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import pytest
import torch
from models.ctm import ContinuousThoughtMachine
from models.ctm_qamnist import ContinuousThoughtMachineQAMNIST
from utils.samplers import QAMNISTSampler
from tasks.qamnist.utils import get_dataset
from tests.test_data import *
from utils.housekeeping import set_seed
from tasks.rl.train import Agent
from types import SimpleNamespace
# --- Housekeeping ---
@pytest.fixture
def device():
return torch.device("cpu")
@pytest.fixture
def seed():
return 42
@pytest.fixture(autouse=True)
def auto_set_seed(seed):
set_seed(seed)
# --- Golden Test Fixtures ---
# ------ Parity ------
@pytest.fixture
def golden_test_params_parity(parity_params):
parity_length = 4
parity_small_params = parity_params.copy()
parity_small_params["out_dims"] = 2 * parity_length
parity_small_params["prediction_reshaper"] = [parity_length, 2]
return parity_small_params
@pytest.fixture
def golden_test_model_parity(golden_test_params_parity, device):
return ContinuousThoughtMachine(**golden_test_params_parity).to(device)
@pytest.fixture
def golden_test_input_parity(device):
return GOLDEN_TEST_INPUT_PARITY.to(device)
@pytest.fixture
def golden_test_expected_predictions_parity(device):
return GOLDEN_TEST_EXPECTED_PREDICTIONS_PARITY.to(device)
@pytest.fixture
def golden_test_expected_certainties_parity(device):
return GOLDEN_TEST_EXPECTED_CERTAINTIES_PARITY.to(device)
@pytest.fixture
def golden_test_expected_synchronization_out_tracking_parity():
return GOLDEN_TEST_EXPECTED_SYNCH_OUT_TRACKING_PARITY
@pytest.fixture
def golden_test_expected_synchronization_action_tracking_parity():
return GOLDEN_TEST_EXPECTED_SYNCH_ACTION_TRACKING_PARITY
@pytest.fixture
def golden_test_expected_pre_activations_tracking_parity():
return GOLDEN_TEST_EXPECTED_PRE_ACTIVATIONS_TRACKING_PARITY
@pytest.fixture
def golden_test_expected_post_activations_tracking_parity():
return GOLDEN_TEST_EXPECTED_POST_ACTIVATIONS_TRACKING_PARITY
@pytest.fixture
def golden_test_expected_attentions_tracking_parity():
return GOLDEN_TEST_EXPECTED_ATTENTIONS_TRACKING_PARITY
# ------ QAMNIST ------
@pytest.fixture
def golden_test_params_qamnist(base_params):
params = base_params.copy()
params.pop("backbone_type")
params.pop("positional_embedding_type")
params["iterations_per_digit"] = 1
params["iterations_per_question_part"] = 1
params["iterations_for_answering"] = 1
return params
@pytest.fixture
def golden_test_model_qamnist(golden_test_params_qamnist, device):
return ContinuousThoughtMachineQAMNIST(**golden_test_params_qamnist).to(device)
@pytest.fixture
def golden_test_input_qamnist(device):
q_num_images = 2
q_num_images_delta = 0
q_num_repeats_per_input = 1
q_num_operations = 2
q_num_operations_delta = 0
batch_size = 1
train_data, _, _, _, _ = get_dataset(
q_num_images=q_num_images,
q_num_images_delta=q_num_images_delta,
q_num_repeats_per_input=q_num_repeats_per_input,
q_num_operations=q_num_operations,
q_num_operations_delta=q_num_operations_delta,
)
sampler = QAMNISTSampler(train_data, batch_size=batch_size)
loader = torch.utils.data.DataLoader(train_data, batch_sampler=sampler, num_workers=0)
inputs, z, _, _ = next(iter(loader))
inputs = inputs.to(device)
z = torch.stack(z, 1).to(device)
return inputs, z
@pytest.fixture
def golden_test_expected_predictions_qamnist(device):
return GOLDEN_TEST_EXPECTED_PREDICTIONS_QAMNIST.to(device)
@pytest.fixture
def golden_test_expected_certainties_qamnist(device):
return GOLDEN_TEST_EXPECTED_CERTAINTIES_QAMNIST.to(device)
@pytest.fixture
def golden_test_expected_synchronization_out_tracking_qamnist(device):
return GOLDEN_TEST_EXPECTED_SYNCH_OUT_TRACKING_QAMNIST.to(device)
@pytest.fixture
def golden_test_expected_synchronization_action_tracking_qamnist():
return GOLDEN_TEST_EXPECTED_SYNCH_ACTION_TRACKING_QAMNIST
@pytest.fixture
def golden_test_expected_pre_activations_tracking_qamnist():
return GOLDEN_TEST_EXPECTED_PRE_ACTIVATIONS_TRACKING_QAMNIST
@pytest.fixture
def golden_test_expected_post_activations_tracking_qamnist():
return GOLDEN_TEST_EXPECTED_POST_ACTIVATIONS_TRACKING_QAMNIST
@pytest.fixture
def golden_test_expected_attentions_tracking_qamnist():
return GOLDEN_TEST_EXPECTED_ATTENTIONS_TRACKING_QAMNIST
@pytest.fixture
def golden_test_expected_embeddings_tracking_qamnist():
return GOLDEN_TEST_EXPECTED_EMBEDDINGS_TRACKING_QAMNIST
# ------ RL (CartPole) ------
@pytest.fixture
def golden_test_params_rl(base_params):
params = base_params.copy()
params.pop("heads")
params.pop("n_synch_action")
params.pop("out_dims")
params.pop("n_random_pairing_self")
params.pop("positional_embedding_type")
return params
@pytest.fixture
def golden_test_model_rl(golden_test_params_rl, device):
args = SimpleNamespace(
env_id="CartPole-v1",
model_type="ctm",
continuous_state_trace=True,
iterations=golden_test_params_rl['iterations'],
d_model=golden_test_params_rl['d_model'],
d_input=golden_test_params_rl['d_input'],
n_synch_out=golden_test_params_rl['n_synch_out'],
synapse_depth=golden_test_params_rl['synapse_depth'],
memory_length=golden_test_params_rl['memory_length'],
deep_memory=golden_test_params_rl['deep_nlms'],
memory_hidden_dims=golden_test_params_rl['memory_hidden_dims'],
do_normalisation=golden_test_params_rl['do_layernorm_nlm'],
dropout=golden_test_params_rl.get('dropout', 0),
neuron_select_type=golden_test_params_rl.get('neuron_select_type', 'first-last'),
)
size_action_space = 2
model = Agent(size_action_space, args, device).to(device)
return model
@pytest.fixture()
def environment_to_test():
return "cartpole"
@pytest.fixture
def golden_test_inputs_rl(environment_to_test):
if environment_to_test != "cartpole":
raise NotImplementedError("RL test only tests cartpole.")
observations = torch.tensor([[ 0.01, 0.02, 0.03, 0.04],], dtype=torch.float32)
return observations
@pytest.fixture
def golden_test_expected_initial_state_trace_rl(device):
return GOLDEN_TEST_EXPECTED_INITIAL_STATE_TRACE_RL.to(device)
@pytest.fixture
def golden_test_expected_initial_activated_state_trace_rl(device):
return GOLDEN_TEST_EXPECTED_INITIAL_ACTIVATED_STATE_TRACE_RL.to(device)
@pytest.fixture
def golden_test_expected_action_rl(device):
return GOLDEN_TEST_EXPECTED_ACTION_RL.to(device)
@pytest.fixture
def golden_test_expected_action_log_prob_rl(device):
return GOLDEN_TEST_EXPECTED_ACTION_LOG_PROB_RL.to(device)
@pytest.fixture
def golden_test_expected_action_entropy_rl(device):
return GOLDEN_TEST_EXPECTED_ENTROPY_RL.to(device)
@pytest.fixture
def golden_test_expected_value_rl(device):
return GOLDEN_TEST_EXPECTED_VALUE_RL.to(device)
@pytest.fixture
def golden_test_expected_state_trace_rl(device):
return GOLDEN_TEST_EXPECTED_STATE_TRACE_RL.to(device)
@pytest.fixture
def golden_test_expected_activated_state_trace_rl(device):
return GOLDEN_TEST_EXPECTED_ACTIVATED_STATE_TRACE_RL.to(device)
@pytest.fixture
def golden_test_expected_action_logits_rl(device):
return GOLDEN_TEST_EXPECTED_ACTION_LOGITS_RL.to(device)
@pytest.fixture
def golden_test_expected_action_probs_rl(device):
return GOLDEN_TEST_EXPECTED_ACTION_PROBS_RL.to(device)
@pytest.fixture
def golden_test_expected_pre_activations_tracking_rl():
return GOLDEN_TEST_EXPECTED_PRE_ACTIVATIONS_TRACKING_RL
@pytest.fixture
def golden_test_expected_post_activations_tracking_rl():
return GOLDEN_TEST_EXPECTED_POST_ACTIVATIONS_TRACKING_RL
@pytest.fixture
def golden_test_expected_synch_out_tracking_rl():
return GOLDEN_TEST_SYNCH_OUT_TRACKING_RL
# --- Parity Fixtures ---
@pytest.fixture
def base_params():
return dict(
iterations=10,
d_model=32,
d_input=4,
heads=2,
n_synch_out=3,
n_synch_action=3,
synapse_depth=1,
memory_length=5,
deep_nlms=True,
memory_hidden_dims=2,
do_layernorm_nlm=False,
backbone_type="none",
positional_embedding_type="none",
out_dims=10,
prediction_reshaper=[-1],
dropout=0.0,
neuron_select_type="first-last",
n_random_pairing_self=0,
)
@pytest.fixture
def parity_input(device):
batch_size = 4
parity_length = 64
return torch.randint(0, 2, (batch_size, parity_length), dtype=torch.float32, device=device) * 2 - 1
@pytest.fixture
def ctm_factory():
def _create_model(base_config, **overrides):
config = base_config.copy()
config.update(overrides)
return ContinuousThoughtMachine(**config)
return _create_model
@pytest.fixture
def parity_params(base_params):
parity_length = 64
parity_params = base_params.copy()
parity_params["backbone_type"] = "parity_backbone"
parity_params["positional_embedding_type"] = "custom-rotational-1d"
parity_params["out_dims"] = 2 * parity_length
parity_params["prediction_reshaper"] = [parity_length, 2]
return parity_params
@pytest.fixture
def parity_ctm_model(parity_params, device):
return ContinuousThoughtMachine(**parity_params).to(device)
# --- QAMNIST Fixtures ---
@pytest.fixture
def qamnist_params():
return dict(
iterations=1,
d_model=1024,
d_input=64,
heads=4,
n_synch_out=32,
n_synch_action=32,
synapse_depth=1,
memory_length=10,
deep_nlms=True,
memory_hidden_dims=16,
do_layernorm_nlm=True,
out_dims=10,
prediction_reshaper=[-1],
dropout=0.0,
neuron_select_type="first-last",
iterations_per_digit=10,
iterations_per_question_part=10,
iterations_for_answering=10,
n_random_pairing_self=0,
)
@pytest.fixture
def qamnist_input(device):
q_num_images = 3
q_num_images_delta = 2
q_num_repeats_per_input = 10
q_num_operations = 3
q_num_operations_delta = 2
batch_size = 4
train_data, _, _, _, _ = get_dataset(
q_num_images=q_num_images,
q_num_images_delta=q_num_images_delta,
q_num_repeats_per_input=q_num_repeats_per_input,
q_num_operations=q_num_operations,
q_num_operations_delta=q_num_operations_delta,
)
sampler = QAMNISTSampler(train_data, batch_size=batch_size)
loader = torch.utils.data.DataLoader(train_data, batch_sampler=sampler, num_workers=0)
inputs, z, _, _ = next(iter(loader))
inputs = inputs.to(device)
z = torch.stack(z, 1).to(device)
return inputs, z
@pytest.fixture
def qamnist_model_factory(qamnist_params):
def _create_model(neuron_select_type):
return ContinuousThoughtMachineQAMNIST(
**{**qamnist_params, "neuron_select_type": neuron_select_type}
)
return _create_model
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