handle vectorized env

.github/workflows/test.yml

@@ -5,11 +5,11 @@ jobs:
   build:
 
     runs-on: ubuntu-latest
-    timeout-minutes: 60
+    timeout-minutes: 20
     strategy:
       fail-fast: false
       matrix:
-        group: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
+        group: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
 
     steps:
     - uses: actions/checkout@v4
@@ -24,4 +24,4 @@ jobs:
         python -m uv pip install -e .[test]
     - name: Test with pytest
       run: |
-        python -m pytest --num-shards 20 --shard-id ${{ matrix.group }} tests/
+        python -m pytest --num-shards 10 --shard-id ${{ matrix.group }} tests/

README.md

@@ -1,99 +1,10 @@
 <img src="./dreamer4-fig2.png" width="400px"></img>
 
-## Dreamer 4
+## Dreamer 4 (wip)
 
 Implementation of Danijar's [latest iteration](https://arxiv.org/abs/2509.24527v1) for his [Dreamer](https://danijar.com/project/dreamer4/) line of work
 
-[Discord channel](https://discord.gg/PmGR7KRwxq) for collaborating with other researchers interested in this work
+[Temporary Discord](https://discord.gg/MkACrrkrYR)
-
-## Appreciation
-
-- [@dirkmcpherson](https://github.com/dirkmcpherson) for fixes to typo errors and unpassed arguments!
-
-## Install
-
-```bash
-$ pip install dreamer4
-```
-
-## Usage
-
-```python
-import torch
-from dreamer4 import VideoTokenizer, DynamicsWorldModel
-
-# video tokenizer, learned through MAE + lpips
-
-tokenizer = VideoTokenizer(
-    dim = 512,
-    dim_latent = 32,
-    patch_size = 32,
-    image_height = 256,
-    image_width = 256
-)
-
-video = torch.randn(2, 3, 10, 256, 256)
-
-# learn the tokenizer
-
-loss = tokenizer(video)
-loss.backward()
-
-# dynamics world model
-
-world_model = DynamicsWorldModel(
-    dim = 512,
-    dim_latent = 32,
-    video_tokenizer = tokenizer,
-    num_discrete_actions = 4,
-    num_residual_streams = 1
-)
-
-# state, action, rewards
-
-video = torch.randn(2, 3, 10, 256, 256)
-discrete_actions = torch.randint(0, 4, (2, 10, 1))
-rewards = torch.randn(2, 10)
-
-# learn dynamics / behavior cloned model
-
-loss = world_model(
-    video = video,
-    rewards = rewards,
-    discrete_actions = discrete_actions
-)
-
-loss.backward()
-
-# do the above with much data
-
-# then generate dreams
-
-dreams = world_model.generate(
-    10,
-    batch_size = 2,
-    return_decoded_video = True,
-    return_for_policy_optimization = True
-)
-
-# learn from the dreams
-
-actor_loss, critic_loss = world_model.learn_from_experience(dreams)
-
-(actor_loss + critic_loss).backward()
-
-# learn from environment
-
-from dreamer4.mocks import MockEnv
-
-mock_env = MockEnv((256, 256), vectorized = True, num_envs = 4)
-
-experience = world_model.interact_with_env(mock_env, max_timesteps = 8, env_is_vectorized = True)
-
-actor_loss, critic_loss = world_model.learn_from_experience(experience)
-
-(actor_loss + critic_loss).backward()
-```
 
 ## Citation
 
@@ -108,5 +19,3 @@ actor_loss, critic_loss = world_model.learn_from_experience(experience)
     url     = {https://arxiv.org/abs/2509.24527}, 
 }
 ```
-
-*the conquest of nature is to be achieved through number and measure - angels to Descartes in a dream*

dreamer4/__init__.py

@@ -1,7 +1,6 @@
 from dreamer4.dreamer4 import (
     VideoTokenizer,
-    DynamicsWorldModel,
+    DynamicsWorldModel
-    AxialSpaceTimeTransformer
 )
 
 

dreamer4/mocks.py

@@ -7,11 +7,6 @@ from torch.nn import Module
 
 from einops import repeat
 
-# helpers
-
-def exists(v):
-    return v is not None
-
 # mock env
 
 class MockEnv(Module):
@@ -20,11 +15,7 @@ class MockEnv(Module):
         image_shape,
         reward_range = (-100, 100),
         num_envs = 1,
-        vectorized = False,
+        vectorized = False
-        terminate_after_step = None,
-        rand_terminate_prob = 0.05,
-        can_truncate = False,
-        rand_truncate_prob = 0.05,
     ):
         super().__init__()
         self.image_shape = image_shape
@@ -34,15 +25,6 @@ class MockEnv(Module):
         self.vectorized = vectorized
         assert not (vectorized and num_envs == 1)
 
-        # mocking termination and truncation
-
-        self.can_terminate = exists(terminate_after_step)
-        self.terminate_after_step = terminate_after_step
-        self.rand_terminate_prob = rand_terminate_prob
-
-        self.can_truncate = can_truncate
-        self.rand_truncate_prob = rand_truncate_prob
-
         self.register_buffer('_step', tensor(0))
 
     def get_random_state(self):
@@ -68,30 +50,13 @@ class MockEnv(Module):
 
         reward = empty(()).uniform_(*self.reward_range)
 
-        if self.vectorized:
+        if not self.vectorized:
-            discrete, continuous = actions
+            return state, reward
-            assert discrete.shape[0] == self.num_envs, f'expected batch of actions for {self.num_envs} environments'
 
-            state = repeat(state, '... -> b ...', b = self.num_envs)
+        discrete, continuous = actions
-            reward = repeat(reward, ' -> b', b = self.num_envs)
+        assert discrete.shape[0] == self.num_envs, f'expected batch of actions for {self.num_envs} environments'
 
-        out = (state, reward)
+        state = repeat(state, '... -> b ...', b = self.num_envs)
+        reward = repeat(reward, ' -> b', b = self.num_envs)
 
-
+        return state, reward
-        if self.can_terminate:
-            shape = (self.num_envs,) if self.vectorized else (1,)
-            valid_step = self._step > self.terminate_after_step
-
-            terminate = (torch.rand(shape) < self.rand_terminate_prob) & valid_step
-
-            out = (*out, terminate)
-
-            # maybe truncation
-
-            if self.can_truncate:
-                truncate = (torch.rand(shape) < self.rand_truncate_prob) & valid_step & ~terminate
-                out = (*out, truncate)
-
-        self._step.add_(1)
-
-        return out

dreamer4/trainers.py

@@ -4,7 +4,7 @@ import torch
 from torch import is_tensor
 from torch.nn import Module
 from torch.optim import AdamW
-from torch.utils.data import Dataset, TensorDataset, DataLoader
+from torch.utils.data import Dataset, DataLoader
 
 from accelerate import Accelerator
 
@@ -12,9 +12,7 @@ from adam_atan2_pytorch import MuonAdamAtan2
 
 from dreamer4.dreamer4 import (
     VideoTokenizer,
-    DynamicsWorldModel,
+    DynamicsWorldModel
-    Experience,
-    combine_experiences
 )
 
 from ema_pytorch import EMA
@@ -287,7 +285,7 @@ class DreamTrainer(Module):
         for _ in range(self.num_train_steps):
 
             dreams = self.unwrapped_model.generate(
-                self.generate_timesteps + 1, # plus one for bootstrap value
+                self.generate_timesteps,
                 batch_size = self.batch_size,
                 return_rewards_per_frame = True,
                 return_agent_actions = True,
@@ -319,221 +317,3 @@ class DreamTrainer(Module):
             self.value_head_optim.zero_grad()
 
         self.print('training complete')
-
-# training from sim
-
-class SimTrainer(Module):
-    def __init__(
-        self,
-        model: DynamicsWorldModel,
-        optim_klass = AdamW,
-        batch_size = 16,
-        generate_timesteps = 16,
-        learning_rate = 3e-4,
-        max_grad_norm = None,
-        epochs = 2,
-        weight_decay = 0.,
-        accelerate_kwargs: dict = dict(),
-        optim_kwargs: dict = dict(),
-        cpu = False,
-    ):
-        super().__init__()
-
-        self.accelerator = Accelerator(
-            cpu = cpu,
-            **accelerate_kwargs
-        )
-
-        self.model = model
-
-        optim_kwargs = dict(
-            lr = learning_rate,
-            weight_decay = weight_decay
-        )
-
-        self.policy_head_optim = AdamW(model.policy_head_parameters(), **optim_kwargs)
-        self.value_head_optim = AdamW(model.value_head_parameters(), **optim_kwargs)
-
-        self.max_grad_norm = max_grad_norm
-
-        self.epochs = epochs
-        self.batch_size = batch_size
-
-        self.generate_timesteps = generate_timesteps
-
-        self.unwrapped_model = self.model
-
-        (
-            self.model,
-            self.policy_head_optim,
-            self.value_head_optim,
-        ) = self.accelerator.prepare(
-            self.model,
-            self.policy_head_optim,
-            self.value_head_optim
-        )
-
-    @property
-    def device(self):
-        return self.accelerator.device
-
-    @property
-    def unwrapped_model(self):
-        return self.accelerator.unwrap_model(self.model)
-
-    def print(self, *args, **kwargs):
-        return self.accelerator.print(*args, **kwargs)
-
-    def learn(
-        self,
-        experience: Experience
-    ):
-
-        step_size = experience.step_size
-        agent_index = experience.agent_index
-
-        latents = experience.latents
-        old_values = experience.values
-        rewards = experience.rewards
-
-        has_agent_embed = exists(experience.agent_embed)
-        agent_embed = experience.agent_embed
-
-        discrete_actions, continuous_actions = experience.actions
-        discrete_log_probs, continuous_log_probs = experience.log_probs
-
-        discrete_old_action_unembeds, continuous_old_action_unembeds = default(experience.old_action_unembeds, (None, None))
-
-        # handle empties
-
-        empty_tensor = torch.empty_like(rewards)
-
-        agent_embed = default(agent_embed, empty_tensor)
-
-        has_discrete = exists(discrete_actions)
-        has_continuous = exists(continuous_actions)
-
-        discrete_actions = default(discrete_actions, empty_tensor)
-        continuous_actions = default(continuous_actions, empty_tensor)
-
-        discrete_log_probs = default(discrete_log_probs, empty_tensor)
-        continuous_log_probs = default(continuous_log_probs, empty_tensor)
-
-        discrete_old_action_unembeds = default(discrete_old_action_unembeds, empty_tensor)
-        continuous_old_action_unembeds = default(discrete_old_action_unembeds, empty_tensor)
-
-        # create the dataset and dataloader
-
-        dataset = TensorDataset(
-            latents,
-            discrete_actions,
-            continuous_actions,
-            discrete_log_probs,
-            continuous_log_probs,
-            agent_embed,
-            discrete_old_action_unembeds,
-            continuous_old_action_unembeds,
-            old_values,
-            rewards
-        )
-
-        dataloader = DataLoader(dataset, batch_size = self.batch_size, shuffle = True)
-
-        for epoch in range(self.epochs):
-
-            for (
-                latents,
-                discrete_actions,
-                continuous_actions,
-                discrete_log_probs,
-                continuous_log_probs,
-                agent_embed,
-                discrete_old_action_unembeds,
-                continuous_old_action_unembeds,
-                old_values,
-                rewards
-            ) in dataloader:
-
-                actions = (
-                    discrete_actions if has_discrete else None,
-                    continuous_actions if has_continuous else None
-                )
-
-                log_probs = (
-                    discrete_log_probs if has_discrete else None,
-                    continuous_log_probs if has_continuous else None
-                )
-
-                old_action_unembeds = (
-                    discrete_old_action_unembeds if has_discrete else None,
-                    continuous_old_action_unembeds if has_continuous else None
-                )
-
-                batch_experience = Experience(
-                    latents = latents,
-                    actions = actions,
-                    log_probs = log_probs,
-                    agent_embed = agent_embed if has_agent_embed else None,
-                    old_action_unembeds = old_action_unembeds,
-                    values = old_values,
-                    rewards = rewards,
-                    step_size = step_size,
-                    agent_index = agent_index
-                )
-
-                policy_head_loss, value_head_loss = self.model.learn_from_experience(batch_experience)
-
-                self.print(f'policy head loss: {policy_head_loss.item():.3f} | value head loss: {value_head_loss.item():.3f}')
-
-                # update policy head
-
-                self.accelerator.backward(policy_head_loss)
-
-                if exists(self.max_grad_norm):
-                    self.accelerator.clip_grad_norm_(self.model.policy_head_parameters()(), self.max_grad_norm)
-
-                self.policy_head_optim.step()
-                self.policy_head_optim.zero_grad()
-
-                # update value head
-
-                self.accelerator.backward(value_head_loss)
-
-                if exists(self.max_grad_norm):
-                    self.accelerator.clip_grad_norm_(self.model.value_head_parameters(), self.max_grad_norm)
-
-                self.value_head_optim.step()
-                self.value_head_optim.zero_grad()
-
-        self.print('training complete')
-
-    def forward(
-        self,
-        env,
-        num_episodes = 50000,
-        max_experiences_before_learn = 8,
-        env_is_vectorized = False
-    ):
-
-        for _ in range(num_episodes):
-
-            total_experience = 0
-            experiences = []
-
-            while total_experience < max_experiences_before_learn:
-
-                experience = self.unwrapped_model.interact_with_env(env, env_is_vectorized = env_is_vectorized)
-
-                num_experience = experience.video.shape[0]
-
-                total_experience += num_experience
-
-                experiences.append(experience.cpu())
-
-            combined_experiences = combine_experiences(experiences)
-
-            self.learn(combined_experiences)
-
-            experiences.clear()
-
-        self.print('training complete')

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.1.24"
+version = "0.0.64"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }
@@ -36,8 +36,7 @@ dependencies = [
     "hyper-connections>=0.2.1",
     "torch>=2.4",
     "torchvision",
-    "x-mlps-pytorch>=0.0.29",
+    "x-mlps-pytorch>=0.0.29"
-    "vit-pytorch>=1.15.3"
 ]
 
 [project.urls]

tests/test_dreamer.py

@@ -2,9 +2,6 @@ import pytest
 param = pytest.mark.parametrize
 import torch
 
-def exists(v):
-    return v is not None
-
 @param('pred_orig_latent', (False, True))
 @param('grouped_query_attn', (False, True))
 @param('dynamics_with_video_input', (False, True))
@@ -15,9 +12,7 @@ def exists(v):
 @param('condition_on_actions', (False, True))
 @param('num_residual_streams', (1, 4))
 @param('add_reward_embed_to_agent_token', (False, True))
-@param('add_state_pred_head', (False, True))
+@param('use_time_kv_cache', (False, True))
-@param('use_time_cache', (False, True))
-@param('var_len', (False, True))
 def test_e2e(
     pred_orig_latent,
     grouped_query_attn,
@@ -29,9 +24,7 @@ def test_e2e(
     condition_on_actions,
     num_residual_streams,
     add_reward_embed_to_agent_token,
-    add_state_pred_head,
+    use_time_kv_cache
-    use_time_cache,
-    var_len
 ):
     from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
 
@@ -43,9 +36,7 @@ def test_e2e(
         patch_size = 32,
         attn_dim_head = 16,
         num_latent_tokens = 4,
-        num_residual_streams = num_residual_streams,
+        num_residual_streams = num_residual_streams
-        encoder_add_decor_aux_loss = True,
-        decorr_sample_frac = 1.
     )
 
     video = torch.randn(2, 3, 4, 256, 256)
@@ -73,13 +64,12 @@ def test_e2e(
         pred_orig_latent = pred_orig_latent,
         num_discrete_actions = 4,
         attn_dim_head = 16,
-        attn_heads = heads,
         attn_kwargs = dict(
+            heads = heads,
             query_heads = query_heads,
         ),
         prob_no_shortcut_train = prob_no_shortcut_train,
         add_reward_embed_to_agent_token = add_reward_embed_to_agent_token,
-        add_state_pred_head = add_state_pred_head,
         num_residual_streams = num_residual_streams
     )
 
@@ -102,13 +92,8 @@ def test_e2e(
     if condition_on_actions:
         actions = torch.randint(0, 4, (2, 3, 1))
 
-    lens = None
-    if var_len:
-        lens = torch.randint(1, 4, (2,))
-
     flow_loss = dynamics(
         **dynamics_input,
-        lens = lens,
         tasks = tasks,
         signal_levels = signal_levels,
         step_sizes_log2 = step_sizes_log2,
@@ -126,7 +111,7 @@ def test_e2e(
         image_width = 128,
         batch_size = 2,
         return_rewards_per_frame = True,
-        use_time_cache = use_time_cache
+        use_time_kv_cache = use_time_kv_cache
     )
 
     assert generations.video.shape == (2, 3, 10, 128, 128)
@@ -351,15 +336,6 @@ def test_action_embedder():
     assert discrete_logits.shape == (2, 3, 8)
     assert continuous_mean_log_var.shape == (2, 3, 2, 2)
 
-    # test kl div
-
-    discrete_logits_tgt, continuous_mean_log_var_tgt = embedder.unembed(action_embed)
-
-    discrete_kl_div, continuous_kl_div = embedder.kl_div((discrete_logits, continuous_mean_log_var), (discrete_logits_tgt, continuous_mean_log_var_tgt))
-
-    assert discrete_kl_div.shape == (2, 3)
-    assert continuous_kl_div.shape == (2, 3)
-
     # return discrete split by number of actions
 
     discrete_logits, continuous_mean_log_var = embedder.unembed(action_embed, return_split_discrete = True)
@@ -421,14 +397,14 @@ def test_mtp():
 
     reward_targets, mask = create_multi_token_prediction_targets(rewards, 3) # say three token lookahead
 
-    assert reward_targets.shape == (3, 16, 3)
+    assert reward_targets.shape == (3, 15, 3)
-    assert mask.shape == (3, 16, 3)
+    assert mask.shape == (3, 15, 3)
 
     actions = torch.randint(0, 10, (3, 16, 2))
     action_targets, mask = create_multi_token_prediction_targets(actions, 3)
 
-    assert action_targets.shape == (3, 16, 3, 2)
+    assert action_targets.shape == (3, 15, 3, 2)
-    assert mask.shape == (3, 16, 3)
+    assert mask.shape == (3, 15, 3)
 
     from dreamer4.dreamer4 import ActionEmbedder
 
@@ -620,22 +596,12 @@ def test_cache_generate():
         num_residual_streams = 1
     )
 
-    generated, time_cache = dynamics.generate(1, return_time_cache = True)
+    generated, time_kv_cache = dynamics.generate(1, return_time_kv_cache = True)
-    generated, time_cache = dynamics.generate(1, time_cache = time_cache, return_time_cache = True)
+    generated, time_kv_cache = dynamics.generate(1, time_kv_cache = time_kv_cache, return_time_kv_cache = True)
-    generated, time_cache = dynamics.generate(1, time_cache = time_cache, return_time_cache = True)
+    generated, time_kv_cache = dynamics.generate(1, time_kv_cache = time_kv_cache, return_time_kv_cache = True)
 
 @param('vectorized', (False, True))
-@param('use_pmpo', (False, True))
+def test_online_rl(vectorized):
-@param('env_can_terminate', (False, True))
-@param('env_can_truncate', (False, True))
-@param('store_agent_embed', (False, True))
-def test_online_rl(
-    vectorized,
-    use_pmpo,
-    env_can_terminate,
-    env_can_truncate,
-    store_agent_embed
-):
     from dreamer4.dreamer4 import DynamicsWorldModel, VideoTokenizer
 
     tokenizer = VideoTokenizer(
@@ -646,9 +612,7 @@ def test_online_rl(
         dim_latent = 16,
         patch_size = 32,
         attn_dim_head = 16,
-        num_latent_tokens = 1,
+        num_latent_tokens = 1
-        image_height = 256,
-        image_width = 256,
     )
 
     world_model_and_policy = DynamicsWorldModel(
@@ -669,163 +633,11 @@ def test_online_rl(
     )
 
     from dreamer4.mocks import MockEnv
-    from dreamer4.dreamer4 import combine_experiences
+    mock_env = MockEnv((256, 256), vectorized = vectorized, num_envs = 4)
 
-    mock_env = MockEnv(
+    one_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 16, env_is_vectorized = vectorized)
-        (256, 256),
-        vectorized = vectorized,
-        num_envs = 4,
-        terminate_after_step = 2 if env_can_terminate else None,
-        can_truncate = env_can_truncate,
-        rand_terminate_prob = 0.1
-    )
 
-    # manually
+    actor_loss, critic_loss = world_model_and_policy.learn_from_experience(one_experience)
-
-    dream_experience = world_model_and_policy.generate(10, batch_size = 1, store_agent_embed = store_agent_embed, return_for_policy_optimization = True)
-
-    one_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 8, env_is_vectorized = vectorized, store_agent_embed = store_agent_embed)
-    another_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 16, env_is_vectorized = vectorized, store_agent_embed = store_agent_embed)
-
-    combined_experience = combine_experiences([dream_experience, one_experience, another_experience])
-
-    # quick test moving the experience to different devices
-
-    if torch.cuda.is_available():
-        combined_experience = combined_experience.to(torch.device('cuda'))
-        combined_experience = combined_experience.to(world_model_and_policy.device)
-
-    if store_agent_embed:
-        assert exists(combined_experience.agent_embed)
-
-    actor_loss, critic_loss = world_model_and_policy.learn_from_experience(combined_experience, use_pmpo = use_pmpo)
 
     actor_loss.backward()
     critic_loss.backward()
-
-    # with trainer
-
-    from dreamer4.trainers import SimTrainer
-
-    trainer = SimTrainer(
-        world_model_and_policy,
-        batch_size = 4,
-        cpu = True
-    )
-
-    trainer(mock_env, num_episodes = 2, env_is_vectorized = vectorized)
-
-@param('num_video_views', (1, 2))
-def test_proprioception(
-    num_video_views
-):
-    from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
-
-    tokenizer = VideoTokenizer(
-        512,
-        dim_latent = 32,
-        patch_size = 32,
-        encoder_depth = 2,
-        decoder_depth = 2,
-        time_block_every = 2,
-        attn_heads = 8,
-        image_height = 256,
-        image_width = 256,
-        attn_kwargs = dict(
-            query_heads = 16
-        )
-    )
-
-    dynamics = DynamicsWorldModel(
-        512,
-        num_agents = 1,
-        video_tokenizer = tokenizer,
-        dim_latent = 32,
-        dim_proprio = 21,
-        num_tasks = 4,
-        num_video_views = num_video_views,
-        num_discrete_actions = 4,
-        num_residual_streams = 1
-    )
-
-    if num_video_views > 1:
-        video_shape = (2, num_video_views, 3, 10, 256, 256)
-    else:
-        video_shape = (2, 3, 10, 256, 256)
-
-    video = torch.randn(*video_shape)
-    rewards = torch.randn(2, 10)
-    proprio = torch.randn(2, 10, 21)
-    discrete_actions = torch.randint(0, 4, (2, 10, 1))
-    tasks = torch.randint(0, 4, (2,))
-
-    loss = dynamics(
-        video = video,
-        rewards = rewards,
-        tasks = tasks,
-        proprio = proprio,
-        discrete_actions = discrete_actions
-    )
-
-    loss.backward()
-
-    generations = dynamics.generate(
-        10,
-        batch_size = 2,
-        return_decoded_video = True
-    )
-
-    assert exists(generations.proprio)
-    assert generations.video.shape == video_shape
-
-def test_epo():
-    from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
-
-    tokenizer = VideoTokenizer(
-        512,
-        dim_latent = 32,
-        patch_size = 32,
-        encoder_depth = 2,
-        decoder_depth = 2,
-        time_block_every = 2,
-        attn_heads = 8,
-        image_height = 256,
-        image_width = 256,
-        attn_kwargs = dict(
-            query_heads = 16
-        )
-    )
-
-    dynamics = DynamicsWorldModel(
-        512,
-        num_agents = 1,
-        video_tokenizer = tokenizer,
-        dim_latent = 32,
-        dim_proprio = 21,
-        num_tasks = 4,
-        num_latent_genes = 16,
-        num_discrete_actions = 4,
-        num_residual_streams = 1
-    )
-
-    fitness = torch.randn(16,)
-    dynamics.evolve_(fitness)
-
-def test_images_to_video_tokenizer():
-    import torch
-    from dreamer4 import VideoTokenizer, DynamicsWorldModel, AxialSpaceTimeTransformer
-
-    tokenizer = VideoTokenizer(
-        dim = 512,
-        dim_latent = 32,
-        patch_size = 32,
-        image_height = 256,
-        image_width = 256,
-        encoder_add_decor_aux_loss = True
-    )
-
-    images = torch.randn(2, 3, 256, 256)
-    loss, (losses, recon_images) = tokenizer(images, return_intermediates = True)
-    loss.backward()
-
-    assert images.shape == recon_images.shape