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2lambda123 / huawei-noah-xingtian

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Added Chinese Chess Environment, Utility Functions, and Nash Battle Worker Module #1

Closed 2lambda123 closed 6 months ago

2lambda123 commented 6 months ago

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Relevant files
Enhancement
cchess_env.py
Added Chinese Chess Environment and Utility Functions       
examples/chinese_chess/env/cchess_env.py ['Added a new class `CchessEnv` with various methods for Chinese chess environment.', 'Added functions for creating UCI labels and position labels.', 'Added a new class `BaseChessBoard` with methods for initializing, copying, putting pieces, and moving pieces on the board.', 'Added functions for creating position labels and labels for legal moves.', 'Added a new class `Pos` for representing positions on the board.', 'Added a new class `ChessSide` with methods for handling different sides in the game.']
+889/-0 
nash_evaluate_worker.py
Added Nash Battle Worker Module                                                   
examples/chinese_chess/nash_evaluate_worker.py ['Added a new module for Nash battle worker in Chinese chess.', 'Implemented functions for setting up players, playing till the end, and conducting Nash battles.', 'Added a new class `IcyPlayer` for handling player actions and interactions.', 'Implemented functions for getting models to battle and performing Nash battles.']
+358/-0 
__init__.py
Imported Exchanger and Trainer Classes                                     
examples/chinese_chess/server/__init__.py ['Imported `Exchanger` and `Trainer` classes from the `server` module.']
+2/-0     
performance-testing-bot[bot] commented 6 months ago

Unable to locate .performanceTestingBot config file

cr-gpt[bot] commented 6 months ago

Seems you are using me but didn't get OPENAI_API_KEY seted in Variables/Secrets for this repo. you could follow readme for more information

code-companion-ai[bot] commented 6 months ago

Processing PR updates...

git-greetings[bot] commented 6 months ago

Thanks @2lambda123 for opening this PR!

For COLLABORATOR only :

quine-bot[bot] commented 6 months ago

👋 Figuring out if a PR is useful is hard, hopefully this will help.

  • @2lambda123 has been on GitHub since 2019 and in that time has had 2267 public PRs merged
  • They haven't contributed to this repo before
  • Here's a good example of their work: HeyGenClone
  • From looking at their profile, they seem to be good with Shell and Python.

Their most recently public accepted PR is: https://github.com/2lambda123/-inbarhub-DDPM_inversion/pull/2

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git-greetings[bot] commented 6 months ago

First PR by @2lambda123

PR Details of @2lambda123 in huawei-noah-xingtian : OPEN CLOSED TOTAL
1 0 1
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Commits Files that changed from the base of the PR and between e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 and 5845b8c2941deb47caa9a4de9d5bf0a897123728.

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pep8speaks commented 6 months ago

Hello @2lambda123! Thanks for opening this PR. We checked the lines you've touched for PEP 8 issues, and found:

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Line 29:1: W293 blank line contains whitespace Line 38:43: E231 missing whitespace after ',' Line 42:80: E501 line too long (110 > 79 characters) Line 64:80: E501 line too long (80 > 79 characters) Line 66:80: E501 line too long (80 > 79 characters) Line 69:80: E501 line too long (80 > 79 characters) Line 71:80: E501 line too long (80 > 79 characters)

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codesyncapp[bot] commented 6 months ago

Check out the playback for this Pull Request here.

penify-dev[bot] commented 6 months ago

PR Review 🔍

⏱️ Estimated effort to review [1-5] 4, due to the significant amount of new code added, including multiple classes, methods, and functions. Understanding the Chinese Chess environment, utility functions, and Nash battle worker module requires careful examination.
🧪 Relevant tests No
⚡ Possible issues Possible Bug: - In the `BaseChessBoard` class, the `move` method modifies the `pos_from` and `pos_to` objects directly, which might lead to unexpected behavior. Consider creating new `Pos` objects instead of modifying the existing ones.
Code Duplication: - There is some code duplication in the `create_uci_labels` and `create_position_labels` functions. Consider refactoring to reduce redundancy and improve maintainability.
Performance Concern: - The `get_legal_moves` method in the `CchessEnv` class performs a large number of nested loops and conditional checks, which could impact performance for large datasets. Consider optimizing the algorithm for better efficiency.
🔒 Security concerns No
penify-dev[bot] commented 6 months ago

PR Code Suggestions ✨

CategorySuggestion                                                                                                                                    Score
Maintainability
Break down the cal_nash function into smaller functions for improved maintainability ___ **Consider breaking down the cal_nash function into smaller, more focused functions for
better maintainability.** [examples/chinese_chess/nash_evaluate.py [337-450]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-5d365731269c4acfc9ddecf11b21809c7d48b1364b9284e76ef06366e4a82633R337-R450) ```diff def cal_nash(): """ - cal nash and rpp - upload all res files + Calculate Nash equilibrium and RPP, and upload all result files. """ model_stamps = get_sorted_weight_list() + model_pool_list = get_model_pool_list() + latest_model_stamp = get_latest_weight_path() + nash_res, rate_table_pd, nash_res_with_bot, rate_table_bot_pd = nash.build_rate_table(model_pool_list, latest_model_stamp, conf.ResourceConfig.nash_battle_local_dir) + model_pool = maintain_pool_model(nash_res, model_stamps) + save_model_pool(model_pool, latest_model_stamp) + save_nash_res(nash_res, latest_model_stamp) + save_nash_res_with_bot(nash_res_with_bot, latest_model_stamp) + save_rate_tables(rate_table_pd, rate_table_bot_pd, latest_model_stamp) ... ```
Suggestion importance[1-10]: 8 Why: Breaking down the `cal_nash` function into smaller, focused functions will significantly enhance maintainability by improving code organization and clarity.
8
Refactor the download_latest_model function to enhance readability and maintainability ___ **Consider refactoring the download_latest_model function to improve readability and
maintainability.** [examples/chinese_chess/nash_evaluate.py [30-97]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-5d365731269c4acfc9ddecf11b21809c7d48b1364b9284e76ef06366e4a82633R30-R97) ```diff def download_latest_model(args): """ - download latest weight + Download the latest weight if `nash_index` is 0. """ - if args.nash_index == 0: - while True: + while True: + done = False + new_weight_names = [] + local_weights = os.listdir(conf.ResourceConfig.model_dir) + local_weights.sort() + latest_weight_stamp = get_latest_weight_path() ... ```
Suggestion importance[1-10]: 7 Why: Refactoring the `download_latest_model` function will enhance code readability and maintainability, making the logic clearer.
7
Refactor the get_model_to_battle function to improve readability and maintainability by extracting start and end index calculations ___ **Consider refactoring the get_model_to_battle function for better readability and
maintainability.** [examples/chinese_chess/nash_evaluate_worker.py [37-48]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-f5259c60cc9ecaac9d5c84c5abab4d7f328041a96335ad49b96ba2908c1b46c9R37-R48) ```diff def get_model_to_battle(model_pool_list, args): -""" -""" -if not model_pool_list: -return [], [] -len_pre_node = int(len(model_pool_list) / conf.EvaluateConfig.nash_nodes_num) -if args.nash_index < conf.EvaluateConfig.nash_nodes_num - 1: -model_pool_this_nash_worker = model_pool_list[args.nash_index * len_pre_node: (args.nash_index + 1) * len_pre_node] -else: -model_pool_this_nash_worker = model_pool_list[args.nash_index * len_pre_node:] + if not model_pool_list: + return [], [] + + len_pre_node = int(len(model_pool_list) / conf.EvaluateConfig.nash_nodes_num) + start_index = args.nash_index * len_pre_node + end_index = (args.nash_index + 1) * len_pre_node if args.nash_index < conf.EvaluateConfig.nash_nodes_num - 1 else None + model_pool_this_nash_worker = model_pool_list[start_index:end_index] ```
Suggestion importance[1-10]: 7 Why: The suggestion improves the readability and maintainability by extracting start and end index calculations in the `get_model_to_battle` function.
7
Refactor variable names in a method for clarity and maintainability ___ **Consider refactoring the `cal_rate` method for improved readability and maintainability.** [examples/chinese_chess/nash/cal_nash_eq.py [49]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0132e74a04acb916c62f2405e43f50d8f5e02e0747c51c8fda3d787b2056cfeaR49-R49) ```diff -for one_play in chess_plays: +for play_file in chess_plays: ```
Suggestion importance[1-10]: 7 Why: The suggestion improves code readability and maintainability by using a more descriptive variable name 'play_file' instead of 'one_play'.
7
Refactor the add_grad_to_list function for improved readability and maintainability ___ **Consider refactoring the add_grad_to_list function to improve readability and
maintainability.** [examples/chinese_chess/agent/resnet.py [116-119]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0a8bba383181412f524780c90e1dc331f1a3d09573cba6cae65535a1bbf5b46aR116-R119) ```diff -def add_grad_to_list(opt, train_param, loss, tower_grad): - grads = opt.compute_gradients(loss, var_list=train_param) - grads = [i[0] for i in grads] - tower_grad.append(zip(grads, train_param)) +def add_grad_to_list(optimizer, trainable_params, loss, tower_grad): + gradients = optimizer.compute_gradients(loss, var_list=trainable_params) + gradients = [grad[0] for grad in gradients] + tower_grad.append(zip(gradients, trainable_params)) ```
Suggestion importance[1-10]: 7 Why: The suggestion addresses maintainability by refactoring the function for better readability.
7
Break down the get_model function into smaller, more focused functions to enhance readability and maintainability ___ **Consider breaking down the get_model function into smaller, more focused functions to
improve readability and maintainability.** [examples/chinese_chess/agent/resnet.py [136-291]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0a8bba383181412f524780c90e1dc331f1a3d09573cba6cae65535a1bbf5b46aR136-R291) ```diff -136 +def get_model( -... -291 + return (sess, graph), ((x_input, training), (net_softmax, value_head)) +# Split the get_model function into smaller, more focused functions +def build_graph(): + # Build the TensorFlow graph + ... +def initialize_session(): + # Initialize the TensorFlow session + ... + +def restore_model(): + # Restore the model if provided + ... + +def train_model(): + # Train the model + ... + +def return_results(): + # Return the results + ... + +return build_graph(), initialize_session(), restore_model(), train_model(), return_results() + ```
Suggestion importance[1-10]: 7 Why: Breaking down the function into smaller, focused functions enhances readability and maintainability.
7
Improve error handling in the nash_battle function by catching specific exceptions and providing more detailed error messages ___ **Consider handling potential exceptions more specifically in the nash_battle function for
better error management.** [examples/chinese_chess/nash_evaluate_worker.py [330]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-f5259c60cc9ecaac9d5c84c5abab4d7f328041a96335ad49b96ba2908c1b46c9R330-R330) ```diff -except: - logging.error('battle model {} error'.format(model)) +except Exception as e: + logging.error(f'Error occurred while battling model {model}: {e}') ```
Suggestion importance[1-10]: 6 Why: Improving error handling in the `nash_battle` function by catching specific exceptions and providing more detailed error messages enhances maintainability.
6
Improve handling of division by zero in a calculation ___ **Consider handling the case where the sum of row_play_counts is zero more explicitly.** [examples/chinese_chess/nash/cal_nash_eq.py [93]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0132e74a04acb916c62f2405e43f50d8f5e02e0747c51c8fda3d787b2056cfeaR93-R93) ```diff -row_play_counts / np.sum(row_play_counts) if np.sum(row_play_counts) != 0 else row_play_counts + 1 / len(row_play_counts) +if np.sum(row_play_counts) == 0: + row_play_counts = [1 / len(row_play_counts) for _ in row_play_counts] +else: + row_play_counts = row_play_counts / np.sum(row_play_counts) ```
Suggestion importance[1-10]: 6 Why: The suggestion suggests a more explicit way to handle the case where the sum of 'row_play_counts' is zero, enhancing code maintainability.
6
Encapsulate complex logic in a separate method for improved maintainability ___ **Consider extracting the complex logic in the rpp method into smaller, more focused
functions for better maintainability.** [examples/chinese_chess/nash/cal_nash_eq.py [158]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0132e74a04acb916c62f2405e43f50d8f5e02e0747c51c8fda3d787b2056cfeaR158-R158) ```diff -rpp = np.matmul(row_nash_q.T, rate_tables) +rpp = self.calculate_relative_population_performance(row_nash_q, rate_tables) ```
Suggestion importance[1-10]: 6 Why: The suggestion recommends extracting complex logic into a separate method 'calculate_relative_population_performance' for better maintainability.
6
Consolidate the logic for creating convolutional layers to reduce duplication ___ **Consider consolidating the repeated logic for creating convolutional layers in the
res_block and conv_block functions to reduce duplication.** [examples/chinese_chess/agent/resnet.py [5-35]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0a8bba383181412f524780c90e1dc331f1a3d09573cba6cae65535a1bbf5b46aR5-R35) ```diff -5 +def res_block(inputx, name, training, block_num=2, filters=256, kernel_size=(3, 3)): -... -23 +def conv_block(inputx, name, training, block_num=1, filters=2, kernel_size=(1, 1)): -... +def create_conv_layer(inputx, name, training, block_num, filters, kernel_size): + net = inputx + for i in range(block_num): + net = tf.layers.conv2d( + net, + filters=filters, + kernel_size=kernel_size, + activation=None, + name="{}_conv_conv{}".format(name, i), + padding='same' + ) + net = tf.layers.batch_normalization(net, training=training, name="{}_conv_bn{}".format(name, i)) + net = tf.nn.elu(net, name="{}_conv_elu{}".format(name, i)) + return net ```
Suggestion importance[1-10]: 6 Why: The suggestion aims to reduce duplication and improve maintainability by consolidating the logic for creating convolutional layers.
6
Enhance the get_nash_battle_list function by consolidating the logic for shuffling and extending lists ___ **Consider consolidating the repeated logic for shuffling and extending lists in the
get_nash_battle_list function.** [examples/chinese_chess/nash_evaluate_worker.py [109-112]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-f5259c60cc9ecaac9d5c84c5abab4d7f328041a96335ad49b96ba2908c1b46c9R109-R112) ```diff np.random.shuffle(models_to_battle_other_workers) -models_to_battle.extend(models_to_battle_other_workers) np.random.shuffle(models_to_battle_other_node) -models_to_battle.extend(models_to_battle_other_node) +models_to_battle.extend(models_to_battle_other_workers + models_to_battle_other_node) ```
Suggestion importance[1-10]: 5 Why: The suggestion enhances the `get_nash_battle_list` function by consolidating the logic for shuffling and extending lists, improving maintainability.
5
Replace magic numbers with named constants for clarity in calculations ___ **Avoid using magic numbers directly in calculations for better code understanding.** [examples/chinese_chess/nash/cal_nash_eq.py [142]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0132e74a04acb916c62f2405e43f50d8f5e02e0747c51c8fda3d787b2056cfeaR142-R142) ```diff -rate_tables = [[0 for _ in range(model_pool_size)] for _ in range(model_pool_size)] +initial_value = 0 +rate_tables = [[initial_value for _ in range(model_pool_size)] for _ in range(model_pool_size)] ```
Suggestion importance[1-10]: 5 Why: The suggestion enhances code understanding by introducing a named constant 'initial_value' instead of a magic number directly in calculations.
5
Modularity
Extract the logic for creating policy and value heads into separate functions for better modularity ___ **Consider extracting the logic for creating the policy and value heads into separate
functions for better modularity.** [examples/chinese_chess/agent/resnet.py [184-211]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-0a8bba383181412f524780c90e1dc331f1a3d09573cba6cae65535a1bbf5b46aR184-R211) ```diff -184 + with tf.variable_scope("policy_head"): -... -199 + with tf.variable_scope("value_head"): -... +def create_policy_head(body): + policy_head = tf.layers.conv2d(body, 2, 1, padding='SAME') + policy_head = tf.contrib.layers.batch_norm( + policy_head, + center=False, + epsilon=1e-5, + fused=True, + is_training=training, + activation_fn=tf.nn.relu + ) + policy_head = tf.reshape(policy_head, [-1, 9 * 10 * 2]) + policy_head = tf.contrib.layers.fully_connected(policy_head, len(labels), activation_fn=None) + return policy_head +def create_value_head(body): + value_head = tf.layers.conv2d(body, 1, 1, padding='SAME') + value_head = tf.contrib.layers.batch_norm( + value_head, + center=False, + epsilon=1e-5, + fused=True, + is_training=training, + activation_fn=tf.nn.relu + ) + value_head = tf.reshape(value_head, [-1, 9 * 10 * 1]) + value_head = tf.contrib.layers.fully_connected(value_head, 256, activation_fn=tf.nn.relu) + value_head = tf.contrib.layers.fully_connected(value_head, 1, activation_fn=tf.nn.tanh) + return value_head + ```
Suggestion importance[1-10]: 8 Why: Extracting the logic into separate functions enhances modularity and improves code organization.
8
Extract the logic from nash_battle_process into a separate function for improved modularity ___ **Consider extracting the logic inside the nash_battle_process function into a separate
helper function for better modularity.** [examples/chinese_chess/nash_evaluate.py [150-161]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-5d365731269c4acfc9ddecf11b21809c7d48b1364b9284e76ef06366e4a82633R150-R161) ```diff -def nash_battle_process(gpu_num, worker_index, nash_index): +def execute_nash_battle(gpu_num, worker_index, nash_index): try: logging.info('nash battle gpu_num {}'.format(gpu_num)) worker_nash_battle = os.path.join(project_path, 'nash_evaluate_worker.py') - logging.info('excute command python {} -gpu_num {} -worker_index {} -nash_index {}'.format( + logging.info('execute command python {} -gpu_num {} -worker_index {} -nash_index {}'.format( worker_nash_battle, gpu_num, worker_index, nash_index)) subprocess.run('{} {} -gpu_num {} -worker_index {} -nash_index {}'.format( conf.ResourceConfig.python_executor, worker_nash_battle, gpu_num, worker_index, nash_index), shell=False) except: - logging.error('nahs battle error') + logging.error('nash battle error') ```
Suggestion importance[1-10]: 6 Why: Extracting the logic from `nash_battle_process` into a separate function can improve modularity and make the code more organized.
6
Readability, maintainability
Refactor the nash_eva function by splitting the logic into smaller, focused functions for improved readability and maintainability ___ **Consider refactoring the nash_eva function to separate the logic into smaller, more
focused functions for better readability and maintainability.** [examples/chinese_chess/nash_evaluate.py [512-541]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-5d365731269c4acfc9ddecf11b21809c7d48b1364b9284e76ef06366e4a82633R512-R541) ```diff +def nash_eva(args): + init_nash(args) + nash_battle(args) + if args.nash_index == 0: + try: + cal_nash() + except: + logging.error('cal nash error') + upload_all_res() + while True: + download_latest_model(args) + download_model_pool_list(args) + nash_battle(args) + if args.nash_index == 0: + try: + cal_nash() + except BaseException: + logging.error('cal nash error') + upload_all_res() - ```
Suggestion importance[1-10]: 8 Why: Refactoring the `nash_eva` function by splitting the logic into smaller, focused functions will greatly improve code readability and maintainability.
8
Readability
Enhance string formatting in the nash_battle function using f-strings for better readability ___ **Consider using f-strings for string formatting in the nash_battle function for improved
readability.** [examples/chinese_chess/nash_evaluate_worker.py [329]](https://github.com/2lambda123/huawei-noah-xingtian/pull/1/files#diff-f5259c60cc9ecaac9d5c84c5abab4d7f328041a96335ad49b96ba2908c1b46c9R329-R329) ```diff -logging.info('{} vs {} battle {} games'.format(latest_model_name, model, battle_num) +logging.info(f'{latest_model_name} vs {model} battle {battle_num} games') ```
Suggestion importance[1-10]: 4 Why: The suggestion enhances string formatting in the `nash_battle` function using f-strings for better readability.
4
secure-code-warrior-for-github[bot] commented 6 months ago

Micro-Learning Topic: Buffer overflow (Detected by phrase)

Matched on "buffer overflow"

What is this? (2min video)

A buffer overflow condition exists when a program attempts to put more data in a buffer than it can hold or when a program attempts to put data in a memory area past a buffer.

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