[Refactor] fix ruff rule C416: unnecessary-comprehension

pyproject.toml

@@ -37,7 +37,6 @@ ignore = [
     "B027",
     "B035",
     "B904",
-    "C416",
     "C419",
     # Below are auto-fixable rules
     "I001",

python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py

@@ -110,9 +110,7 @@ class ScaleRequest:
 
     def get_non_terminated(self) -> Dict[CloudInstanceId, CloudInstance]:
         self._sync_with_api_server()
-        return copy.deepcopy(
-            {id: instance for id, instance in self._cached_instances.items()}
-        )
+        return copy.deepcopy(dict(self._cached_instances))
 
     def terminate(self, ids: List[CloudInstanceId], request_id: str) -> None:
         if request_id in self._requests:

python/ray/dashboard/modules/job/common.py

@@ -373,12 +373,7 @@ async def get_job_info(job_id: str):
             job_info = await self.get_info(job_id, timeout)
             return job_id, job_info
 
-        return {
-            job_id: job_info
-            for job_id, job_info in await asyncio.gather(
-                *[get_job_info(job_id) for job_id in job_ids]
-            )
-        }
+        return dict(await asyncio.gather(*[get_job_info(job_id) for job_id in job_ids]))
 
 
 def uri_to_http_components(package_uri: str) -> Tuple[str, str]:

python/ray/dashboard/modules/metrics/metrics_head.py

@@ -283,7 +283,7 @@ def _create_default_grafana_configs(self):
         if isinstance(prometheus_headers, list):
             prometheus_header_pairs = prometheus_headers
         elif isinstance(prometheus_headers, dict):
-            prometheus_header_pairs = [(k, v) for k, v in prometheus_headers.items()]
+            prometheus_header_pairs = list(prometheus_headers.items())
 
         data_sources_path = os.path.join(grafana_provisioning_folder, "datasources")
         os.makedirs(

python/ray/data/tests/test_arrow_serialization.py

@@ -234,10 +234,7 @@ def fixed_size_list_array():
 @pytest.fixture
 def map_array():
     return pa.array(
-        [
-            [(key, item) for key, item in zip("abcdefghij", range(10))]
-            for _ in range(1000)
-        ],
+        [list(zip("abcdefghij", range(10))) for _ in range(1000)],
         type=pa.map_(pa.string(), pa.int64()),
     )
 
@@ -349,10 +346,7 @@ def complex_nested_array():
                 ]
             ),
             pa.array(
-                [
-                    [(key, item) for key, item in zip("abcdefghij", range(10))]
-                    for _ in range(1000)
-                ],
+                [list(zip("abcdefghij", range(10))) for _ in range(1000)],
                 type=pa.map_(pa.string(), pa.int64()),
             ),
         ],

python/ray/serve/grpc_util.py

@@ -18,13 +18,13 @@ def __init__(self, grpc_context: grpc._cython.cygrpc._ServicerContext):
         self._auth_context = grpc_context.auth_context()
         self._code = grpc_context.code()
         self._details = grpc_context.details()
-        self._invocation_metadata = [
+        self._invocation_metadata = [  # noqa: C416
             (key, value) for key, value in grpc_context.invocation_metadata()
         ]
         self._peer = grpc_context.peer()
         self._peer_identities = grpc_context.peer_identities()
         self._peer_identity_key = grpc_context.peer_identity_key()
-        self._trailing_metadata = [
+        self._trailing_metadata = [  # noqa: C416
             (key, value) for key, value in grpc_context.trailing_metadata()
         ]
         self._compression = None

python/ray/serve/tests/test_proxy_state.py

@@ -621,9 +621,7 @@ def test_proxy_state_manager_timing_out_on_start(number_of_worker_nodes, all_nod
         proxy_state._actor_proxy_wrapper.is_ready_response = False
 
     # Capture current proxy states (prior to updating)
-    prev_proxy_states = {
-        node_id: state for node_id, state in proxy_state_manager._proxy_states.items()
-    }
+    prev_proxy_states = dict(proxy_state_manager._proxy_states)
 
     # Trigger PSM to reconcile
     proxy_state_manager.update(proxy_nodes=node_ids)
@@ -644,9 +642,7 @@ def test_proxy_state_manager_timing_out_on_start(number_of_worker_nodes, all_nod
         proxy_state._actor_proxy_wrapper.is_ready_response = True
 
     # Capture current proxy states again (prior to updating)
-    prev_proxy_states = {
-        node_id: state for node_id, state in proxy_state_manager._proxy_states.items()
-    }
+    prev_proxy_states = dict(proxy_state_manager._proxy_states)
 
     # Trigger PSM to reconcile
     proxy_state_manager.update(proxy_nodes=node_ids)

python/ray/train/v2/_internal/execution/worker_group/worker_group.py

@@ -598,10 +598,7 @@ def poll_status(self, timeout: Optional[float] = None) -> WorkerGroupStatus:
         worker_group_status = WorkerGroupStatus(
             num_workers=len(self._workers),
             latest_start_time=self._latest_start_time,
-            worker_statuses={
-                world_rank: worker_status
-                for world_rank, worker_status in enumerate(poll_results)
-            },
+            worker_statuses=dict(enumerate(poll_results)),
         )
 
         for callback in self._callbacks:

python/ray/train/v2/tests/test_report_handler.py

@@ -38,9 +38,7 @@ def generate_worker_group_status(num_workers, num_ckpt, num_dummy, num_none):
     )
     random.shuffle(worker_statuses)
 
-    return WorkerGroupStatus(
-        num_workers, 0.0, {i: ws for i, ws in enumerate(worker_statuses)}
-    )
+    return WorkerGroupStatus(num_workers, 0.0, dict(enumerate(worker_statuses)))
 
 
 @pytest.mark.parametrize(

python/ray/tune/execution/placement_groups.py

@@ -114,7 +114,7 @@ def resource_dict_to_pg_factory(spec: Optional[Dict[str, float]] = None):
     memory = spec.pop("memory", 0.0)
 
     # If there is a custom_resources key, use as base for bundle
-    bundle = {k: v for k, v in spec.pop("custom_resources", {}).items()}
+    bundle = dict(spec.pop("custom_resources", {}))
 
     # Otherwise, consider all other keys as custom resources
     if not bundle:

python/ray/tune/search/optuna/optuna_search.py

@@ -607,9 +607,7 @@ def add_evaluated_point(
             ot_trial_state = OptunaTrialState.PRUNED
 
         if intermediate_values:
-            intermediate_values_dict = {
-                i: value for i, value in enumerate(intermediate_values)
-            }
+            intermediate_values_dict = dict(enumerate(intermediate_values))
         else:
             intermediate_values_dict = None
 

python/ray/util/client/common.py

@@ -707,7 +707,7 @@ def _get_client_id_from_context(context: Any) -> str:
     Get `client_id` from gRPC metadata. If the `client_id` is not present,
     this function logs an error and sets the status_code.
     """
-    metadata = {k: v for k, v in context.invocation_metadata()}
+    metadata = dict(context.invocation_metadata())
     client_id = metadata.get("client_id") or ""
     if client_id == "":
         logger.error("Client connecting with no client_id")

python/ray/util/client/server/dataservicer.py

@@ -32,7 +32,7 @@ def _get_reconnecting_from_context(context: Any) -> bool:
     """
     Get `reconnecting` from gRPC metadata, or False if missing.
     """
-    metadata = {k: v for k, v in context.invocation_metadata()}
+    metadata = dict(context.invocation_metadata())
     val = metadata.get("reconnecting")
     if val is None or val not in ("True", "False"):
         logger.error(
@@ -155,7 +155,7 @@ def Datapath(self, request_iterator, context):
         start_time = time.time()
         # set to True if client shuts down gracefully
         cleanup_requested = False
-        metadata = {k: v for k, v in context.invocation_metadata()}
+        metadata = dict(context.invocation_metadata())
         client_id = metadata.get("client_id")
         if client_id is None:
             logger.error("Client connecting with no client_id")

python/ray/util/client/worker.py

@@ -724,7 +724,7 @@ def get_cluster_info(
         resp = self.server.ClusterInfo(req, timeout=timeout, metadata=self.metadata)
         if resp.WhichOneof("response_type") == "resource_table":
             # translate from a proto map to a python dict
-            output_dict = {k: v for k, v in resp.resource_table.table.items()}
+            output_dict = dict(resp.resource_table.table)
             return output_dict
         elif resp.WhichOneof("response_type") == "runtime_context":
             return resp.runtime_context

rllib/algorithms/algorithm.py

@@ -903,23 +903,21 @@ def setup(self, config: AlgorithmConfig) -> None:
                 ),
             )
             # Get the devices of each learner.
-            learner_locations = [
-                (i, loc)
-                for i, loc in enumerate(
+            learner_locations = list(
+                enumerate(
                     self.learner_group.foreach_learner(
                         func=lambda _learner: (_learner.node, _learner.device),
                     )
                 )
-            ]
+            )
             # Get the devices of each AggregatorActor.
-            aggregator_locations = [
-                (i, loc)
-                for i, loc in enumerate(
+            aggregator_locations = list(
+                enumerate(
                     self._aggregator_actor_manager.foreach_actor(
                         func=lambda actor: (actor._node, actor._device)
                     )
                 )
-            ]
+            )
             self._aggregator_actor_to_learner = {}
             for agg_idx, aggregator_location in aggregator_locations:
                 for learner_idx, learner_location in learner_locations:

rllib/algorithms/marwil/tests/test_marwil.py

@@ -179,9 +179,7 @@ def possibly_masked_mean(data_):
         # Calculate our own expected values (to then compare against the
         # agent's loss output).
         module = algo.learner_group._learner.module[DEFAULT_MODULE_ID].unwrapped()
-        fwd_out = module.forward_train(
-            {k: v for k, v in batch[DEFAULT_MODULE_ID].items()}
-        )
+        fwd_out = module.forward_train(dict(batch[DEFAULT_MODULE_ID]))
         advantages = (
             batch[DEFAULT_MODULE_ID][Columns.VALUE_TARGETS].detach().cpu().numpy()
             - module.compute_values(batch[DEFAULT_MODULE_ID]).detach().cpu().numpy()
@@ -210,7 +208,7 @@ def possibly_masked_mean(data_):
         # calculation above).
         total_loss = algo.learner_group._learner.compute_loss_for_module(
             module_id=DEFAULT_MODULE_ID,
-            batch={k: v for k, v in batch[DEFAULT_MODULE_ID].items()},
+            batch=dict(batch[DEFAULT_MODULE_ID]),
             fwd_out=fwd_out,
             config=config,
         )

rllib/algorithms/sac/tests/test_sac.py

@@ -130,9 +130,7 @@ def test_sac_dict_obs_order(self):
 
         # Dict space .sample() returns an ordered dict.
         # Make sure the keys in samples are ordered differently.
-        dict_samples = [
-            {k: v for k, v in reversed(dict_space.sample().items())} for _ in range(10)
-        ]
+        dict_samples = [dict(reversed(dict_space.sample().items())) for _ in range(10)]
 
         class NestedDictEnv(gym.Env):
             def __init__(self):

rllib/env/remote_base_env.py

@@ -328,7 +328,7 @@ def stop(self) -> None:
     @override(BaseEnv)
     def get_sub_environments(self, as_dict: bool = False) -> List[EnvType]:
         if as_dict:
-            return {env_id: actor for env_id, actor in enumerate(self.actors)}
+            return dict(enumerate(self.actors))
         return self.actors
 
     @property

rllib/env/tests/test_multi_agent_episode.py

@@ -3498,10 +3498,7 @@ def _mock_multi_agent_records_from_env(
         # In the other case we need at least the last observations for the next
         # actions.
         else:
-            obs = {
-                agent_id: agent_obs
-                for agent_id, agent_obs in episode.get_observations(-1).items()
-            }
+            obs = dict(episode.get_observations(-1))
 
         # Sample `size` many records.
         done_agents = {aid for aid, t in episode.get_terminateds().items() if t}

rllib/env/vector_env.py

@@ -498,10 +498,7 @@ def get_sub_environments(self, as_dict: bool = False) -> Union[List[EnvType], di
         if not as_dict:
             return self.vector_env.get_sub_environments()
         else:
-            return {
-                _id: env
-                for _id, env in enumerate(self.vector_env.get_sub_environments())
-            }
+            return dict(enumerate(self.vector_env.get_sub_environments()))
 
     @override(BaseEnv)
     def try_render(self, env_id: Optional[EnvID] = None) -> None:

rllib/env/wrappers/open_spiel.py

@@ -61,7 +61,7 @@ def step(self, action):
                 penalties[curr_player] = -0.1
 
             # Compile rewards dict.
-            rewards = {ag: r for ag, r in enumerate(self.state.returns())}
+            rewards = dict(enumerate(self.state.returns()))
         # Simultaneous game.
         else:
             assert self.state.current_player() == -2
@@ -73,7 +73,7 @@ def step(self, action):
 
         # Compile rewards dict and add the accumulated penalties
         # (for taking invalid actions).
-        rewards = {ag: r for ag, r in enumerate(self.state.returns())}
+        rewards = dict(enumerate(self.state.returns()))
         for ag, penalty in penalties.items():
             rewards[ag] += penalty
 

rllib/examples/rl_modules/classes/modelv2_to_rlm.py

@@ -187,7 +187,7 @@ def compute_values(self, batch: Dict[str, Any], embeddings: Optional[Any] = None
     def get_initial_state(self):
         """Converts the initial state list of ModelV2 into a dict (new API stack)."""
         init_state_list = self._model_v2.get_initial_state()
-        return {i: s for i, s in enumerate(init_state_list)}
+        return dict(enumerate(init_state_list))
 
     def _translate_dist_class(self, old_dist_class):
         map_ = {

rllib/models/torch/mingpt.py

@@ -193,7 +193,7 @@ def configure_gpt_optimizer(
                 no_decay.add(fpn)
 
     # validate that we considered every parameter
-    param_dict = {pn: p for pn, p in model.named_parameters()}
+    param_dict = dict(model.named_parameters())
     inter_params = decay & no_decay
     union_params = decay | no_decay
     assert (

rllib/policy/dynamic_tf_policy.py

@@ -782,7 +782,7 @@ def _initialize_loss_from_dummy_batch(
                 {SampleBatch.SEQ_LENS: train_batch[SampleBatch.SEQ_LENS]}
             )
 
-        self._loss_input_dict.update({k: v for k, v in train_batch.items()})
+        self._loss_input_dict.update(dict(train_batch))
 
         if log_once("loss_init"):
             logger.debug(

rllib/policy/dynamic_tf_policy_v2.py

@@ -733,7 +733,7 @@ def _initialize_loss_from_dummy_batch(
                 {SampleBatch.SEQ_LENS: train_batch[SampleBatch.SEQ_LENS]}
             )
 
-        self._loss_input_dict.update({k: v for k, v in train_batch.items()})
+        self._loss_input_dict.update(dict(train_batch))
 
         if log_once("loss_init"):
             logger.debug(

rllib/policy/tf_policy.py

@@ -420,7 +420,7 @@ def compute_log_likelihoods(
                     self._state_inputs, state_batches
                 )
             )
-        builder.add_feed_dict({k: v for k, v in zip(self._state_inputs, state_batches)})
+        builder.add_feed_dict(dict(zip(self._state_inputs, state_batches)))
         if state_batches:
             builder.add_feed_dict({self._seq_lens: np.ones(len(obs_batch))})
         # Prev-a and r.