polish(pu): use different sample data for training transformer and to…

…kenizer

lzero/entry/train_muzero_gpt.py

@@ -167,30 +167,51 @@ def train_muzero_gpt(
         # remove the oldest data if the replay buffer is full.
         replay_buffer.remove_oldest_data_to_fit()
 
-        # Learn policy from collected data.
-        for i in range(update_per_collect):
-            # Learner will train ``update_per_collect`` times in one iteration.
-            if replay_buffer.get_num_of_transitions() > batch_size:
-                train_data = replay_buffer.sample(batch_size, policy)
-            else:
-                logging.warning(
-                    f'The data in replay_buffer is not sufficient to sample a mini-batch: '
-                    f'batch_size: {batch_size}, '
-                    f'{replay_buffer} '
-                    f'continue to collect now ....'
-                )
-                break
-
-            # The core train steps for MCTS+RL algorithms.
-            log_vars = learner.train(train_data, collector.envstep)
 
-            if cfg.policy.use_priority:
-                replay_buffer.update_priority(train_data, log_vars[0]['value_priority_orig'])
+        if collector.envstep > cfg.policy.transformer_start_after_envsteps:
+            # TODO：transformer tokenizer交替更新
+            # Learn policy from collected data.
+            for i in range(cfg.policy.update_per_collect_transformer):
+                # Learner will train ``update_per_collect`` times in one iteration.
+                if replay_buffer.get_num_of_transitions() > batch_size:
+                    train_data = replay_buffer.sample(batch_size, policy)
+                    train_data.append({'train_which_component':'transformer'})
+                else:
+                    logging.warning(
+                        f'The data in replay_buffer is not sufficient to sample a mini-batch: '
+                        f'batch_size: {batch_size}, '
+                        f'{replay_buffer} '
+                        f'continue to collect now ....'
+                    )
+                    break
+                # The core train steps for MCTS+RL algorithms.
+                log_vars = learner.train(train_data, collector.envstep)
+                if cfg.policy.use_priority:
+                    replay_buffer.update_priority(train_data, log_vars[0]['value_priority_orig'])
 
         # NOTE: TODO
         # TODO: for batch world model ,to improve kv reuse, we could donot reset
         policy._learn_model.world_model.past_keys_values_cache.clear()
 
+        if collector.envstep > cfg.policy.tokenizer_start_after_envsteps:
+            # Learn policy from collected data.
+            for i in range(cfg.policy.update_per_collect_tokenizer):
+                # Learner will train ``update_per_collect`` times in one iteration.
+                if replay_buffer.get_num_of_transitions() > batch_size:
+                    train_data = replay_buffer.sample(batch_size, policy)
+                    train_data.append({'train_which_component':'tokenizer'})
+                else:
+                    logging.warning(
+                        f'The data in replay_buffer is not sufficient to sample a mini-batch: '
+                        f'batch_size: {batch_size}, '
+                        f'{replay_buffer} '
+                        f'continue to collect now ....'
+                    )
+                    break
+                # The core train steps for MCTS+RL algorithms.
+                log_vars = learner.train(train_data, collector.envstep)
+
+
         if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
             break
 

lzero/entry/train_muzero_gpt_same-data.py

@@ -0,0 +1,200 @@
+import logging
+import os
+from functools import partial
+from typing import Optional, Tuple
+
+import torch
+from ding.config import compile_config
+from ding.envs import create_env_manager
+from ding.envs import get_vec_env_setting
+from ding.policy import create_policy
+from ding.utils import set_pkg_seed, get_rank
+from ding.rl_utils import get_epsilon_greedy_fn
+from ding.worker import BaseLearner
+from tensorboardX import SummaryWriter
+
+from lzero.entry.utils import log_buffer_memory_usage
+from lzero.policy import visit_count_temperature
+from lzero.policy.random_policy import LightZeroRandomPolicy
+from lzero.worker import MuZeroCollector as Collector
+from lzero.worker import MuZeroEvaluator as Evaluator
+from .utils import random_collect
+
+
+def train_muzero_gpt(
+        input_cfg: Tuple[dict, dict],
+        seed: int = 0,
+        model: Optional[torch.nn.Module] = None,
+        model_path: Optional[str] = None,
+        max_train_iter: Optional[int] = int(1e10),
+        max_env_step: Optional[int] = int(1e10),
+) -> 'Policy':  # noqa
+    """
+    Overview:
+        The train entry for MCTS+RL algorithms, including MuZero, EfficientZero, Sampled EfficientZero, Gumbel Muzero.
+    Arguments:
+        - input_cfg (:obj:`Tuple[dict, dict]`): Config in dict type.
+            ``Tuple[dict, dict]`` type means [user_config, create_cfg].
+        - seed (:obj:`int`): Random seed.
+        - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module.
+        - model_path (:obj:`Optional[str]`): The pretrained model path, which should
+            point to the ckpt file of the pretrained model, and an absolute path is recommended.
+            In LightZero, the path is usually something like ``exp_name/ckpt/ckpt_best.pth.tar``.
+        - max_train_iter (:obj:`Optional[int]`): Maximum policy update iterations in training.
+        - max_env_step (:obj:`Optional[int]`): Maximum collected environment interaction steps.
+    Returns:
+        - policy (:obj:`Policy`): Converged policy.
+    """
+
+    cfg, create_cfg = input_cfg
+    assert create_cfg.policy.type in ['efficientzero', 'muzero_gpt', 'sampled_efficientzero', 'gumbel_muzero', 'stochastic_muzero'], \
+        "train_muzero_gpt entry now only support the following algo.: 'efficientzero', 'muzero', 'sampled_efficientzero', 'gumbel_muzero'"
+
+    if create_cfg.policy.type == 'muzero_gpt':
+        from lzero.mcts import MuZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'efficientzero':
+        from lzero.mcts import EfficientZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'sampled_efficientzero':
+        from lzero.mcts import SampledEfficientZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'gumbel_muzero':
+        from lzero.mcts import GumbelMuZeroGameBuffer as GameBuffer
+    elif create_cfg.policy.type == 'stochastic_muzero':
+        from lzero.mcts import StochasticMuZeroGameBuffer as GameBuffer
+
+    if cfg.policy.cuda and torch.cuda.is_available():
+        cfg.policy.device = 'cuda'
+    else:
+        cfg.policy.device = 'cpu'
+
+    cfg = compile_config(cfg, seed=seed, env=None, auto=True, create_cfg=create_cfg, save_cfg=True)
+    # Create main components: env, policy
+    env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
+
+    collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
+    evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
+
+    collector_env.seed(cfg.seed)
+    evaluator_env.seed(cfg.seed, dynamic_seed=False)
+    set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
+
+    policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval'])
+
+    # load pretrained model
+    if model_path is not None:
+        policy.learn_mode.load_state_dict(torch.load(model_path, map_location=cfg.policy.device))
+
+    # Create worker components: learner, collector, evaluator, replay buffer, commander.
+    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) if get_rank() == 0 else None
+    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
+
+    # ==============================================================
+    # MCTS+RL algorithms related core code
+    # ==============================================================
+    policy_config = cfg.policy
+    batch_size = policy_config.batch_size
+    # specific game buffer for MCTS+RL algorithms
+    replay_buffer = GameBuffer(policy_config)
+    collector = Collector(
+        env=collector_env,
+        policy=policy.collect_mode,
+        tb_logger=tb_logger,
+        exp_name=cfg.exp_name,
+        policy_config=policy_config
+    )
+    evaluator = Evaluator(
+        eval_freq=cfg.policy.eval_freq,
+        n_evaluator_episode=cfg.env.n_evaluator_episode,
+        stop_value=cfg.env.stop_value,
+        env=evaluator_env,
+        policy=policy.eval_mode,
+        tb_logger=tb_logger,
+        exp_name=cfg.exp_name,
+        policy_config=policy_config
+    )
+
+    # ==============================================================
+    # Main loop
+    # ==============================================================
+    # Learner's before_run hook.
+    learner.call_hook('before_run')
+
+    if cfg.policy.update_per_collect is not None:
+        update_per_collect = cfg.policy.update_per_collect
+
+    # The purpose of collecting random data before training:
+    # Exploration: Collecting random data helps the agent explore the environment and avoid getting stuck in a suboptimal policy prematurely.
+    # Comparison: By observing the agent's performance during random action-taking, we can establish a baseline to evaluate the effectiveness of reinforcement learning algorithms.
+    if cfg.policy.random_collect_episode_num > 0:
+        random_collect(cfg.policy, policy, LightZeroRandomPolicy, collector, collector_env, replay_buffer)
+
+    while True:
+        log_buffer_memory_usage(learner.train_iter, replay_buffer, tb_logger)
+        collect_kwargs = {}
+        # set temperature for visit count distributions according to the train_iter,
+        # please refer to Appendix D in MuZero paper for details.
+        collect_kwargs['temperature'] = visit_count_temperature(
+            policy_config.manual_temperature_decay,
+            policy_config.fixed_temperature_value,
+            policy_config.threshold_training_steps_for_final_temperature,
+            trained_steps=learner.train_iter
+        )
+
+        if policy_config.eps.eps_greedy_exploration_in_collect:
+            epsilon_greedy_fn = get_epsilon_greedy_fn(
+                start=policy_config.eps.start,
+                end=policy_config.eps.end,
+                decay=policy_config.eps.decay,
+                type_=policy_config.eps.type
+            )
+            collect_kwargs['epsilon'] = epsilon_greedy_fn(collector.envstep)
+        else:
+            collect_kwargs['epsilon'] = 0.0
+
+        # Evaluate policy performance.
+        if evaluator.should_eval(learner.train_iter):
+            stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
+            if stop:
+                break
+
+        # Collect data by default config n_sample/n_episode.
+        new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs)
+        if cfg.policy.update_per_collect is None:
+            # update_per_collect is None, then update_per_collect is set to the number of collected transitions multiplied by the model_update_ratio.
+            collected_transitions_num = sum([len(game_segment) for game_segment in new_data[0]])
+            update_per_collect = int(collected_transitions_num * cfg.policy.model_update_ratio)
+        # save returned new_data collected by the collector
+        replay_buffer.push_game_segments(new_data)
+        # remove the oldest data if the replay buffer is full.
+        replay_buffer.remove_oldest_data_to_fit()
+
+
+        # Learn policy from collected data.
+        for i in range(update_per_collect):
+            # Learner will train ``update_per_collect`` times in one iteration.
+            if replay_buffer.get_num_of_transitions() > batch_size:
+                train_data = replay_buffer.sample(batch_size, policy)
+            else:
+                logging.warning(
+                    f'The data in replay_buffer is not sufficient to sample a mini-batch: '
+                    f'batch_size: {batch_size}, '
+                    f'{replay_buffer} '
+                    f'continue to collect now ....'
+                )
+                break
+
+            # The core train steps for MCTS+RL algorithms.
+            log_vars = learner.train(train_data, collector.envstep)
+
+            if cfg.policy.use_priority:
+                replay_buffer.update_priority(train_data, log_vars[0]['value_priority_orig'])
+
+        # NOTE: TODO
+        # TODO: for batch world model ,to improve kv reuse, we could donot reset
+        policy._learn_model.world_model.past_keys_values_cache.clear()
+
+        if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
+            break
+
+    # Learner's after_run hook.
+    learner.call_hook('after_run')
+    return policy

lzero/model/gpt_models/cfg.py

@@ -1,19 +1,20 @@
-# large net
+# small net
 cfg = {}
-
 cfg['tokenizer'] = {'_target_': 'models.tokenizer.Tokenizer',
-                    'vocab_size': 512, # TODO: for atari
-                    'embed_dim': 512,
+                    # 'vocab_size': 512, # TODO: for atari
+                    # 'embed_dim': 512,
+                    # 'vocab_size': 256,  # TODO: for atari debug
+                    # 'embed_dim': 256,
                     # 'vocab_size': 128,  # TODO: for atari debug
                     # 'embed_dim': 128,
-                         # 'vocab_size': 64,  # TODO: for atari debug
-                    # 'embed_dim': 64,
+                       'vocab_size': 64,  # TODO: for atari debug
+                    'embed_dim': 64,
                     'encoder':
-                               {'resolution': 64, 'in_channels': 3, 'z_channels': 512, 'ch': 64,
+                               {'resolution': 64, 'in_channels': 3, 'z_channels': 64, 'ch': 64,
                                 'ch_mult': [1, 1, 1, 1, 1], 'num_res_blocks': 2, 'attn_resolutions': [8, 16],
                                 'out_ch': 3, 'dropout': 0.0},# TODO：for atari debug
                             'decoder':
-                    {'resolution': 64, 'in_channels': 3, 'z_channels': 512, 'ch': 64,
+                    {'resolution': 64, 'in_channels': 3, 'z_channels': 64, 'ch': 64,
                      'ch_mult': [1, 1, 1, 1, 1], 'num_res_blocks': 2, 'attn_resolutions': [8, 16],
                      'out_ch': 3, 'dropout': 0.0}}  # TODO：for atari debug
                     # {'resolution': 64, 'in_channels': 1, 'z_channels': 512, 'ch': 64,
@@ -33,22 +34,17 @@
                       # 'num_layers': 10,# TODO：for atari
                       'num_layers': 2, # TODO：for atari debug
                       'num_heads': 4,
-                      'embed_dim': 256, # TODO：for atari
                       # 'embed_dim': 128, # TODO：for atari
-                      # 'embed_dim': 64, # TODO：for atari debug
+                      'embed_dim': 64, # TODO：for atari debug
                       'embed_pdrop': 0.1,
                       'resid_pdrop': 0.1,
                       'attn_pdrop': 0.1,
-                      "device": 'cuda:3',
+                      "device": 'cuda:0',
                     #   "device": 'cpu',
                       'support_size': 601,
                       'action_shape': 6,# TODO：for atari
-                      # 'max_cache_size':5000,
                       'max_cache_size':500,
-
                       "env_num":8,
-
                       }
-
 from easydict import EasyDict
-cfg = EasyDict(cfg)
+cfg = EasyDict(cfg)

lzero/model/gpt_models/cfg_atari.py

@@ -53,7 +53,6 @@
 
 # # mediumnet
 # cfg = {}
-
 # cfg['tokenizer'] = {'_target_': 'models.tokenizer.Tokenizer',
 #                     # 'vocab_size': 512, # TODO: for atari
 #                     # 'embed_dim': 512,
@@ -148,9 +147,6 @@
                       'action_shape': 6,# TODO：for atari
                       'max_cache_size':5000,
                       "env_num":8,
-
                       }
-
 from easydict import EasyDict
-
 cfg = EasyDict(cfg)

lzero/model/gpt_models/cfg_cartpole.py

@@ -42,6 +42,8 @@
 from easydict import EasyDict
 cfg = EasyDict(cfg)
 
+
+
 # # mediumnet
 # cfg = {}
 # cfg['tokenizer'] = {'_target_': 'models.tokenizer.Tokenizer',