n",
"def populate_states_fast(envs, active_mask, bbs_np, meta_np):\n",
" \"\"\"Direct attribute access to bypass function call overhead.\"\"\"\n",
" for b in range(len(envs)):\n",
" if not active_mask[b]: continue\n",
" env = envs[b]\n",
" w = env.occupied_co[chess.WHITE]\n",
" bc = env.occupied_co[chess.BLACK]\n",
"\n",
" bbs_np[b, 0] = env.pawns & w\n",
" bbs_np[b, 1] = env.knights & w\n",
" bbs_np[b, 2] = env.bishops & w\n",
" bbs_np[b, 3] = env.rooks & w\n",
" bbs_np[b, 4] = env.queens & w\n",
" bbs_np[b, 5] = env.kings & w\n",
" bbs_np[b, 6] = env.pawns & bc\n",
" bbs_np[b, 7] = env.knights & bc\n",
" bbs_np[b, 8] = env.bishops & bc\n",
" bbs_np[b, 9] = env.rooks & bc\n",
" bbs_np[b, 10] = env.queens & bc\n",
" bbs_np[b, 11] = env.kings & bc\n",
"\n",
" meta_np[b, 0] = 1.0 if env.turn else -1.0\n",
" meta_np[b, 1] = env.castling_rights * 0.1333333 - 1.0 # (x/15)*2-1 optimized\n",
" meta_np[b, 2] = 1.0 if env.ep_square else -1.0\n",
"\n",
"def get_legal_masks(envs, active_mask) -> tuple[np.ndarray, list[list[chess.Move]]]:\n",
" masks = np.zeros((len(envs), ACTION_MAPPER.num_actions), dtype=np.bool_)\n",
" moves_list = [None] * len(envs)\n",
" for b in range(len(envs)):\n",
" if not active_mask[b]: continue\n",
" legal = list(envs[b].legal_moves)\n",
" moves_list[b] = legal\n",
" for m in legal:\n",
" masks[b, ACTION_MAPPER.move_to_idx[m.uci()]] = True\n",
" return masks, moves_list\n",
"\n",
"# ----------------------------------------------------------------------\n",
"# Neural Network – ResNet (Channels Last Optimized)\n",
"# ----------------------------------------------------------------------\n",
"class ChessNet(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.conv_in = nn.Conv2d(14, 128, kernel_size=3, padding=1, bias=False)\n",
" self.bn_in = nn.BatchNorm2d(128)\n",
"\n",
" self.res_blocks = nn.ModuleList([\n",
" nn.Sequential(\n",
" nn.Conv2d(128, 128, 3, padding=1, bias=False),\n",
" nn.BatchNorm2d(128),\n",
" nn.ReLU(inplace=True),\n",
" nn.Conv2d(128, 128, 3, padding=1, bias=False),\n",
" nn.BatchNorm2d(128)\n",
" ) for _ in range(6)\n",
" ])\n",
"\n",
" self.policy_head = nn.Sequential(\n",
" nn.Conv2d(128, 32, 1, bias=False),\n",
" nn.BatchNorm2d(32),\n",
" nn.ReLU(inplace=True),\n",
" nn.Flatten(),\n",
" nn.Linear(32 * 8 * 8, ACTION_MAPPER.num_actions)\n",
" )\n",
"\n",
" self.value_head = nn.Sequential(\n",
" nn.Conv2d(128, 32, 1, bias=False),\n",
" nn.BatchNorm2d(32),\n",
" nn.ReLU(inplace=True),\n",
" nn.Flatten(),\n",
" nn.Linear(32 * 8 * 8, 256),\n",
" nn.ReLU(inplace=True),\n",
" nn.Linear(256, 1),\n",
" nn.Tanh()\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(self.bn_in(self.conv_in(x)), inplace=True)\n",
" for block in self.res_blocks:\n",
" x = F.relu(x + block(x), inplace=True)\n",
" return self.policy_head(x), self.value_head(x)\n",
"\n",
"# ----------------------------------------------------------------------\n",
"# Optimized GRPO Trainer\n",
"# ----------------------------------------------------------------------\n",
"class GRPOTrainer:\n",
" def __init__(self):\n",
" self.device = CONFIG[\"device\"]\n",
"\n",
" _model = ChessNet().to(self.device).to(memory_format=torch.channels_last)\n",
" try:\n",
" print(\"Compiling JIT model for Tensor Core reduction...\")\n",
" self.model = torch.compile(_model, mode=\"max-autotune\")\n",
" except Exception:\n",
" self.model = _model\n",
"\n",
" # Fused AdamW prevents host-to-device kernel launch overheads\n",
" self.optimizer = torch.optim.AdamW(\n",
" self.model.parameters(),\n",
" lr=CONFIG[\"learning_rate\"],\n",
" weight_decay=CONFIG[\"weight_decay\"],\n",
" fused=True if torch.cuda.is_available() else False\n",
" )\n",
" self.scaler = torch.amp.GradScaler('cuda')\n",
" self.start_iter = 0\n",
"\n",
" self.shifts = torch.arange(64, dtype=torch.int64, device=self.device).view(1, 1, 64)\n",
"\n",
" os.makedirs(CONFIG[\"checkpoint_dir\"], exist_ok=True)\n",
" self.log_file = os.path.join(CONFIG[\"checkpoint_dir\"], \"training_log.csv\")\n",
"\n",
" if not os.path.exists(self.log_file):\n",
" with open(self.log_file, \"w\", newline=\"\") as f:\n",
" csv.writer(f).writerow([\"iteration\", \"p_loss\", \"v_loss\", \"v_mean\", \"fps\", \"win_rate\", \"draw_rate\"])\n",
"\n",
" self._init_checkpointing()\n",
"\n",
" def _init_checkpointing(self):\n",
" latest = os.path.join(CONFIG[\"checkpoint_dir\"], \"latest.pt\")\n",
" if os.path.exists(latest):\n",
" checkpoint = torch.load(latest, map_location=self.device)\n",
" self.model.load_state_dict(checkpoint['model_state_dict'])\n",
" self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
" self.scaler.load_state_dict(checkpoint['scaler_state_dict'])\n",
" self.start_iter = checkpoint['iteration'] + 1\n",
"\n",
" def save_checkpoint(self, iteration: int):\n",
" path = os.path.join(CONFIG[\"checkpoint_dir\"], f\"iter_{iteration:04d}.pt\")\n",
" torch.save({\n",
" 'iteration': iteration,\n",
" 'model_state_dict': self.model.state_dict(),\n",
" 'optimizer_state_dict': self.optimizer.state_dict(),\n",
" 'scaler_state_dict': self.scaler.state_dict(),\n",
" }, path)\n",
" latest = os.path.join(CONFIG[\"checkpoint_dir\"], \"latest.pt\")\n",
" if os.path.exists(latest): os.remove(latest)\n",
" os.symlink(os.path.basename(path), latest)\n",
"\n",
" def train(self, num_iterations: int):\n",
" B = CONFIG[\"num_envs\"]\n",
" max_steps = CONFIG[\"max_steps\"]\n",
"\n",
" # Buffer Allocation (int8/bool used heavily to minimize VRAM footprint)\n",
" states_buf = torch.zeros((max_steps, B, 14, 8, 8), dtype=torch.int8, device=self.device)\n",
" actions_buf = torch.zeros((max_steps, B), dtype=torch.int16, device=self.device)\n",
" logprobs_buf = torch.zeros((max_steps, B), dtype=torch.float32, device=self.device)\n",
" values_buf = torch.zeros((max_steps, B), dtype=torch.float32, device=self.device)\n",
" rewards_buf = torch.zeros((max_steps, B), dtype=torch.float32, device=self.device)\n",
" dones_buf = torch.zeros((max_steps, B), dtype=torch.bool, device=self.device)\n",
" active_buf = torch.zeros((max_steps, B), dtype=torch.bool, device=self.device)\n",
"\n",
" bbs_np = np.zeros((B, 12), dtype=np.uint64)\n",
" meta_np = np.zeros((B, 3), dtype=np.float32)\n",
"\n",
" print(f\"\\n🚀 Optimized Pipeline | Envs: {B} | BZ: {CONFIG['mini_batch_size']} | Device: {self.device.upper()}\")\n",
"\n",
" for iteration in range(self.start_iter, num_iterations):\n",
" iter_start = time.time()\n",
" envs = [chess.Board() for _ in range(B)]\n",
" active = np.ones(B, dtype=bool)\n",
"\n",
" # --- PHASE 1: VECTORIZED ROLLOUT ---\n",
" for t in range(max_steps):\n",
" if not active.any(): break\n",
"\n",
" populate_states_fast(envs, active, bbs_np, meta_np)\n",
"\n",
" bbs_t = torch.as_tensor(bbs_np.astype(np.int64), dtype=torch.int64, device=self.device)\n",
" unpacked_bits = ((bbs_t.unsqueeze(-1) >> self.shifts) & 1).to(torch.int8)\n",
" meta_t = torch.as_tensor(meta_np, dtype=torch.int8, device=self.device)\n",
"\n",
" states_buf[t, :, :12, :, :] = unpacked_bits.view(B, 12, 8, 8)\n",
" states_buf[t, :, 12, :, :] = meta_t[:, 0].view(B, 1, 1).expand(B, 8, 8)\n",
" states_buf[t, :, 13, :, :] = meta_t[:, 1].view(B, 1, 1).expand(B, 8, 8)\n",
" states_buf[t, :, 13, 0, 1] = meta_t[:, 2]\n",
"\n",
" active_buf[t] = torch.as_tensor(active, dtype=torch.bool, device=self.device)\n",
"\n",
" # Expand states to float32 only for forward pass\n",
" model_input = states_buf[t].to(dtype=torch.float32, memory_format=torch.channels_last)\n",
"\n",
" self.model.eval()\n",
" with torch.no_grad(), torch.amp.autocast('cuda'):\n",
" logits, values = self.model(model_input)\n",
"\n",
" masks_np, legal_moves_list = get_legal_masks(envs, active)\n",
" masks_t = torch.as_tensor(masks_np, dtype=torch.bool, device=self.device)\n",
"\n",
" # Float16 safe clamping\n",
" logits = logits.to(torch.float32)\n",
" logits = torch.where(masks_t, logits, torch.tensor(-60000.0, device=self.device))\n",
"\n",
" # Fix for environments with 0 legal moves (checkmate scenarios caught before break)\n",
" is_all_zero = (~masks_t.any(dim=-1, keepdim=True))\n",
" logits.masked_fill_(is_all_zero, 0.0)\n",
"\n",
" probs = F.softmax(logits, dim=-1)\n",
" dist = torch.distributions.Categorical(probs)\n",
" actions = dist.sample()\n",
"\n",
" actions_buf[t] = actions.to(torch.int16)\n",
" logprobs_buf[t] = dist.log_prob(actions)\n",
" values_buf[t] = values.squeeze(-1)\n",
"\n",
" actions_cpu = actions.cpu().numpy()\n",
"\n",
" for b in range(B):\n",
" if not active[b]: continue\n",
"\n",
" move_uci = ACTION_MAPPER.idx_to_move[actions_cpu[b]]\n",
" move = chess.Move.from_uci(move_uci)\n",
"\n",
" if move not in legal_moves_list[b]:\n",
" move = random.choice(legal_moves_list[b])\n",
"\n",
" envs[b].push(move)\n",
"\n",
" if envs[b].is_game_over():\n",
" res = envs[b].result()\n",
" term_reward = 1.0 if res == \"1-0\" else (-1.0 if res == \"0-1\" else 0.0)\n",
" rewards_buf[t, b] = term_reward if envs[b].turn == chess.BLACK else -term_reward\n",
" dones_buf[t, b] = True\n",
" active[b] = False\n",
"\n",
" # --- PHASE 2: PARALLELIZED RETURNS ---\n",
" returns = torch.zeros(B, dtype=torch.float32, device=self.device)\n",
" returns_buf = torch.zeros_like(rewards_buf)\n",
"\n",
" for step in reversed(range(max_steps)):\n",
" returns = rewards_buf[step] + CONFIG[\"gamma\"] * returns * (~dones_buf[step]).float()\n",
" returns_buf[step] = returns * active_buf[step].float()\n",
"\n",
" valid_mask = active_buf.view(-1)\n",
" flat_states = states_buf.view(-1, 14, 8, 8)[valid_mask].to(torch.float32, memory_format=torch.channels_last)\n",
" flat_actions = actions_buf.view(-1)[valid_mask].to(torch.int64)\n",
" flat_old_logprobs = logprobs_buf.view(-1)[valid_mask]\n",
" flat_returns = returns_buf.view(-1)[valid_mask]\n",
" flat_values = values_buf.view(-1)[valid_mask]\n",
"\n",
" dataset_size = flat_states.size(0)\n",
" if dataset_size < 100: continue # Skip degenerate rollouts\n",
"\n",
" flat_advantages = flat_returns - flat_values\n",
" flat_advantages = (flat_advantages - flat_advantages.mean()) / (flat_advantages.std() + 1e-8)\n",
"\n",
" # --- PHASE 3: PPO OPTIMIZATION ---\n",
" self.model.train()\n",
" total_policy_loss, total_value_loss = 0.0, 0.0\n",
" mb_size = CONFIG[\"mini_batch_size\"]\n",
" num_updates = 0\n",
"\n",
" for _ in range(CONFIG[\"ppo_epochs\"]):\n",
" indices = torch.randperm(dataset_size, device=self.device)\n",
"\n",
" for start in range(0, dataset_size, mb_size):\n",
" end = min(start + mb_size, dataset_size)\n",
" mb_idx = indices[start:end]\n",
"\n",
" with torch.amp.autocast('cuda'):\n",
" new_logits, new_values = self.model(flat_states[mb_idx])\n",
" new_dist = torch.distributions.Categorical(logits=new_logits)\n",
" new_log_probs = new_dist.log_prob(flat_actions[mb_idx])\n",
"\n",
" ratio = torch.exp(new_log_probs - flat_old_logprobs[mb_idx])\n",
" mb_adv = flat_advantages[mb_idx]\n",
"\n",
" surr1 = ratio * mb_adv\n",
" surr2 = torch.clamp(ratio, 1.0 - CONFIG[\"clip_epsilon\"], 1.0 + CONFIG[\"clip_epsilon\"]) * mb_adv\n",
" policy_loss = -torch.min(surr1, surr2).mean()\n",
"\n",
" value_loss = F.mse_loss(new_values.squeeze(-1), flat_returns[mb_idx])\n",
" entropy = new_dist.entropy().mean()\n",
"\n",
" loss = policy_loss + CONFIG[\"value_coef\"] * value_loss - CONFIG[\"entropy_coef\"] * entropy\n",
"\n",
" self.optimizer.zero_grad(set_to_none=True)\n",
" self.scaler.scale(loss).backward()\n",
" self.scaler.unscale_(self.optimizer)\n",
" nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)\n",
" self.scaler.step(self.optimizer)\n",
" self.scaler.update()\n",
"\n",
" total_policy_loss += policy_loss.item()\n",
" total_value_loss += value_loss.item()\n",
" num_updates += 1\n",
"\n",
" # --- PHASE 4: METRICS ---\n",
" win_count = (rewards_buf > 0).sum().item()\n",
" draw_count = ((dones_buf) & (rewards_buf == 0)).sum().item()\n",
"\n",
" if (iteration + 1) % CONFIG[\"log_interval\"] == 0:\n",
" fps = dataset_size / max(time.time() - iter_start, 0.001)\n",
" log_data = [\n",
" iteration + 1,\n",
" total_policy_loss / max(1, num_updates),\n",
" total_value_loss / max(1, num_updates),\n",
" flat_returns.mean().item(),\n",
" fps,\n",
" win_count / B,\n",
" draw_count / B\n",
" ]\n",
"\n",
" print(f\"[Iter {log_data[0]:04d}] P: {log_data[1]:.4f} | V: {log_data[2]:.4f} | \"\n",
" f\"V_Mean: {log_data[3]:.3f} | Win%: {log_data[5]:.2f} | FPS: {log_data[4]:.0f}\")\n",
"\n",
" with open(self.log_file, \"a\", newline=\"\") as f:\n",
" csv.writer(f).writerow(log_data)\n",
"\n",
" if (iteration + 1) % CONFIG[\"save_interval\"] == 0:\n",
" self.save_checkpoint(iteration + 1)\n",
" self.plot_metrics()\n",
"\n",
" # Free unused memory back to cache aggressively\n",
" torch.cuda.empty_cache()\n",
"\n",
" def plot_metrics(self):\n",
" if not os.path.exists(self.log_file): return\n",
" df = pd.read_csv(self.log_file)\n",
" if len(df) == 0: return\n",
"\n",
" fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
" axs[0, 0].plot(df['iteration'], df['p_loss'], color='blue'); axs[0, 0].set_title('Policy Loss')\n",
" axs[0, 1].plot(df['iteration'], df['v_loss'], color='red'); axs[0, 1].set_title('Value Loss')\n",
" axs[1, 0].plot(df['iteration'], df['v_mean'], color='green'); axs[1, 0].set_title('Value Mean')\n",
" if 'win_rate' in df.columns:\n",
" axs[1, 1].plot(df['iteration'], df['win_rate'], label='Win', color='purple')\n",
" axs[1, 1].plot(df['iteration'], df['draw_rate'], label='Draw', color='orange')\n",
" axs[1, 1].set_title('Outcomes'); axs[1, 1].legend()\n",
"\n",
" plt.tight_layout()\n",
" plt.savefig(os.path.join(CONFIG[\"checkpoint_dir\"], \"training_performance.png\"))\n",
" plt.close(fig)\n",
"\n",
"if __name__ == \"__main__\":\n",
" parser = argparse.ArgumentParser()\n",
" parser.add_argument(\"--iterations\", type=int, default=10000)\n",
" parser.add_argument(\"--test-batch\", action=\"store_true\")\n",
" args, _ = parser.parse_known_args()\n",
"\n",
" torch.manual_seed(CONFIG[\"seed\"])\n",
" np.random.seed(CONFIG[\"seed\"])\n",
" random.seed(CONFIG[\"seed\"])\n",
"\n",
" trainer = GRPOTrainer()\n",
" trainer.train(2 if args.test_batch else args.iterations)"
]
}
]
} |