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#!/bin/python3 |
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from cshogi import Board, move_to_usi |
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from cshogi.dlshogi import FEATURES1_NUM, FEATURES2_NUM, make_input_features, make_move_label |
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import numpy as np |
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import onnxruntime |
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import time |
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# License: GPLv3 https://www.gnu.org/licenses/gpl-3.0.html |
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# エンジン名 |
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enginename = "OnnxPolicyPlayer" |
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# モデルファイル名（より強いモデルを使えばより強くなる） |
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modelfile_default: str = "amano.onnx" |
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modelfile: str = modelfile_default |
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# 温度パラメータ（温度が低いと最大の指し手が選択されやすくなる、温度が高いと全ての指し手が均等に選択されやすくなる） |
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temperature_default: int = 200 |
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temperature: int = temperature_default |
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# 平手初期局面のSFEN文字列 |
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SFEN_HIRATE = "lnsgkgsnl/1r5b1/ppppppppp/9/9/9/PPPPPPPPP/1B5R1/LNSGKGSNL b - 1" |
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# 局面情報 |
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board = Board() |
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# 推論クラス |
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player = None |
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# device_id（複数のCUDAデバイスが存在する場合） |
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device_id: int = 0 |
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# enable_cuda（CUDAを使用するかどうか） |
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enable_cuda: bool = False |
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# enable_tensorrt（TensorRTを使用するかどうか） |
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enable_tensorrt: bool = False |
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# usi_ponder |
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usi_ponder: bool = False |
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# 先読みモード |
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go_ponder: bool = False |
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class OnnxPolicyPlayer: |
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# セッション初期化 |
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def __init__(self, modelfile:str="model.onnx", temperature:int=200): |
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self.temperature = temperature |
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print("info string load modelfile {}".format(modelfile)) |
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# ONNXモデルの推論セッション |
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available_providers = onnxruntime.get_available_providers() |
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enable_providers = [] |
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if enable_tensorrt and 'TensorrtExecutionProvider' in available_providers: |
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enable_providers.append(('TensorrtExecutionProvider', { |
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'device_id': device_id, |
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'trt_fp16_enable': True, |
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'trt_engine_cache_enable': True, |
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})) |
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print("info string enable TensorrtExecutionProvider") |
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if enable_cuda and 'CUDAExecutionProvider' in available_providers: |
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enable_providers.append(('CUDAExecutionProvider', { |
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'device_id': device_id, |
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})) |
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print("info string enable CUDAExecutionProvider") |
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if 'CPUExecutionProvider' in available_providers: |
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enable_providers.append('CPUExecutionProvider') |
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print("info string enable CPUExecutionProvider") |
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self.session = onnxruntime.InferenceSession(modelfile, providers=enable_providers) |
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# 入力特徴量の作成 |
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batchsize_max: int = 1 |
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self.features1 = np.empty((batchsize_max, FEATURES1_NUM, 9, 9), dtype=np.float32) |
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self.features2 = np.empty((batchsize_max, FEATURES2_NUM, 9, 9), dtype=np.float32) |
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# 推論の実行（初回は実行に時間が掛かる事があるため、ここで慣らし実行しておく） |
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self.move_infer_choice(board) |
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# 局面の推論と指し手の選択 |
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def move_infer_choice(self, board): |
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# 合法手生成 |
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color: int = board.turn |
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legalmoves = np.array(list(board.legal_moves), dtype=np.uint32) |
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# 合法手が無ければ投了 |
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if len(legalmoves) == 0: |
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return None, None, None, None |
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# 合法手が1個のみなら推論せずにそれを返す |
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if len(legalmoves) == 1: |
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return int(legalmoves[0]), None, None, None |
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# 入力特徴量の作成 |
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make_input_features(board, self.features1[0], self.features2[0]) |
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# 推論 |
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batchsize: int = 1 |
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io_binding = self.session.io_binding() |
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io_binding.bind_cpu_input('input1', self.features1[0:batchsize]) |
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io_binding.bind_cpu_input('input2', self.features2[0:batchsize]) |
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io_binding.bind_output('output_policy') |
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io_binding.bind_output('output_value') |
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self.session.run_with_iobinding(io_binding) |
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# 推論結果の取り出し |
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infer_policy_logits, infer_values = io_binding.copy_outputs_to_cpu() |
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infer_policy_logit = infer_policy_logits[0] |
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infer_value = float(infer_values[0]) |
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# 合法手ごとの選択強度 |
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probabilities_logit = np.empty(len(legalmoves), dtype=np.float32) |
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for i in range(len(legalmoves)): |
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move = legalmoves[i] |
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move_label = make_move_label(move, color) |
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probabilities_logit[i] = infer_policy_logit[move_label] |
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# Boltzmann分布 |
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probabilities = self.softmax_temperature_with_normalize(probabilities_logit, self.temperature) |
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# probabilitiesの確率に従って乱択 |
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cmove: int = np.random.choice(legalmoves, p=probabilities) |
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return cmove, infer_value, probabilities, legalmoves |
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def set_temperature(self, temperature:float): |
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self.temperature = temperature |
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# 温度パラメータの適用と正規化 |
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def softmax_temperature_with_normalize(self, logits, temperature:int): |
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# 温度パラメータを適用 |
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logits /= (0.001 * float(temperature)) |
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# 確率を計算(オーバーフローを防止するため最大値で引く) |
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max_logit = max(logits) |
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probabilities = np.exp(logits - max_logit) |
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# 合計が1になるように正規化 |
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sum_probabilities = sum(probabilities) |
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probabilities /= sum_probabilities |
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return probabilities |
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while True: |
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# コマンド入力行全体 |
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cmdline: str = input() |
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# 時間計測開始 |
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time_sta = time.perf_counter() |
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# 空白文字区切りに分割 |
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cmds = cmdline.split() |
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# 先頭コマンド節取り出し |
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cmd0: str = cmds.pop(0) |
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# USI応答 |
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if cmd0 == "usi": |
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print("id name {}".format(enginename)) |
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print("option name DNN_Model type string default {}".format(modelfile_default)) |
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print("option name Device_ID type spin default 0 min 0 max 255") |
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print("option name Enable_CUDA type check default false") |
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print("option name Enable_TensorRT type check default false") |
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print("option name Softmax_Temperature type spin default {:d} min 1 max 5000".format(temperature_default)) |
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print("option name USI_Ponder type check default false") |
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print("usiok") |
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continue |
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# オプション設定 |
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if cmd0 == "setoption": |
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if len(cmds) < 4: |
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continue |
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cmd1 = cmds.pop(0) |
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cmd2 = cmds.pop(0) |
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cmd3 = cmds.pop(0) |
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cmd4 = ' '.join(cmds) |
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if cmd1 != "name": |
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continue |
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if cmd3 != "value": |
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continue |
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if cmd2 == "DNN_Model": |
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modelfile = cmd4 |
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continue |
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if cmd2 == "Device_ID": |
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valueint = int(cmd4) |
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if 0 <= valueint and valueint < 16: |
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device_id = valueint |
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continue |
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if cmd2 == "Enable_CUDA": |
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if cmd4 == "true": |
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enable_cuda = True |
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else: |
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enable_cuda = False |
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continue |
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if cmd2 == "Enable_TensorRT": |
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if cmd4 == "true": |
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enable_tensorrt = True |
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else: |
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enable_tensorrt = False |
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continue |
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if cmd2 == "Softmax_Temperature": |
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valueint = int(cmd4) |
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if 0 < valueint and valueint <= 5000: |
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temperature = valueint |
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if player is not None: |
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player.set_temperature(valueint) |
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continue |
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if cmd2 == "USI_Ponder": |
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if cmd4 == "true": |
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usi_ponder = True |
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else: |
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usi_ponder = False |
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continue |
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continue |
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# 初期化 |
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if cmd0 == "isready": |
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board.set_sfen(SFEN_HIRATE) |
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go_ponder = False |
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# 推論処理初期化 |
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player = OnnxPolicyPlayer(modelfile=modelfile, temperature=temperature) |
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# 初期化完了 |
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print("readyok") |
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continue |
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# 局面設定 |
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if cmd0 == "position": |
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# 次のコマンド節 |
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cmd1: str = cmds.pop(0) |
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# 初期局面が平手の場合 |
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if cmd1 == "startpos": |
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board.set_sfen(SFEN_HIRATE) |
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# 初期局面が任意の場合 |
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if cmd1 == "sfen": |
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board.set_sfen(' '.join(cmds[0:4])) |
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del cmds[0:4] |
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# 指し手文字列の処理 |
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while len(cmds) > 0: |
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move = cmds.pop(0) |
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if move == "moves": |
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continue |
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board.push_usi(move) |
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continue |
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# 探索 |
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# 今回は長時間の探索処理を省いているので、以下のタイミングで指し手を決定して出力する。 |
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# - 「先読み以外の探索開始(go ponder ではない go)」 |
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# - 「先読みした相手の指し手が当たり(go ponder 中の ponderhit)」 |
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# - 「先読みした相手の指し手が外れ(go ponder 中の stop)」 |
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if cmd0 == "go" or (go_ponder and (cmd0 == "ponderhit" or cmd0 == "stop")): |
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# 推論セッションの初期化が済んでいなければエラーを出力 |
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if player is None: |
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print("info string Error! go cmd before isready cmd.") |
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continue |
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# 先読み探索はしない |
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if "ponder" in cmds: |
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go_ponder = True |
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continue |
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else: |
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go_ponder = False |
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# 終局判定 |
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if board.is_game_over(): |
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time_dur_ms = int(1000 * (time.perf_counter() - time_sta)) |
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print("info time {:d} pv resign".format(time_dur_ms)) |
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print("bestmove resign") |
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continue |
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# 入玉宣言勝ち判定 |
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if board.is_nyugyoku(): |
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time_dur_ms = int(1000 * (time.perf_counter() - time_sta)) |
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print("info time {:d} pv win".format(time_dur_ms)) |
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print("bestmove win") |
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continue |
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# 指し手を選択 |
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bestmove, bestmove_value, bestmove_probs, bestmove_legals = player.move_infer_choice(board) |
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if bestmove is None: |
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time_dur_ms = int(1000 * (time.perf_counter() - time_sta)) |
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print("info time {:d} pv resign".format(time_dur_ms)) |
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print("bestmove resign") |
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continue |
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bestmoveusi: str = move_to_usi(bestmove) |
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# 指し手を進める |
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board.push_usi(bestmoveusi) |
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# 相手の指し手を選択 |
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if usi_ponder: |
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pondermove, pondermove_value, pondermove_probs, pondermove_legals = player.move_infer_choice(board) |
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else: |
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pondermove, pondermove_value, pondermove_probs, pondermove_legals = None, None, None, None |
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if pondermove is not None: |
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pondermoveusi: str = move_to_usi(pondermove) |
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else: |
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pondermoveusi: str = "resign" |
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# 指し手を戻す |
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board.pop() |
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# 評価値を出力 |
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time_dur_ms = int(1000 * (time.perf_counter() - time_sta)) |
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if (bestmove_value is not None) and (bestmove_probs is not None) and (bestmove_legals is not None): |
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score: int = int(4000 * (bestmove_value - 0.5)) |
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for i, mvidx in reversed(list(enumerate(np.argsort(-bestmove_probs)))): |
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prob_percent = 100.0 * bestmove_probs[mvidx] |
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if prob_percent < 0.1: |
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continue |
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if usi_ponder and bestmove == pondermove_legals[mvidx]: |
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print("info time {:d} score cp {:d} multipv {:d} pv {} {} ({:.1f}%)".format(time_dur_ms, score, i + 1, bestmoveusi, pondermoveusi, prob_percent)) |
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else: |
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print("info time {:d} score cp {:d} multipv {:d} pv {} ({:.1f}%)".format(time_dur_ms, score, i + 1, move_to_usi(bestmove_legals[mvidx]), prob_percent)) |
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else: |
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print("info time {:d} pv {}".format(time_dur_ms, bestmoveusi)) |
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# 指し手を出力 |
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if pondermove is not None: |
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print("bestmove {} ponder {}".format(bestmoveusi, pondermoveusi)) |
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else: |
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print("bestmove {}".format(bestmoveusi)) |
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continue |
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# 対局終了：先読み状態のみ解消する |
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if cmd0 == "gameover": |
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go_ponder = False |
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# エンジン終了：ここではループの外に出るだけ |
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if cmd0 == "quit": |
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break |
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# デバッグ用：局面情報の出力 |
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if cmd0 == "d": |
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print(board) |
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print(board.sfen()) |
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# どれにも引っかからない場合は無視してループに戻る |
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continue |
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# ループを抜けたら終了 |
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exit |
