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Merged
copybara-service merged 1 commit into from
Oct 7, 2026
Merged

Replace # pytype: disable suppressions with # pyrefly: ignore #43

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24 changes: 12 additions & 12 deletions iris/algorithms/ars_algorithm_test.py
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Expand Up @@ -41,12 +41,12 @@ def test_ars_gradient(self, orthogonal_suggestions, quasirandom_suggestions):
suggestions = algo.get_param_suggestions()
self.assertLen(suggestions, 6)
eval_results = [
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.empty(0), 0),
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.empty(0), 0),
]
algo.process_evaluations(eval_results)
np.testing.assert_array_equal(algo._opt_params, np.array([10, 11]))
Expand All @@ -63,12 +63,12 @@ def test_ars_gradient_with_schedule(self):
suggestions = algo.get_param_suggestions()
self.assertLen(suggestions, 6)
eval_results = [
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.empty(0), 0),
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.empty(0), 0),
]
algo.process_evaluations(eval_results)
np.testing.assert_array_equal(algo._opt_params, np.array([10, 11]))
Expand Down
2 changes: 1 addition & 1 deletion iris/algorithms/cma_algorithm_test.py
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Expand Up @@ -54,7 +54,7 @@ def test_cma_optimization(self):
np.array(suggestion['params_to_eval']),
test_fn(np.array(suggestion['params_to_eval']))))
if i%10 == 0:
eval_results[0] = worker_util.EvaluationResult(np.empty(0), 0) # pytype: disable=wrong-arg-types # numpy-scalars
eval_results[0] = worker_util.EvaluationResult(np.empty(0), 0)
self.algo.process_evaluations(eval_results)
np.testing.assert_almost_equal(self.algo._opt_params, _TRUE_OPTIMAL)

Expand Down
6 changes: 3 additions & 3 deletions iris/algorithms/es_enas_algorithm.py
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Expand Up @@ -90,7 +90,7 @@ def proper_unserialize(metadata: str) -> pg.DNA:
return dna

if self._multithreading:
dna_list = self._pool.map(proper_unserialize, eval_metadatas) # pytype:disable=attribute-error
dna_list = self._pool.map(proper_unserialize, eval_metadatas)
else:
dna_list = map(proper_unserialize, eval_metadatas)
dna_list = list(dna_list)
Expand All @@ -108,7 +108,7 @@ def get_param_suggestions(self,
# Note that for faster serialization, DNASpec is removed from DNA.
dna_list = [self._controller.propose_dna() for _ in vanilla_suggestions]
if self._multithreading:
metadata_list = self._pool.map(pg.to_json_str, dna_list) # pytype:disable=attribute-error
metadata_list = self._pool.map(pg.to_json_str, dna_list)
else:
metadata_list = map(pg.to_json_str, dna_list)
metadata_list = list(metadata_list)
Expand All @@ -130,7 +130,7 @@ def _get_state(self) -> Dict[str, Any]:
return vanilla_state

def _set_state(self, new_state: Dict[str, Any]) -> None:
super()._set_state(new_state) # pytype: disable=attribute-error
super()._set_state(new_state)
self._interval_counter = new_state["interval_counter"]
self._dna_spec = pg.from_json_str(new_state["serialized_dna_spec"])
self._controller = self._controller_fn(dna_spec=self._dna_spec)
Expand Down
3 changes: 1 addition & 2 deletions iris/algorithms/es_enas_algorithm_test.py
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Expand Up @@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.

# pytype: disable=attribute-error
from gym import spaces
from iris.algorithms import es_enas_algorithm
from iris.policies import nas_policy
Expand Down Expand Up @@ -44,7 +43,7 @@ def make_evaluation_results(suggestion_list):
value=np.random.uniform(),
metadata=suggestion['metadata'])
eval_results.append(evaluation_result)
eval_results.append(worker_util.EvaluationResult(np.empty(0), 0)) # pytype: disable=wrong-arg-types # numpy-scalars
eval_results.append(worker_util.EvaluationResult(np.empty(0), 0))
return eval_results


Expand Down
16 changes: 8 additions & 8 deletions iris/algorithms/multi_agent_ars_algorithm_test.py
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Expand Up @@ -49,42 +49,42 @@ def test_init(self, agent_keys, expected_agent_keys, expected_num_agents):

def _build_evaluation_results(self) -> list[worker_util.EvaluationResult]:
eval_results = [
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.array([10.0, 11.0, 12.0, 13.0]),
value=10,
metrics={'reward_arm': 10, 'reward_opp': -5},
),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.array([10.0, 11.0, 14.0, 15.0]),
value=10,
metrics={'reward_arm': 10, 'reward_opp': -10},
),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.empty(0),
value=0,
metrics={'reward_arm': 0, 'reward_opp': 0},
),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.array([1.0, 2.0, 3.0, 4.0]),
value=10,
metrics={'reward_arm': 10, 'reward_opp': -10},
),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.array([10.0, 11.0, 12.0, 13.0]),
value=-10,
metrics={'reward_arm': -10, 'reward_opp': 5},
),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.array([10.0, 11.0, 14.0, 15.0]),
value=-10,
metrics={'reward_arm': -10, 'reward_opp': 10},
),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.array([5.0, 6.0, 7.0, 8.0]),
value=-10,
metrics={'reward_arm': -10, 'reward_opp': 10},
),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(
params_evaluated=np.empty(0),
value=0,
metrics={'reward_arm': 0, 'reward_opp': 0},
Expand Down
2 changes: 1 addition & 1 deletion iris/algorithms/optimizers.py
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Expand Up @@ -87,7 +87,7 @@ def vector_decoding_function(A, b, optimization_parameters, loss_function):
result = x.value
res_list = []
for i in range(n):
res_list.append(result[i]) # pyrefly: ignore[unsupported-operation]
res_list.append(result[i])
return np.array(res_list)


Expand Down
30 changes: 18 additions & 12 deletions iris/algorithms/pes_algorithm_test.py
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Expand Up @@ -37,18 +37,24 @@ def test_pes_gradient(self, orthogonal_suggestions, quasirandom_suggestions):
init_state = {'init_params': np.array([10., 10.])}
algo.initialize(init_state)
eval_results = [
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
np.array([10., 11.]), 10, metrics={'current_step': 5}),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
np.empty(0), 0, metrics={'current_step': 5}),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
np.array([10., 11.]), 10, metrics={'current_step': 5}),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
np.array([10., 9.]), -10, metrics={'current_step': 5}),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
np.array([10., 9.]), -10, metrics={'current_step': 5}),
worker_util.EvaluationResult( # pytype: disable=wrong-arg-types # numpy-scalars
np.empty(0), 0, metrics={'current_step': 5}),
worker_util.EvaluationResult(
np.array([10.0, 11.0]), 10, metrics={'current_step': 5}
),
worker_util.EvaluationResult(
np.empty(0), 0, metrics={'current_step': 5}
),
worker_util.EvaluationResult(
np.array([10.0, 11.0]), 10, metrics={'current_step': 5}
),
worker_util.EvaluationResult(
np.array([10.0, 9.0]), -10, metrics={'current_step': 5}
),
worker_util.EvaluationResult(
np.array([10.0, 9.0]), -10, metrics={'current_step': 5}
),
worker_util.EvaluationResult(
np.empty(0), 0, metrics={'current_step': 5}
),
]
algo.process_evaluations(eval_results)
np.testing.assert_array_equal(algo._opt_params, np.array([10, 11]))
Expand Down
20 changes: 10 additions & 10 deletions iris/algorithms/piars_algorithm.py
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Expand Up @@ -207,8 +207,8 @@ def __init__(
else:
self.policy = policy

obs_spec = gym_wrapper.spec_from_gym_space(self._env.observation_space) # pyrefly: ignore[bad-argument-type]
action_spec = gym_wrapper.spec_from_gym_space(self._env.action_space) # pyrefly: ignore[bad-argument-type]
obs_spec = gym_wrapper.spec_from_gym_space(self._env.observation_space)
action_spec = gym_wrapper.spec_from_gym_space(self._env.action_space)
time_step_spec = ts.time_step_spec(observation_spec=obs_spec)
policy_step_spec = policy_step.PolicyStep(action=action_spec) # pyrefly: ignore[missing-argument]
collect_data_spec = trajectory.from_transition(
Expand Down Expand Up @@ -325,14 +325,14 @@ def train_single_step(self, obs, reward, action, discount):
@tf.function
def rollout(self, obs, actions):
"""Latent rollout."""
s, _ = self.policy.h_model(obs) # pyrefly: ignore[not-callable]
s, _ = self.policy.h_model(obs)
outputs = []
for i in range(self._rollout_length):
p, v = self.policy.f_model(s) # pyrefly: ignore[not-callable]
u_next, s_next = self.policy.g_model([s, actions[:, i, ...]]) # pyrefly: ignore[not-callable]
p, v = self.policy.f_model(s)
u_next, s_next = self.policy.g_model([s, actions[:, i, ...]])
outputs.append((p, v, u_next, s))
s = s_next
p, v = self.policy.f_model(s) # pyrefly: ignore[not-callable]
p, v = self.policy.f_model(s)
outputs.append((p, v, None, s))
return outputs

Expand Down Expand Up @@ -368,11 +368,11 @@ def infonce(hidden_x, hidden_y, temperature=0.1):
# Latent state (from visual + other observations) for the first time step
hx = latent_traj[0][-1]
# Latent state (from visual observations) for the last time step
_, hy_vision = self.policy.h_model(obs_k) # pyrefly: ignore[not-callable]
_, hy_vision = self.policy.h_model(obs_k)
# A trick from https://arxiv.org/abs/2011.10566
hy_vision = tf.stop_gradient(hy_vision)
zx = self.policy.px_model(hx) # pyrefly: ignore[not-callable]
zy = self.policy.py_model(hy_vision) # pyrefly: ignore[not-callable]
zx = self.policy.px_model(hx)
zy = self.policy.py_model(hy_vision)
iyz, _, _ = infonce(zx, zy, temperature=0.1)
loss_pi = -iyz

Expand Down Expand Up @@ -455,7 +455,7 @@ def flatten_nested(space, x):
"""Flatten nested."""
if isinstance(space, spaces.Box):
x = np.asarray(x, dtype=np.float32)
inner_dims = list(space.shape) # pyrefly: ignore[bad-argument-type]
inner_dims = list(space.shape)
outer_dims = list(x.shape)[: -len(inner_dims)]
x = np.reshape(x, outer_dims + [np.prod(inner_dims)])
return x
Expand Down
8 changes: 4 additions & 4 deletions iris/algorithms/pyglove_algorithm.py
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Expand Up @@ -70,7 +70,7 @@ def proper_unserialize(metadata: str) -> pg.DNA:
return dna

if self._multithreading:
dna_list = self._pool.map(proper_unserialize, eval_metadatas) # pytype:disable=attribute-error
dna_list = self._pool.map(proper_unserialize, eval_metadatas)
else:
dna_list = map(proper_unserialize, eval_metadatas)
dna_list = list(dna_list)
Expand All @@ -89,7 +89,7 @@ def get_param_suggestions(self,
]
# Note that for faster serialization, DNASpec is removed from DNA.
if self._multithreading:
metadata_list = self._pool.map(pg.to_json_str, dna_list) # pytype:disable=attribute-error
metadata_list = self._pool.map(pg.to_json_str, dna_list)
else:
metadata_list = map(pg.to_json_str, dna_list)

Expand All @@ -104,8 +104,8 @@ def get_param_suggestions(self,

def _get_state(self) -> Dict[str, Any]:
vanilla_state = {}
vanilla_state["serialized_dna_spec"] = pg.to_json_str(self._dna_spec) # pytype:disable=attribute-error
vanilla_state["controller_alg_state"] = self._controller.get_state() # pytype:disable=attribute-error
vanilla_state["serialized_dna_spec"] = pg.to_json_str(self._dna_spec)
vanilla_state["controller_alg_state"] = self._controller.get_state()
return vanilla_state

def _set_state(self, new_state: Dict[str, Any]) -> None:
Expand Down
4 changes: 2 additions & 2 deletions iris/algorithms/pyribs_algorithm_test.py
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Expand Up @@ -208,7 +208,7 @@ def test_process_evaluations(self):
worker_util.EvaluationResult(
params_evaluated=np.ones((13,)),
value=1,
obs_norm_buffer_data={ # pyrefly: ignore[bad-argument-type]
obs_norm_buffer_data={
buffer.N: 1, # pyrefly: ignore[bad-assignment]
buffer.STD: np.ones((8,)),
buffer.MEAN: np.ones((8,)),
Expand All @@ -219,7 +219,7 @@ def test_process_evaluations(self):
worker_util.EvaluationResult(
params_evaluated=np.ones((13,) * 2),
value=2,
obs_norm_buffer_data={ # pyrefly: ignore[bad-argument-type]
obs_norm_buffer_data={
buffer.N: 2, # pyrefly: ignore[bad-assignment]
buffer.STD: np.ones((8,)) * 2,
buffer.MEAN: np.ones((8,)) * 2,
Expand Down
32 changes: 16 additions & 16 deletions iris/algorithms/rbo_algorithm_test.py
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Expand Up @@ -32,14 +32,14 @@ def test_rbo_gradient(self):
init_state = {'init_params': np.array([10., 10.])}
algo.initialize(init_state)
eval_results = [
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.empty(0), 0),
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.empty(0), 0),
]
algo.process_evaluations(eval_results)
np.testing.assert_array_almost_equal(
Expand Down Expand Up @@ -67,14 +67,14 @@ def test_rbo_gradient_2(self, regression_method, orthogonal_suggestions,
init_state = {'init_params': np.array([10., 10.])}
algo.initialize(init_state)
eval_results = [
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 11.]), 10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10., 9.]), -10), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.empty(0), 0), # pytype: disable=wrong-arg-types # numpy-scalars
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.empty(0), 0),
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 11.0]), 10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.array([10.0, 9.0]), -10),
worker_util.EvaluationResult(np.empty(0), 0),
]
algo.process_evaluations(eval_results)
np.testing.assert_equal(len(algo._opt_params), 2)
Expand Down
4 changes: 2 additions & 2 deletions iris/policies/gym_space_utils.py
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Expand Up @@ -38,8 +38,8 @@ def filter_space(space: gym.Space,
def extend_space(space: gym.Space, key: str, value: gym.Space):
"""Adds new keys or dimensions to the space."""
if isinstance(space, gym.spaces.Box):
low = np.concatenate((space.low, value.low)) # pyrefly: ignore[missing-attribute]
high = np.concatenate((space.high, value.high)) # pyrefly: ignore[missing-attribute]
low = np.concatenate((space.low, value.low))
high = np.concatenate((space.high, value.high))
return gym.spaces.Box(low=low, high=high)
elif isinstance(space, gym.spaces.Dict):
extended_space = dict(space.spaces)
Expand Down
8 changes: 5 additions & 3 deletions iris/policies/implicit_policy.py
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Expand Up @@ -438,8 +438,9 @@ def act(self, state: np.ndarray) -> np.ndarray:
else:
phi_state = self._energy.linearized_energy_state(state)
if self._bootstrapped_samples == self._num_samples:
return self._actions[np.argmax(
np.dot(self._lat_reps_for_actions, phi_state))] # pyrefly: ignore[bad-argument-type, no-matching-overload]
return self._actions[
np.argmax(np.dot(self._lat_reps_for_actions, phi_state)) # pyrefly: ignore[no-matching-overload]
]
else:
random_indices = np.random.choice(np.arange(len(self._actions)))
return self._actions[random_indices[np.argmax(
Expand Down Expand Up @@ -522,7 +523,8 @@ def act(self, state: np.ndarray) -> np.ndarray:
base_prefix_sum = self._prefix_sum_table[seg_start_index - 1]
prob = np.dot( # pyrefly: ignore[no-matching-overload]
self._prefix_sum_table[seg_end_index - 1] - base_prefix_sum,
phi_state) # pyrefly: ignore[bad-argument-type]
phi_state,
)
probs.append(prob)
start_end_indices.append([seg_start_index, seg_end_index])
seg_start_index = seg_end_index
Expand Down
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