254 lines
6.9 KiB
Python
254 lines
6.9 KiB
Python
import pytest
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param = pytest.mark.parametrize
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import torch
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@param('pred_orig_latent', (False, True))
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@param('grouped_query_attn', (False, True))
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@param('dynamics_with_video_input', (False, True))
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@param('prob_no_shortcut_train', (None, 0., 1.))
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@param('add_task_embeds', (False, True))
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@param('num_spatial_tokens', (2, 8))
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@param('signal_and_step_passed_in', (False, True))
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@param('condition_on_actions', (False, True))
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@param('add_reward_embed_to_agent_token', (False, True))
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def test_e2e(
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pred_orig_latent,
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grouped_query_attn,
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dynamics_with_video_input,
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prob_no_shortcut_train,
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add_task_embeds,
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num_spatial_tokens,
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signal_and_step_passed_in,
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condition_on_actions,
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add_reward_embed_to_agent_token
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):
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from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
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tokenizer = VideoTokenizer(
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16,
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encoder_depth = 1,
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decoder_depth = 1,
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dim_latent = 16,
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patch_size = 32,
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attn_dim_head = 16,
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num_latent_tokens = 4
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)
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video = torch.randn(2, 3, 4, 256, 256)
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loss = tokenizer(video)
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assert loss.numel() == 1
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latents = tokenizer(video, return_latents = True)
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assert latents.shape[-1] == 16
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recon = tokenizer.decode(latents, 256, 256)
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assert recon.shape == video.shape
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query_heads, heads = (16, 4) if grouped_query_attn else (8, 8)
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dynamics = DynamicsWorldModel(
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dim = 16,
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video_tokenizer = tokenizer,
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dim_latent = 16,
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max_steps = 64,
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num_tasks = 4,
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num_latent_tokens = 4,
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depth = 4,
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num_spatial_tokens = num_spatial_tokens,
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pred_orig_latent = pred_orig_latent,
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num_discrete_actions = 4,
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attn_dim_head = 16,
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attn_kwargs = dict(
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heads = heads,
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query_heads = query_heads,
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),
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prob_no_shortcut_train = prob_no_shortcut_train,
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add_reward_embed_to_agent_token = add_reward_embed_to_agent_token
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)
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signal_levels = step_sizes_log2 = None
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if signal_and_step_passed_in:
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signal_levels = torch.randint(0, 32, (2, 4))
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step_sizes_log2 = torch.randint(1, 5, (2,))
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if dynamics_with_video_input:
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dynamics_input = dict(video = video)
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else:
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dynamics_input = dict(latents = latents)
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tasks = None
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if add_task_embeds:
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tasks = torch.randint(0, 4, (2,))
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actions = None
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if condition_on_actions:
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actions = torch.randint(0, 4, (2, 4, 1))
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flow_loss = dynamics(
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**dynamics_input,
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tasks = tasks,
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signal_levels = signal_levels,
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step_sizes_log2 = step_sizes_log2,
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discrete_actions = actions
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)
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assert flow_loss.numel() == 1
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# generating
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generated_video, generated_rewards = dynamics.generate(
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time_steps = 10,
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image_height = 128,
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image_width = 128,
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batch_size = 2,
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return_rewards_per_frame = True
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)
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assert generated_video.shape == (2, 3, 10, 128, 128)
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assert generated_rewards.shape == (2, 10)
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# rl
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rewards = torch.randn((2, 4)) * 100.
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flow_loss = dynamics(
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**dynamics_input,
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tasks = tasks,
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rewards = rewards
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)
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def test_symexp_two_hot():
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import torch
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from dreamer4.dreamer4 import SymExpTwoHot
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two_hot_encoder = SymExpTwoHot(
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(-3., 3.),
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num_bins = 20,
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learned_embedding = True,
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dim_embed = 512
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)
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values = torch.randn((10))
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two_hot_encoded = two_hot_encoder(values)
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recon_values = two_hot_encoder.bins_to_scalar_value(two_hot_encoded)
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assert torch.allclose(recon_values, values, atol = 1e-6)
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reward_embeds = two_hot_encoder.embed(two_hot_encoded)
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assert reward_embeds.shape == (10, 512)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason = 'no cuda')
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@param('causal', (False, True))
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@param('softclamp_value', (50., None))
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@param('num_agent_tokens', (0, 1))
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@param('causal_block_size', (1, 8))
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@param('block_size_per_special', (1, 8))
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@param('special_attend_only_itself', (False, True))
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def test_attend_factory(
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causal,
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softclamp_value,
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num_agent_tokens,
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causal_block_size,
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block_size_per_special,
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special_attend_only_itself
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):
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from dreamer4.dreamer4 import get_attend_fn
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q = torch.randn(1, 8, 1024, 512).cuda()
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k = torch.randn(1, 4, 1024, 512).cuda()
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v = torch.randn(1, 4, 1024, 512).cuda()
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attend_kwargs = dict(
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seq_len = 1024,
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k_seq_len = 1024,
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causal = causal,
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causal_block_size = causal_block_size,
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softclamp_value = softclamp_value,
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device = q.device,
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num_agent_tokens = num_agent_tokens,
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block_size_per_special = block_size_per_special,
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special_attend_only_itself = special_attend_only_itself
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)
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attend = get_attend_fn(True, **attend_kwargs)
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flex_out = attend(q, k, v)
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attend = get_attend_fn(False, **attend_kwargs)
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out = attend(q, k, v)
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assert torch.allclose(flex_out, out, atol = 1e-6)
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def test_action_embedder():
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from dreamer4.dreamer4 import ActionEmbedder
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# 1 discrete action with 4 choices
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embedder = ActionEmbedder(
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512,
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num_discrete_actions = 4
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)
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actions = torch.randint(0, 4, (2, 3, 1))
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action_embed = embedder(discrete_actions = actions)
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assert action_embed.shape == (2, 3, 512)
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# 2 discrete actions with 4 choices each
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embedder = ActionEmbedder(
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512,
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num_discrete_actions = (4, 4)
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)
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actions = torch.randint(0, 4, (2, 3, 2))
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action_embed = embedder(discrete_actions = actions)
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assert action_embed.shape == (2, 3, 512)
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# picking out only the second discrete action
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actions = torch.randint(0, 4, (2, 3, 1))
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action_embed = embedder(discrete_actions = actions, discrete_action_types = 1)
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assert action_embed.shape == (2, 3, 512)
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# 2 continuous actions
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embedder = ActionEmbedder(
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512,
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num_continuous_actions = 2,
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continuous_norm_stats = ((0., 2.), (1., 1.)) # (mean, std) for normalizing each action
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)
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actions = torch.randn((2, 3, 2))
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action_embed = embedder(continuous_actions = actions)
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assert action_embed.shape == (2, 3, 512)
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# 2 discrete actions with 4 choices each and 2 continuous actions
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embedder = ActionEmbedder(
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512,
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num_discrete_actions = (4, 4),
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num_continuous_actions = 2
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)
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discrete_actions = torch.randint(0, 4, (2, 3, 2))
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continuous_actions = torch.randn(2, 3, 2)
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action_embed = embedder(discrete_actions = discrete_actions, continuous_actions = continuous_actions)
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assert action_embed.shape == (2, 3, 512)
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# picking out one discrete and one continuous
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discrete_actions = torch.randint(0, 4, (2, 3, 1))
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continuous_actions = torch.randn(2, 3, 1)
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action_embed = embedder(discrete_actions = discrete_actions, continuous_actions = continuous_actions, discrete_action_types = 1, continuous_action_types = 0)
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assert action_embed.shape == (2, 3, 512)
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