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fix(pu): fix norm and init shape bug #468

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Merged
puyuan1996 merged 1 commit into from
Jan 27, 2026
Merged

fix(pu): fix norm and init shape bug #468

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16 changes: 14 additions & 2 deletions lzero/model/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -297,7 +297,11 @@ def __init__(

# Initial convolution: stride 2
self.conv1 = nn.Conv2d(observation_shape[0], out_channels // 2, kernel_size=3, stride=2, padding=1, bias=False)
self.norm1 = build_normalization(norm_type, dim=2)(out_channels // 2)
if norm_type == 'BN':
self.norm1 = nn.BatchNorm2d(out_channels // 2)
elif norm_type == 'LN':
self.norm1 = nn.LayerNorm([out_channels // 2, observation_shape[-2] // 2, observation_shape[-1] // 2],
eps=1e-5)

# Stage 1 with residual blocks
self.resblocks1 = nn.ModuleList([
Expand Down Expand Up @@ -734,7 +738,15 @@ def __init__(
self.downsample_net = DownSample(observation_shape, num_channels, activation, norm_type)
else:
self.conv = nn.Conv2d(observation_shape[0], num_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.norm = build_normalization(norm_type, dim=2)(num_channels)
if norm_type == 'BN':
self.norm = nn.BatchNorm2d(num_channels)
elif norm_type == 'LN':
if downsample:
self.norm = nn.LayerNorm(
[num_channels, math.ceil(observation_shape[-2] / 16), math.ceil(observation_shape[-1] / 16)],
eps=1e-5)
else:
self.norm = nn.LayerNorm([num_channels, observation_shape[-2], observation_shape[-1]], eps=1e-5)

self.resblocks = nn.ModuleList([
ResBlock(in_channels=num_channels, activation=activation, norm_type=norm_type, res_type='basic', bias=False)
Expand Down
22 changes: 11 additions & 11 deletions lzero/policy/unizero.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1335,10 +1335,10 @@ def _init_collect(self) -> None:
self._collect_epsilon = 0.0
self.collector_env_num = self._cfg.collector_env_num
if self._cfg.model.model_type == 'conv':
self.last_batch_obs = torch.zeros([self.collector_env_num, self._cfg.model.observation_shape[0], 64, 64]).to(self._cfg.device)
self.last_batch_obs_collect = torch.zeros([self.collector_env_num, self._cfg.model.observation_shape[0], 64, 64]).to(self._cfg.device)
self.last_batch_action_collect = [-1 for i in range(self.collector_env_num)]
elif self._cfg.model.model_type == 'mlp':
self.last_batch_obs = torch.full(
self.last_batch_obs_collect = torch.full(
[self.collector_env_num, self._cfg.model.observation_shape], fill_value=self.pad_token_id,
).to(self._cfg.device)
self.last_batch_action_collect = [-1 for i in range(self.collector_env_num)]
Expand Down Expand Up @@ -1390,7 +1390,7 @@ def _forward_collect(
output = {i: None for i in ready_env_id}

with torch.no_grad():
network_output = self._collect_model.initial_inference(self.last_batch_obs, self.last_batch_action_collect, data, timestep)
network_output = self._collect_model.initial_inference(self.last_batch_obs_collect, self.last_batch_action_collect, data, timestep)
latent_state_roots, reward_roots, pred_values, policy_logits = mz_network_output_unpack(network_output)

pred_values = self.value_inverse_scalar_transform_handle(pred_values).detach().cpu().numpy()
Expand Down Expand Up @@ -1461,7 +1461,7 @@ def _forward_collect(
}
batch_action.append(action)

self.last_batch_obs = data
self.last_batch_obs_collect = data
self.last_batch_action_collect = batch_action

# This logic is a temporary workaround specific to the muzero_segment_collector.
Expand Down Expand Up @@ -1505,10 +1505,10 @@ def _init_eval(self) -> None:
self.evaluator_env_num = self._cfg.evaluator_env_num

if self._cfg.model.model_type == 'conv':
self.last_batch_obs = torch.zeros([self.collector_env_num, self._cfg.model.observation_shape[0], 64, 64]).to(self._cfg.device)
self.last_batch_obs_eval = torch.zeros([self.collector_env_num, self._cfg.model.observation_shape[0], 64, 64]).to(self._cfg.device)
self.last_batch_action_eval = [-1 for i in range(self.collector_env_num)]
elif self._cfg.model.model_type == 'mlp':
self.last_batch_obs = torch.full(
self.last_batch_obs_eval = torch.full(
[self.collector_env_num, self._cfg.model.observation_shape], fill_value=self.pad_token_id,
).to(self._cfg.device)
self.last_batch_action_eval = [-1 for i in range(self.collector_env_num)]
Expand Down Expand Up @@ -1623,13 +1623,13 @@ def _reset_collect(self, env_id: int = None, current_steps: int = None, reset_in
- reset_init_data (:obj:`bool`, optional): Whether to reset the initial data. If True, the initial data will be reset.
"""
if reset_init_data:
self.last_batch_obs = initialize_pad_batch(
self.last_batch_obs_collect = initialize_pad_batch(
self._cfg.model.observation_shape,
self._cfg.collector_env_num,
self._cfg.device,
pad_token_id=self.pad_token_id
)
self.last_batch_action = [-1 for _ in range(self._cfg.collector_env_num)]
self.last_batch_action_collect = [-1 for _ in range(self._cfg.collector_env_num)]


# We must handle both single int and list of ints for env_id.
Expand Down Expand Up @@ -1696,7 +1696,7 @@ def _reset_eval(self, env_id: int = None, current_steps: int = None, reset_init_
self._cfg.device,
pad_token_id=self.pad_token_id
)
logging.info(f'unizero.py task_id:{task_id} after _reset_eval: last_batch_obs_eval:', self.last_batch_obs_eval.shape)
logging.info(f'unizero.py task_id:{task_id} after _reset_eval: last_batch_obs_eval:{self.last_batch_obs_eval.shape}')

else:
self.last_batch_obs_eval = initialize_pad_batch(
Expand All @@ -1705,9 +1705,9 @@ def _reset_eval(self, env_id: int = None, current_steps: int = None, reset_init_
self._cfg.device,
pad_token_id=self.pad_token_id
)
logging.info(f'unizero.py task_id:{task_id} after _reset_eval: last_batch_obs_eval:', self.last_batch_obs_eval.shape)
logging.info(f'unizero.py task_id:{task_id} after _reset_eval: last_batch_obs_eval:{self.last_batch_obs_eval.shape}')

self.last_batch_action = [-1 for _ in range(self._cfg.evaluator_env_num)]
self.last_batch_action_eval = [-1 for _ in range(self._cfg.evaluator_env_num)]

# This logic handles the crucial end-of-episode cache clearing for evaluation.
# The evaluator calls `_policy.reset([env_id])` when an episode is done.
Expand Down
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