[Style]: flake8 violation E721

python/ray/_private/services.py

@@ -1608,7 +1608,7 @@ def start_raylet(
     Returns:
         ProcessInfo for the process that was started.
     """
-    assert node_manager_port is not None and type(node_manager_port) == int
+    assert node_manager_port is not None and isinstance(node_manager_port, int)
 
     if use_valgrind and use_profiler:
         raise ValueError("Cannot use valgrind and profiler at the same time.")

python/ray/_private/test_utils.py

@@ -790,7 +790,7 @@ def wait_until_succeeded_without_exception(
     Return:
         Whether exception occurs within a timeout.
     """
-    if type(exceptions) != tuple:
+    if not isinstance(exceptions, tuple):
         raise Exception("exceptions arguments should be given as a tuple")
 
     time_elapsed = 0

python/ray/dag/compiled_dag_node.py

@@ -852,7 +852,7 @@ def _preprocess(self) -> None:
                         "the driver cannot participate in the NCCL group"
                     )
 
-            if type(dag_node.type_hint) == ChannelOutputType:
+            if isinstance(dag_node.type_hint, ChannelOutputType):
                 # No type hint specified by the user. Replace
                 # with the default type hint for this DAG.
                 dag_node.with_type_hint(self._default_type_hint)

python/ray/dashboard/tests/test_dashboard.py

@@ -783,7 +783,7 @@ def test_immutable_types():
     d["list"][0] = {str(i): i for i in range(1000)}
     d["dict"] = {str(i): i for i in range(1000)}
     immutable_dict = dashboard_utils.make_immutable(d)
-    assert type(immutable_dict) == dashboard_utils.ImmutableDict
+    assert isinstance(immutable_dict, dashboard_utils.ImmutableDict)
     assert immutable_dict == dashboard_utils.ImmutableDict(d)
     assert immutable_dict == d
     assert dashboard_utils.ImmutableDict(immutable_dict) == immutable_dict
@@ -801,8 +801,8 @@ def test_immutable_types():
     # Test json dumps / loads
     json_str = json.dumps(immutable_dict, cls=dashboard_optional_utils.CustomEncoder)
     deserialized_immutable_dict = json.loads(json_str)
-    assert type(deserialized_immutable_dict) == dict
-    assert type(deserialized_immutable_dict["list"]) == list
+    assert isinstance(deserialized_immutable_dict, dict)
+    assert isinstance(deserialized_immutable_dict["list"], list)
     assert immutable_dict.mutable() == deserialized_immutable_dict
     dashboard_optional_utils.rest_response(True, "OK", data=immutable_dict)
     dashboard_optional_utils.rest_response(True, "OK", **immutable_dict)
@@ -815,12 +815,12 @@ def test_immutable_types():
 
     # Test get default immutable
     immutable_default_value = immutable_dict.get("not exist list", [1, 2])
-    assert type(immutable_default_value) == dashboard_utils.ImmutableList
+    assert isinstance(immutable_default_value, dashboard_utils.ImmutableList)
 
     # Test recursive immutable
-    assert type(immutable_dict["list"]) == dashboard_utils.ImmutableList
-    assert type(immutable_dict["dict"]) == dashboard_utils.ImmutableDict
-    assert type(immutable_dict["list"][0]) == dashboard_utils.ImmutableDict
+    assert isinstance(immutable_dict["list"], dashboard_utils.ImmutableList)
+    assert isinstance(immutable_dict["dict"], dashboard_utils.ImmutableDict)
+    assert isinstance(immutable_dict["list"][0], dashboard_utils.ImmutableDict)
 
     # Test exception
     with pytest.raises(TypeError):

python/ray/data/_internal/datasource/tfrecords_datasource.py

@@ -355,7 +355,7 @@ def _cast_large_list_to_list(batch: pyarrow.Table):
 
     for column_name in old_schema.names:
         field_type = old_schema.field(column_name).type
-        if type(field_type) == pyarrow.lib.LargeListType:
+        if isinstance(field_type, pyarrow.lib.LargeListType):
             value_type = field_type.value_type
 
             if value_type == pyarrow.large_binary():

python/ray/data/tests/test_numpy_support.py

@@ -22,8 +22,9 @@ def do_map_batches(data):
 
 
 def assert_structure_equals(a, b):
-    assert type(a) == type(b), (type(a), type(b))
-    assert type(a[0]) == type(b[0]), (type(a[0]), type(b[0]))  # noqa: E721
+    # TODO: Remove noqa after flake8 being upgraded to 7.1.1
+    assert type(a) is type(b), (type(a), type(b))  # noqa E721
+    assert type(a[0]) is type(b[0]), (type(a[0]), type(b[0]))  # noqa E721
     assert a.dtype == b.dtype
     assert a.shape == b.shape
     for i in range(len(a)):

python/ray/serve/multiplex.py

@@ -173,7 +173,7 @@ async def load_model(self, model_id: str) -> Any:
             The user-constructed model object.
         """
 
-        if type(model_id) != str:
+        if not isinstance(model_id, str):
             raise TypeError("The model ID must be a string.")
 
         if not model_id:

python/ray/tests/gcp/test_gcp_tpu_command_runner.py

@@ -242,7 +242,7 @@ def test_max_active_connections_env_var():
     cmd_runner = TPUCommandRunner(**args)
     os.environ[ray_constants.RAY_TPU_MAX_CONCURRENT_CONNECTIONS_ENV_VAR] = "1"
     num_connections = cmd_runner.num_connections
-    assert type(num_connections) == int
+    assert isinstance(num_connections, int)
     assert num_connections == 1
 
 

python/ray/tests/modin/modin_test_utils.py

@@ -105,7 +105,8 @@ def df_equals(df1, df2):
     if isinstance(df1, pandas.DataFrame) and isinstance(df2, pandas.DataFrame):
         if (df1.empty and not df2.empty) or (df2.empty and not df1.empty):
             assert False, "One of the passed frames is empty, when other isn't"
-        elif df1.empty and df2.empty and type(df1) != type(df2):
+        # TODO: Remove noqa after flake8 being upgraded to 7.1.1
+        elif df1.empty and df2.empty and type(df1) is not type(df2):  # noqa E721
             assert (
                 False
             ), f"Empty frames have different types: {type(df1)} != {type(df2)}"

python/ray/tests/test_client_builder.py

@@ -51,7 +51,7 @@ def test_client(address):
     if address in ("local", None):
         assert isinstance(builder, client_builder._LocalClientBuilder)
     else:
-        assert type(builder) == client_builder.ClientBuilder
+        assert isinstance(builder, client_builder.ClientBuilder)
         assert builder.address == address.replace("ray://", "")
 
 

python/ray/tests/test_serialization.py

@@ -28,7 +28,7 @@ def is_named_tuple(cls):
     f = getattr(cls, "_fields", None)
     if not isinstance(f, tuple):
         return False
-    return all(type(n) == str for n in f)
+    return all(isinstance(n, str) for n in f)
 
 
 @pytest.mark.parametrize(
@@ -95,8 +95,8 @@ def f(x):
         # TODO(rkn): The numpy dtypes currently come back as regular integers
         # or floats.
         if type(obj).__module__ != "numpy":
-            assert type(obj) == type(new_obj_1)
-            assert type(obj) == type(new_obj_2)
+            assert isinstance(obj, type(new_obj_1))
+            assert isinstance(obj, type(new_obj_2))
 
 
 @pytest.mark.parametrize(

python/ray/tune/tests/test_sample.py

@@ -29,7 +29,7 @@ def assertDictAlmostEqual(a, b):
         assert k in b, f"Key {k} not found in {b}"
         w = b[k]
 
-        assert type(v) == type(w), f"Type {type(v)} is not {type(w)}"
+        assert isinstance(v, type(w)), f"Type {type(v)} is not {type(w)}"
 
         if isinstance(v, dict):
             assert assertDictAlmostEqual(v, w), f"Subdict {v} != {w}"

rllib/connectors/action/pipeline.py

@@ -44,8 +44,8 @@ def to_state(self):
 
     @staticmethod
     def from_state(ctx: ConnectorContext, params: Any):
-        assert (
-            type(params) == list
+        assert isinstance(
+            params, list
         ), "ActionConnectorPipeline takes a list of connector params."
         connectors = []
         for state in params:

rllib/connectors/agent/clip_reward.py

@@ -24,8 +24,8 @@ def __init__(self, ctx: ConnectorContext, sign=False, limit=None):
 
     def transform(self, ac_data: AgentConnectorDataType) -> AgentConnectorDataType:
         d = ac_data.data
-        assert (
-            type(d) == dict
+        assert isinstance(
+            d, dict
         ), "Single agent data must be of type Dict[str, TensorStructType]"
 
         if SampleBatch.REWARDS not in d:

rllib/connectors/agent/mean_std_filter.py

@@ -51,8 +51,8 @@ def __init__(
 
     def transform(self, ac_data: AgentConnectorDataType) -> AgentConnectorDataType:
         d = ac_data.data
-        assert (
-            type(d) == dict
+        assert isinstance(
+            d, dict
         ), "Single agent data must be of type Dict[str, TensorStructType]"
         if SampleBatch.OBS in d:
             d[SampleBatch.OBS] = self.filter(

rllib/connectors/agent/obs_preproc.py

@@ -44,7 +44,7 @@ def is_identity(self):
 
     def transform(self, ac_data: AgentConnectorDataType) -> AgentConnectorDataType:
         d = ac_data.data
-        assert type(d) == dict, (
+        assert isinstance(d, dict), (
             "Single agent data must be of type Dict[str, TensorStructType] but is of "
             "type {}".format(type(d))
         )

rllib/connectors/agent/pipeline.py

@@ -55,8 +55,8 @@ def to_state(self):
 
     @staticmethod
     def from_state(ctx: ConnectorContext, params: List[Any]):
-        assert (
-            type(params) == list
+        assert isinstance(
+            params, list
         ), "AgentConnectorPipeline takes a list of connector params."
         connectors = []
         for state in params:

rllib/connectors/util.py

@@ -54,7 +54,7 @@ def get_agent_connectors_from_config(
     clip_rewards = __clip_rewards(config)
     if clip_rewards is True:
         connectors.append(ClipRewardAgentConnector(ctx, sign=True))
-    elif type(clip_rewards) == float:
+    elif isinstance(clip_rewards, float):
         connectors.append(ClipRewardAgentConnector(ctx, limit=abs(clip_rewards)))
 
     if __preprocessing_enabled(config):

rllib/env/wrappers/dm_control_wrapper.py

@@ -47,10 +47,10 @@ def _spec_to_box(spec):
     def extract_min_max(s):
         assert s.dtype == np.float64 or s.dtype == np.float32
         dim = np.int_(np.prod(s.shape))
-        if type(s) == specs.Array:
+        if isinstance(s, specs.Array):
             bound = np.inf * np.ones(dim, dtype=np.float32)
             return -bound, bound
-        elif type(s) == specs.BoundedArray:
+        elif isinstance(s, specs.BoundedArray):
             zeros = np.zeros(dim, dtype=np.float32)
             return s.minimum + zeros, s.maximum + zeros
 

rllib/utils/replay_buffers/tests/test_multi_agent_mixin_replay_buffer.py

@@ -63,7 +63,7 @@ def test_mixin_sampling_episodes(self):
         for _ in range(20):
             buffer.add(batch)
             sample = buffer.sample(2)
-            assert type(sample) == MultiAgentBatch
+            assert isinstance(sample, MultiAgentBatch)
             results.append(len(sample.policy_batches[DEFAULT_POLICY_ID]))
         # One sample in the episode does not belong the the episode on thus
         # gets dropped. Full episodes are of length two.
@@ -88,7 +88,7 @@ def test_mixin_sampling_sequences(self):
         for _ in range(400):
             buffer.add(batch)
             sample = buffer.sample(10)
-            assert type(sample) == MultiAgentBatch
+            assert isinstance(sample, MultiAgentBatch)
             results.append(len(sample.policy_batches[DEFAULT_POLICY_ID]))
         self.assertAlmostEqual(np.mean(results), 2 * len(batch), delta=0.1)
 
@@ -113,7 +113,7 @@ def test_mixin_sampling_timesteps(self):
             buffer.add(batch)
             buffer.add(batch)
             sample = buffer.sample(3)
-            assert type(sample) == MultiAgentBatch
+            assert isinstance(sample, MultiAgentBatch)
             results.append(len(sample.policy_batches[DEFAULT_POLICY_ID]))
         self.assertAlmostEqual(np.mean(results), 3.0, delta=0.2)
 
@@ -125,7 +125,7 @@ def test_mixin_sampling_timesteps(self):
         for _ in range(100):
             buffer.add(batch)
             sample = buffer.sample(5)
-            assert type(sample) == MultiAgentBatch
+            assert isinstance(sample, MultiAgentBatch)
             results.append(len(sample.policy_batches[DEFAULT_POLICY_ID]))
         self.assertAlmostEqual(np.mean(results), 1.5, delta=0.2)
 
@@ -142,7 +142,7 @@ def test_mixin_sampling_timesteps(self):
         for _ in range(100):
             buffer.add(batch)
             sample = buffer.sample(10)
-            assert type(sample) == MultiAgentBatch
+            assert isinstance(sample, MultiAgentBatch)
             results.append(len(sample.policy_batches[DEFAULT_POLICY_ID]))
         self.assertAlmostEqual(np.mean(results), 10.0, delta=0.2)
 
@@ -156,12 +156,12 @@ def test_mixin_sampling_timesteps(self):
         buffer.add(batch)
         # Expect exactly 1 batch to be returned.
         sample = buffer.sample(1)
-        assert type(sample) == MultiAgentBatch
+        assert isinstance(sample, MultiAgentBatch)
         self.assertTrue(len(sample) == 1)
         # Expect exactly 0 sample to be returned (nothing new to be returned;
         # no replay allowed (replay_ratio=0.0)).
         sample = buffer.sample(1)
-        assert type(sample) == MultiAgentBatch
+        assert isinstance(sample, MultiAgentBatch)
         assert len(sample.policy_batches) == 0
         # If we insert and replay n times, expect roughly return batches of
         # len 1 (replay_ratio=0.0 -> 0% replayed samples -> 1 new and 0 old samples
@@ -170,7 +170,7 @@ def test_mixin_sampling_timesteps(self):
         for _ in range(100):
             buffer.add(batch)
             sample = buffer.sample(1)
-            assert type(sample) == MultiAgentBatch
+            assert isinstance(sample, MultiAgentBatch)
             results.append(len(sample.policy_batches[DEFAULT_POLICY_ID]))
         self.assertAlmostEqual(np.mean(results), 1.0, delta=0.2)
 
@@ -187,19 +187,19 @@ def test_mixin_sampling_timesteps(self):
         buffer.add(batch)
         # Expect exactly 1 sample to be returned (the new batch).
         sample = buffer.sample(1)
-        assert type(sample) == MultiAgentBatch
+        assert isinstance(sample, MultiAgentBatch)
         self.assertTrue(len(sample) == 1)
         # Another replay -> Expect exactly 1 sample to be returned.
         sample = buffer.sample(1)
-        assert type(sample) == MultiAgentBatch
+        assert isinstance(sample, MultiAgentBatch)
         self.assertTrue(len(sample) == 1)
         # If we replay n times, expect roughly return batches of
         # len 1 (replay_ratio=1.0 -> 100% replayed samples -> 0 new and 1 old samples
         # on average in each returned value).
         results = []
         for _ in range(100):
             sample = buffer.sample(1)
-            assert type(sample) == MultiAgentBatch
+            assert isinstance(sample, MultiAgentBatch)
             results.append(len(sample.policy_batches[DEFAULT_POLICY_ID]))
         self.assertAlmostEqual(np.mean(results), 1.0)
 

rllib/utils/replay_buffers/tests/test_multi_agent_prioritized_replay_buffer.py

@@ -186,7 +186,7 @@ def test_update_priorities(self):
 
         # Fetch records, their indices and weights.
         mabatch = buffer.sample(3)
-        assert type(mabatch) == MultiAgentBatch
+        assert isinstance(mabatch, MultiAgentBatch)
         samplebatch = mabatch.policy_batches[DEFAULT_POLICY_ID]
 
         weights = samplebatch["weights"]
@@ -211,9 +211,9 @@ def test_update_priorities(self):
         # (which still has a weight of 1.0).
         for _ in range(10):
             mabatch = buffer.sample(1000)
-            assert type(mabatch) == MultiAgentBatch
+            assert isinstance(mabatch, MultiAgentBatch)
             samplebatch = mabatch.policy_batches[DEFAULT_POLICY_ID]
-            assert type(mabatch) == MultiAgentBatch
+            assert isinstance(mabatch, MultiAgentBatch)
             indices = samplebatch["batch_indexes"]
             self.assertTrue(1900 < np.sum(indices) < 2200)
         # Test get_state/set_state.

rllib_contrib/bandit/src/rllib_bandit/env/bandit_envs_discrete.py

@@ -56,7 +56,7 @@ class LinearDiscreteEnv(gym.Env):
 
     def __init__(self, config=None):
         self.config = copy.copy(self.DEFAULT_CONFIG_LINEAR)
-        if config is not None and type(config) == dict:
+        if config is not None and isinstance(config, dict):
             self.config.update(config)
 
         self.feature_dim = self.config["feature_dim"]
@@ -128,7 +128,7 @@ class WheelBanditEnv(gym.Env):
 
     def __init__(self, config=None):
         self.config = copy.copy(self.DEFAULT_CONFIG_WHEEL)
-        if config is not None and type(config) == dict:
+        if config is not None and isinstance(config, dict):
             self.config.update(config)
 
         self.delta = self.config["delta"]

rllib_contrib/dt/tests/test_segmentation_buffer.py

@@ -89,9 +89,9 @@ def _get_internal_buffer(
     """Get the internal buffer list from the buffer. If MultiAgent then return the
     internal buffer corresponding to the given policy_id.
     """
-    if type(buffer) == SegmentationBuffer:
+    if isinstance(buffer, SegmentationBuffer):
         return buffer._buffer
-    elif type(buffer) == MultiAgentSegmentationBuffer:
+    elif isinstance(buffer, MultiAgentSegmentationBuffer):
         return buffer.buffers[policy_id]._buffer
     else:
         raise NotImplementedError
@@ -104,9 +104,9 @@ def _as_sample_batch(
     """Returns a SampleBatch. If MultiAgentBatch then return the SampleBatch
     corresponding to the given policy_id.
     """
-    if type(batch) == SampleBatch:
+    if isinstance(batch, SampleBatch):
         return batch
-    elif type(batch) == MultiAgentBatch:
+    elif isinstance(batch, MultiAgentBatch):
         return batch.policy_batches[policy_id]
     else:
         raise NotImplementedError

rllib_contrib/leela_chess_zero/src/rllib_leela_chess_zero/leela_chess_zero/leela_chess_zero_model.py

@@ -93,7 +93,7 @@ def forward(self, input_dict, state, seq_lens):
                     print(input_dict)
                     raise Exception("No observation in input_dict")
         if self.alpha_zero_obs:
-            if not type(obs) == torch.Tensor:
+            if not isinstance(obs, torch.Tensor):
                 obs = torch.from_numpy(obs.astype(np.float32))
                 action_mask = torch.from_numpy(action_mask.astype(np.float32))
             try: