{ "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", "šŸš€ 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: 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FPS: 2543\n", "[Iter 0781] P: 0.0538 | V: 0.1641 | V_Mean: 0.260 | Win%: 2.61 | FPS: 2447\n", "[Iter 0782] P: 0.0687 | V: 0.1649 | V_Mean: 0.261 | Win%: 2.61 | FPS: 2569\n", "[Iter 0783] P: 0.0651 | V: 0.1641 | V_Mean: 0.260 | Win%: 2.61 | FPS: 2200\n", "[Iter 0784] P: 0.0640 | V: 0.1642 | V_Mean: 0.260 | Win%: 2.62 | FPS: 2501\n", "[Iter 0785] P: 0.0700 | V: 0.1644 | V_Mean: 0.259 | Win%: 2.62 | FPS: 2448\n", "[Iter 0786] P: 0.0654 | V: 0.1638 | V_Mean: 0.259 | Win%: 2.62 | FPS: 2204\n", "[Iter 0787] P: 0.0661 | V: 0.1647 | V_Mean: 0.259 | Win%: 2.62 | FPS: 2139\n", "[Iter 0788] P: 0.0614 | V: 0.1634 | V_Mean: 0.258 | Win%: 2.62 | FPS: 2306\n", "[Iter 0789] P: 0.0585 | V: 0.1649 | V_Mean: 0.259 | Win%: 2.62 | FPS: 2380\n", "[Iter 0790] P: 0.0600 | V: 0.1652 | V_Mean: 0.259 | Win%: 2.62 | FPS: 2546\n", "[Iter 0791] P: 0.0816 | V: 0.1650 | V_Mean: 0.258 | Win%: 2.62 | FPS: 2529\n", "[Iter 0792] P: 0.0692 | V: 0.1635 | V_Mean: 0.258 | Win%: 2.63 | FPS: 2526\n", "[Iter 0793] P: 0.0711 | V: 0.1640 | V_Mean: 0.258 | Win%: 2.63 | FPS: 2517\n", "[Iter 0794] P: 0.0586 | V: 0.1638 | V_Mean: 0.260 | Win%: 2.63 | FPS: 2509\n", "[Iter 0795] P: 0.0551 | V: 0.1639 | V_Mean: 0.259 | Win%: 2.64 | FPS: 2140\n", "[Iter 0796] P: 0.0599 | V: 0.1624 | V_Mean: 0.258 | Win%: 2.64 | FPS: 2412\n", "[Iter 0797] P: 0.0599 | V: 0.1640 | V_Mean: 0.259 | Win%: 2.64 | FPS: 2356\n", "[Iter 0798] P: 0.0606 | V: 0.1645 | V_Mean: 0.259 | Win%: 2.64 | FPS: 2134\n", "[Iter 0799] P: 0.0546 | V: 0.1633 | V_Mean: 0.258 | Win%: 2.64 | FPS: 2173\n", "[Iter 0800] P: 0.0551 | V: 0.1640 | V_Mean: 0.258 | Win%: 2.64 | FPS: 2269\n", "[Iter 0801] P: 0.0704 | V: 0.1646 | V_Mean: 0.258 | Win%: 2.64 | FPS: 2143\n", "[Iter 0802] P: 0.0568 | V: 0.1644 | V_Mean: 0.258 | Win%: 2.64 | FPS: 2071\n", "[Iter 0803] P: 0.0551 | V: 0.1644 | V_Mean: 0.258 | Win%: 2.64 | FPS: 2268\n", "[Iter 0804] P: 0.0567 | V: 0.1633 | V_Mean: 0.257 | Win%: 2.64 | FPS: 2245\n", "[Iter 0805] P: 0.0644 | V: 0.1652 | V_Mean: 0.255 | Win%: 2.64 | FPS: 2201\n", "[Iter 0806] P: 0.0589 | V: 0.1638 | V_Mean: 0.255 | Win%: 2.65 | FPS: 2247\n", "[Iter 0807] P: 0.0615 | V: 0.1645 | V_Mean: 0.254 | Win%: 2.65 | FPS: 2157\n", "[Iter 0808] P: 0.0555 | V: 0.1630 | V_Mean: 0.251 | Win%: 2.65 | FPS: 2118\n", "[Iter 0809] P: 0.0602 | V: 0.1624 | V_Mean: 0.250 | Win%: 2.65 | FPS: 2041\n" ] } ], "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 ā€“ 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 ā€“ 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\"\\nšŸš€ 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)" ] } ] }