[Retiarii] Coding style improvements for pylint and flake8 (#3190)

nni/retiarii/codegen/pytorch.py

 import logging
-from typing import *
+from typing import List
 
 from ..graph import IllegalGraphError, Edge, Graph, Node, Model
-from ..operation import Operation, Cell
 
 _logger = logging.getLogger(__name__)
 
 
-
-def model_to_pytorch_script(model: Model, placement = None) -> str:
+def model_to_pytorch_script(model: Model, placement=None) -> str:
     graphs = []
     total_pkgs = set()
     for name, cell in model.graphs.items():
-        import_pkgs, graph_code = graph_to_pytorch_model(name, cell, placement = placement)
+        import_pkgs, graph_code = graph_to_pytorch_model(name, cell, placement=placement)
         graphs.append(graph_code)
         total_pkgs.update(import_pkgs)
     pkgs_code = '\n'.join(['import {}'.format(pkg) for pkg in total_pkgs])
     return _PyTorchScriptTemplate.format(pkgs_code, '\n\n'.join(graphs)).strip()
 
+
 def _sorted_incoming_edges(node: Node) -> List[Edge]:
     edges = [edge for edge in node.graph.edges if edge.tail is node]
-    _logger.info('sorted_incoming_edges: {}'.format(edges))
+    _logger.info('sorted_incoming_edges: %s', str(edges))
     if not edges:
         return []
-    _logger.info(f'all tail_slots are None: {[edge.tail_slot for edge in edges]}')
+    _logger.info('all tail_slots are None: %s', str([edge.tail_slot for edge in edges]))
     if all(edge.tail_slot is None for edge in edges):
         return edges
     if all(isinstance(edge.tail_slot, int) for edge in edges):
@@ -32,6 +31,7 @@ def _sorted_incoming_edges(node: Node) -> List[Edge]:
             return edges
     raise IllegalGraphError(node.graph, 'Node {} has bad inputs'.format(node.name))
 
+
 def _format_inputs(node: Node) -> List[str]:
     edges = _sorted_incoming_edges(node)
     inputs = []
@@ -53,6 +53,7 @@ def _format_inputs(node: Node) -> List[str]:
                 inputs.append('{}[{}]'.format(edge.head.name, edge.head_slot))
     return inputs
 
+
 def _remove_prefix(names, graph_name):
     """
     variables name (full name space) is too long,
@@ -69,14 +70,14 @@ def _remove_prefix(names, graph_name):
     else:
         return names[len(graph_name):] if names.startswith(graph_name) else names
 
-def graph_to_pytorch_model(graph_name: str, graph: Graph, placement = None) -> str:
+
+def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str:
     nodes = graph.topo_sort()
 
     # handle module node and function node differently
     # only need to generate code for module here
     import_pkgs = set()
     node_codes = []
-    placement_codes = []
     for node in nodes:
         if node.operation:
             pkg_name = node.operation.get_import_pkg()

nni/retiarii/converter/__init__.py

 from .graph_gen import convert_to_graph
-from .visualize import visualize_model
\ No newline at end of file

nni/retiarii/converter/graph_gen.py

-import json_tricks
 import logging
 import re
-import torch
 
-from ..graph import Graph, Node, Edge, Model
-from ..operation import Cell, Operation
-from ..nn.pytorch import Placeholder, LayerChoice, InputChoice
+import torch
 
-from .op_types import MODULE_EXCEPT_LIST, OpTypeName, BasicOpsPT
-from .utils import build_full_name, _convert_name
+from ..graph import Graph, Model, Node
+from ..nn.pytorch import InputChoice, LayerChoice, Placeholder
+from ..operation import Cell
+from .op_types import MODULE_EXCEPT_LIST, BasicOpsPT, OpTypeName
+from .utils import _convert_name, build_full_name
 
 _logger = logging.getLogger(__name__)
 
@@ -16,6 +15,7 @@ global_seq = 0
 global_graph_id = 0
 modules_arg = None
 
+
 def _add_edge(ir_graph, node, graph_inputs, node_index, new_node, output_remap, ignore_first=False):
     """
     Parameters
@@ -76,6 +76,7 @@ def _add_edge(ir_graph, node, graph_inputs, node_index, new_node, output_remap,
 
         new_node_input_idx += 1
 
+
 def create_prim_constant_node(ir_graph, node, module_name):
     global global_seq
     attrs = {}
@@ -86,14 +87,17 @@ def create_prim_constant_node(ir_graph, node, module_name):
                                  node.kind(), attrs)
     return new_node
 
+
 def handle_prim_attr_node(node):
     assert node.hasAttribute('name')
     attrs = {'name': node.s('name'), 'input': node.inputsAt(0).debugName()}
     return node.kind(), attrs
 
+
 def _remove_mangle(module_type_str):
     return re.sub('\\.___torch_mangle_\\d+', '', module_type_str)
 
+
 def remove_unconnected_nodes(ir_graph, targeted_type=None):
     """
     Parameters
@@ -122,6 +126,7 @@ def remove_unconnected_nodes(ir_graph, targeted_type=None):
     for hidden_node in to_removes:
         hidden_node.remove()
 
+
 def handle_graph_nodes(script_module, sm_graph, module, module_name, ir_model, ir_graph):
     """
     Convert torch script node to our node ir, and build our graph ir
@@ -248,7 +253,8 @@ def handle_graph_nodes(script_module, sm_graph, module, module_name, ir_model, i
                     # therefore, we do this check for a module. example below:
                     # %25 : __torch__.xxx = prim::GetAttr[name="input_switch"](%self)
                     # %27 : Tensor = prim::CallMethod[name="forward"](%25, %out.1)
-                    assert submodule_name in script_module._modules, "submodule_name: {} not in script_module {}".format(submodule_name, script_module._modules.keys())
+                    assert submodule_name in script_module._modules, "submodule_name: {} not in script_module {}".format(
+                        submodule_name, script_module._modules.keys())
 
                     submodule_full_name = build_full_name(module_name, submodule_name)
                     submodule_obj = getattr(module, submodule_name)
@@ -350,6 +356,7 @@ def handle_graph_nodes(script_module, sm_graph, module, module_name, ir_model, i
 
     return node_index
 
+
 def merge_aten_slices(ir_graph):
     """
     if there is aten::slice node, merge the consecutive ones together.
@@ -408,13 +415,14 @@ def refine_graph(ir_graph):
     remove_unconnected_nodes(ir_graph, targeted_type='prim::GetAttr')
     merge_aten_slices(ir_graph)
 
+
 def _handle_layerchoice(module):
     global modules_arg
 
     m_attrs = {}
     candidates = module.candidate_ops
     choices = []
-    for i, cand in enumerate(candidates):
+    for cand in candidates:
         assert id(cand) in modules_arg, 'id not exist: {}'.format(id(cand))
         assert isinstance(modules_arg[id(cand)], dict)
         cand_type = '__torch__.' + cand.__class__.__module__ + '.' + cand.__class__.__name__
@@ -423,6 +431,7 @@ def _handle_layerchoice(module):
     m_attrs['label'] = module.label
     return m_attrs
 
+
 def _handle_inputchoice(module):
     m_attrs = {}
     m_attrs['n_chosen'] = module.n_chosen
@@ -430,6 +439,7 @@ def _handle_inputchoice(module):
     m_attrs['label'] = module.label
     return m_attrs
 
+
 def convert_module(script_module, module, module_name, ir_model):
     """
     Convert a module to its graph ir (i.e., Graph) along with its input arguments
@@ -507,6 +517,7 @@ def convert_module(script_module, module, module_name, ir_model):
     # should not be parsed further.
     return ir_graph, {}
 
+
 def convert_to_graph(script_module, module, recorded_modules_arg):
     """
     Convert module to our graph ir, i.e., build a ```Model``` type

nni/retiarii/converter/op_types.py

@@ -16,6 +16,7 @@ class OpTypeName(str, Enum):
     Placeholder = 'Placeholder'
     MergedSlice = 'MergedSlice'
 
+
 # deal with aten op
 BasicOpsPT = {
     'aten::mean': 'Mean',

nni/retiarii/converter/utils.py

@@ -6,6 +6,7 @@ def build_full_name(prefix, name, seq=None):
     else:
         return '{}__{}{}'.format(prefix, name, str(seq))
 
+
 def _convert_name(name: str) -> str:
     """
     Convert the names using separator '.' to valid variable name in code

nni/retiarii/converter/visualize.py

 import graphviz
 
+
 def convert_to_visualize(graph_ir, vgraph):
     for name, graph in graph_ir.items():
         if name == '_training_config':
@@ -33,6 +34,7 @@ def convert_to_visualize(graph_ir, vgraph):
                     dst = cell_node[dst][0]
                 subgraph.edge(src, dst)
 
+
 def visualize_model(graph_ir):
     vgraph = graphviz.Digraph('G', filename='vgraph', format='jpg')
     convert_to_visualize(graph_ir, vgraph)

nni/retiarii/execution/api.py

 import time
 import os
-import importlib.util
-from typing import *
+from typing import List
 
 from ..graph import Model, ModelStatus
 from .base import BaseExecutionEngine
 from .cgo_engine import CGOExecutionEngine
-from .interface import *
+from .interface import AbstractExecutionEngine, WorkerInfo
 from .listener import DefaultListener
 
 _execution_engine = None

nni/retiarii/execution/base.py

 import logging
-from typing import *
+from typing import Dict, Any, List
 
 from .interface import AbstractExecutionEngine, AbstractGraphListener, WorkerInfo
 from .. import codegen, utils
@@ -61,16 +61,16 @@ class BaseExecutionEngine(AbstractExecutionEngine):
 
     def _send_trial_callback(self, paramater: dict) -> None:
         for listener in self._listeners:
-            _logger.warning('resources: {}'.format(listener.resources))
+            _logger.warning('resources: %s', listener.resources)
             if not listener.has_available_resource():
                 _logger.warning('There is no available resource, but trial is submitted.')
             listener.on_resource_used(1)
-            _logger.warning('on_resource_used: {}'.format(listener.resources))
+            _logger.warning('on_resource_used: %s', listener.resources)
 
     def _request_trial_jobs_callback(self, num_trials: int) -> None:
         for listener in self._listeners:
             listener.on_resource_available(1 * num_trials)
-            _logger.warning('on_resource_available: {}'.format(listener.resources))
+            _logger.warning('on_resource_available: %s', listener.resources)
 
     def _trial_end_callback(self, trial_id: int, success: bool) -> None:
         model = self._running_models[trial_id]

nni/retiarii/execution/cgo_engine.py

 import logging
-import json 
-from typing import *
+from typing import List, Dict, Tuple
 
 from .interface import AbstractExecutionEngine, AbstractGraphListener, WorkerInfo
 from .. import codegen, utils
@@ -12,8 +11,10 @@ from .logical_optimizer.opt_dedup_input import DedupInputOptimizer
 from .base import BaseGraphData
 
 _logger = logging.getLogger(__name__)
+
+
 class CGOExecutionEngine(AbstractExecutionEngine):
-    def __init__(self, n_model_per_graph = 4) -> None:
+    def __init__(self, n_model_per_graph=4) -> None:
         self._listeners: List[AbstractGraphListener] = []
         self._running_models: Dict[int, Model] = dict()
         self.logical_plan_counter = 0
@@ -30,12 +31,11 @@ class CGOExecutionEngine(AbstractExecutionEngine):
         advisor.intermediate_metric_callback = self._intermediate_metric_callback
         advisor.final_metric_callback = self._final_metric_callback
 
-    
     def add_optimizer(self, opt):
         self._optimizers.append(opt)
 
     def submit_models(self, *models: List[Model]) -> None:
-        _logger.info(f'{len(models)} Models are submitted')
+        _logger.info('%d models are submitted', len(models))
         logical = self._build_logical(models)
 
         for opt in self._optimizers:
@@ -55,13 +55,13 @@ class CGOExecutionEngine(AbstractExecutionEngine):
         #                          model.config['trainer_module'], model.config['trainer_kwargs'])
         #     self._running_models[send_trial(data.dump())] = model
 
-    def _assemble(self, logical_plan : LogicalPlan) -> List[Tuple[Model, PhysicalDevice]]:
+    def _assemble(self, logical_plan: LogicalPlan) -> List[Tuple[Model, PhysicalDevice]]:
         # unique_models = set()
         # for node in logical_plan.graph.nodes:
         #     if node.graph.model not in unique_models:
         #         unique_models.add(node.graph.model)
         # return [m for m in unique_models]
-        grouped_models : List[Dict[Model, PhysicalDevice]] = AssemblePolicy().group(logical_plan)
+        grouped_models: List[Dict[Model, PhysicalDevice]] = AssemblePolicy().group(logical_plan)
         phy_models_and_placements = []
         for multi_model in grouped_models:
             model, model_placement = logical_plan.assemble(multi_model)
@@ -69,7 +69,7 @@ class CGOExecutionEngine(AbstractExecutionEngine):
         return phy_models_and_placements
 
     def _build_logical(self, models: List[Model]) -> LogicalPlan:
-        logical_plan = LogicalPlan(id = self.logical_plan_counter)
+        logical_plan = LogicalPlan(plan_id=self.logical_plan_counter)
         for model in models:
             logical_plan.add_model(model)
         self.logical_plan_counter += 1
@@ -108,7 +108,7 @@ class CGOExecutionEngine(AbstractExecutionEngine):
         for model_id in merged_metrics:
             int_model_id = int(model_id)
             self._original_models[int_model_id].intermediate_metrics.append(merged_metrics[model_id])
-            #model.intermediate_metrics.append(metrics)
+            # model.intermediate_metrics.append(metrics)
             for listener in self._listeners:
                 listener.on_intermediate_metric(self._original_models[int_model_id], merged_metrics[model_id])
 
@@ -117,11 +117,10 @@ class CGOExecutionEngine(AbstractExecutionEngine):
         for model_id in merged_metrics:
             int_model_id = int(model_id)
             self._original_models[int_model_id].intermediate_metrics.append(merged_metrics[model_id])
-            #model.intermediate_metrics.append(metrics)
+            # model.intermediate_metrics.append(metrics)
             for listener in self._listeners:
                 listener.on_metric(self._original_models[int_model_id], merged_metrics[model_id])
 
-
     def query_available_resource(self) -> List[WorkerInfo]:
         raise NotImplementedError  # move the method from listener to here?
 
@@ -141,6 +140,7 @@ class CGOExecutionEngine(AbstractExecutionEngine):
         trainer_instance = trainer_cls(model_cls(), graph_data.training_kwargs)
         trainer_instance.fit()
 
+
 class AssemblePolicy:
     @staticmethod
     def group(logical_plan):
@@ -148,4 +148,3 @@ class AssemblePolicy:
         for idx, m in enumerate(logical_plan.models):
             group_model[m] = PhysicalDevice('server', f'cuda:{idx}')
         return [group_model]
-        
\ No newline at end of file

nni/retiarii/execution/interface.py

-from abc import *
-from typing import *
+from abc import ABC, abstractmethod, abstractclassmethod
+from typing import Any, NewType, List
 
 from ..graph import Model, MetricData
 

nni/retiarii/execution/listener.py

-from typing import *
-
-from ..graph import *
-from .interface import *
+from ..graph import Model, ModelStatus
+from .interface import MetricData, AbstractGraphListener
 
 
 class DefaultListener(AbstractGraphListener):

nni/retiarii/execution/logical_optimizer/interface.py

-from abc import *
-from typing import *
+from abc import ABC
 
 from .logical_plan import LogicalPlan
 
+
 class AbstractOptimizer(ABC):
     def __init__(self) -> None:
         pass

nni/retiarii/execution/logical_optimizer/logical_plan.py

-from nni.retiarii.operation import Operation
-from nni.retiarii.graph import Model, Graph, Edge, Node, Cell
-from typing import *
-import logging
-from nni.retiarii.operation import _IOPseudoOperation
 import copy
+from typing import Dict, Tuple, List, Any
+
+from ...graph import Cell, Edge, Graph, Model, Node
+from ...operation import Operation, _IOPseudoOperation
 
 
 class PhysicalDevice:
@@ -108,11 +107,11 @@ class OriginNode(AbstractLogicalNode):
 
 
 class LogicalPlan:
-    def __init__(self, id=0) -> None:
+    def __init__(self, plan_id=0) -> None:
         self.lp_model = Model(_internal=True)
-        self.id = id
+        self.id = plan_id
         self.logical_graph = LogicalGraph(
-            self.lp_model, id, name=f'{id}', _internal=True)._register()
+            self.lp_model, self.id, name=f'{self.id}', _internal=True)._register()
         self.lp_model._root_graph_name = self.logical_graph.name
         self.models = []
 
@@ -148,7 +147,7 @@ class LogicalPlan:
             phy_model.training_config.kwargs['is_multi_model'] = True
             phy_model.training_config.kwargs['model_cls'] = phy_graph.name
             phy_model.training_config.kwargs['model_kwargs'] = []
-            #FIXME: allow user to specify
+            # FIXME: allow user to specify
             phy_model.training_config.module = 'nni.retiarii.trainer.PyTorchMultiModelTrainer'
 
         # merge sub-graphs
@@ -158,7 +157,6 @@ class LogicalPlan:
                     model.graphs[graph_name]._fork_to(
                         phy_model, name_prefix=f'M_{model.model_id}_')
 
-        
         # When replace logical nodes, merge the training configs when
         # input/output nodes are replaced.
         training_config_slot = {}  # Model ID -> Slot ID
@@ -230,7 +228,7 @@ class LogicalPlan:
                     to_node = copied_op[(edge.head, tail_placement)]
                 else:
                     to_operation = Operation.new(
-                        'ToDevice', {"device":tail_placement.device})
+                        'ToDevice', {"device": tail_placement.device})
                     to_node = Node(phy_graph, phy_model._uid(),
                                    edge.head.name+"_to_"+edge.tail.name, to_operation)._register()
                     Edge((edge.head, edge.head_slot),
@@ -250,7 +248,6 @@ class LogicalPlan:
                 edge.head_slot = input_slot_mapping[edge.head]
                 edge.head = phy_graph.input_node
 
-
         # merge all output nodes into one with multiple slots
         output_nodes = []
         for node in phy_graph.hidden_nodes:

nni/retiarii/execution/logical_optimizer/opt_batching.py

-from .base_optimizer import BaseOptimizer
-from .logical_plan import LogicalPlan
-
-
-class BatchingOptimizer(BaseOptimizer):
-    def __init__(self) -> None:
-        pass
-
-    def convert(self, logical_plan: LogicalPlan) -> None:
-        pass

nni/retiarii/execution/logical_optimizer/opt_dedup_input.py

-from .interface import AbstractOptimizer
-from .logical_plan import LogicalPlan, AbstractLogicalNode, LogicalGraph, OriginNode, PhysicalDevice
-from nni.retiarii import Graph, Node, Model
-from typing import *
-from nni.retiarii.operation import _IOPseudoOperation
+from typing import List, Dict, Tuple
 
+from ...graph import Graph, Model, Node
+from .interface import AbstractOptimizer
+from .logical_plan import (AbstractLogicalNode, LogicalGraph, LogicalPlan,
+                           OriginNode, PhysicalDevice)
 
 _supported_training_modules = ['nni.retiarii.trainer.PyTorchImageClassificationTrainer']
+
+
 class DedupInputNode(AbstractLogicalNode):
-    def __init__(self, logical_graph : LogicalGraph, id : int, \
-            nodes_to_dedup : List[Node], _internal=False):
-        super().__init__(logical_graph, id, \
-            "Dedup_"+nodes_to_dedup[0].name, \
+    def __init__(self, logical_graph: LogicalGraph, node_id: int,
+                 nodes_to_dedup: List[Node], _internal=False):
+        super().__init__(logical_graph, node_id,
+                         "Dedup_"+nodes_to_dedup[0].name,
                          nodes_to_dedup[0].operation)
-        self.origin_nodes : List[OriginNode] = nodes_to_dedup.copy()
+        self.origin_nodes: List[OriginNode] = nodes_to_dedup.copy()
 
     def assemble(self, multi_model_placement: Dict[Model, PhysicalDevice]) -> Tuple[Node, PhysicalDevice]:
         for node in self.origin_nodes:
             if node.original_graph.model in multi_model_placement:
-                new_node = Node(node.original_graph, node.id, \
-                        f'M_{node.original_graph.model.model_id}_{node.name}', \
+                new_node = Node(node.original_graph, node.id,
+                                f'M_{node.original_graph.model.model_id}_{node.name}',
                                 node.operation)
                 return new_node, multi_model_placement[node.original_graph.model]
         raise ValueError(f'DedupInputNode {self.name} does not contain nodes from multi_model')
@@ -26,7 +28,6 @@ class DedupInputNode(AbstractLogicalNode):
     def _fork_to(self, graph: Graph):
         DedupInputNode(graph, self.id, self.origin_nodes)._register()
 
-    
     def __repr__(self) -> str:
         return f'DedupNode(id={self.id}, name={self.name}, \
             len(nodes_to_dedup)={len(self.origin_nodes)}'
@@ -35,6 +36,7 @@ class DedupInputNode(AbstractLogicalNode):
 class DedupInputOptimizer(AbstractOptimizer):
     def __init__(self) -> None:
         pass
+
     def _check_deduplicate_by_node(self, root_node, node_to_check):
         if root_node == node_to_check:
             return True
@@ -51,12 +53,11 @@ class DedupInputOptimizer(AbstractOptimizer):
         else:
             return False
 
-    
     def convert(self, logical_plan: LogicalPlan) -> None:
         nodes_to_skip = set()
         while True:  # repeat until the logical_graph converges
             input_nodes = logical_plan.logical_graph.get_nodes_by_type("_inputs")
-                #_PseudoOperation(type_name="_inputs"))
+            # _PseudoOperation(type_name="_inputs"))
             root_node = None
             for node in input_nodes:
                 if node in nodes_to_skip:
@@ -77,7 +78,7 @@ class DedupInputOptimizer(AbstractOptimizer):
                     assert(nodes_to_dedup[0] == root_node)
                     nodes_to_skip.add(root_node)
                 else:
-                    dedup_node = DedupInputNode(logical_plan.logical_graph, \
+                    dedup_node = DedupInputNode(logical_plan.logical_graph,
                                                 logical_plan.lp_model._uid(), nodes_to_dedup)._register()
                     for edge in logical_plan.logical_graph.edges:
                         if edge.head in nodes_to_dedup:

nni/retiarii/execution/logical_optimizer/opt_weight_sharing.py

-from .base_optimizer import BaseOptimizer
-from .logical_plan import LogicalPlan
-
-
-class WeightSharingOptimizer(BaseOptimizer):
-    def __init__(self) -> None:
-        pass
-
-    def convert(self, logical_plan: LogicalPlan) -> None:
-        pass

nni/retiarii/experiment.py

-import dataclasses
 import logging
 import time
 
 from dataclasses import dataclass
 from pathlib import Path
+from subprocess import Popen
 from threading import Thread
-from typing import Any, List, Optional
+from typing import Any, Optional
 
-from ..experiment import Experiment, TrainingServiceConfig
-from ..experiment import launcher, rest
+from ..experiment import Experiment, TrainingServiceConfig, launcher, rest
 from ..experiment.config.base import ConfigBase, PathLike
 from ..experiment.config import util
+from ..experiment.pipe import Pipe
+from .graph import Model
 from .utils import get_records
 from .integration import RetiariiAdvisor
-from .converter.graph_gen import convert_to_graph
-from .mutator import LayerChoiceMutator, InputChoiceMutator
+from .converter import convert_to_graph
+from .mutator import Mutator, LayerChoiceMutator, InputChoiceMutator
+from .trainer.interface import BaseTrainer
+from .strategies.strategy import BaseStrategy
 
 _logger = logging.getLogger(__name__)
 
+
 @dataclass(init=False)
 class RetiariiExeConfig(ConfigBase):
     experiment_name: Optional[str] = None
@@ -52,6 +56,7 @@ class RetiariiExeConfig(ConfigBase):
     def _validation_rules(self):
         return _validation_rules
 
+
 _canonical_rules = {
     'trial_code_directory': util.canonical_path,
     'max_experiment_duration': lambda value: f'{util.parse_time(value)}s' if value is not None else None,
@@ -70,8 +75,8 @@ _validation_rules = {
 
 
 class RetiariiExperiment(Experiment):
-    def __init__(self, base_model: 'nn.Module', trainer: 'BaseTrainer',
-                 applied_mutators: List['Mutator'], strategy: 'BaseStrategy'):
+    def __init__(self, base_model: Model, trainer: BaseTrainer,
+                 applied_mutators: Mutator, strategy: BaseStrategy):
         self.config: RetiariiExeConfig = None
         self.port: Optional[int] = None
 

nni/retiarii/graph.py

@@ -5,7 +5,6 @@ Model representation.
 import copy
 from enum import Enum
 import json
-from collections import defaultdict
 from typing import (Any, Dict, List, Optional, Tuple, Union, overload)
 
 from .operation import Cell, Operation, _IOPseudoOperation
@@ -330,11 +329,11 @@ class Graph:
         """
         return [node for node in self.hidden_nodes if node.operation.type == operation_type]
 
-    def get_node_by_id(self, id: int) -> Optional['Node']:
+    def get_node_by_id(self, node_id: int) -> Optional['Node']:
         """
         Returns the node which has specified name; or returns `None` if no node has this name.
         """
-        found = [node for node in self.nodes if node.id == id]
+        found = [node for node in self.nodes if node.id == node_id]
         return found[0] if found else None
 
     def get_nodes_by_label(self, label: str) -> List['Node']:
@@ -365,7 +364,8 @@ class Graph:
                     curr_nodes.append(successor)
 
         for key in node_to_fanin:
-            assert node_to_fanin[key] == 0, '{}, fanin: {}, predecessor: {}, edges: {}, fanin: {}, keys: {}'.format(key,
+            assert node_to_fanin[key] == 0, '{}, fanin: {}, predecessor: {}, edges: {}, fanin: {}, keys: {}'.format(
+                key,
                 node_to_fanin[key],
                 key.predecessors[0],
                 self.edges,
@@ -587,6 +587,7 @@ class Node:
             ret['label'] = self.label
         return ret
 
+
 class Edge:
     """
     A tensor, or "data flow", between two nodes.

nni/retiarii/integration.py

 import logging
-import threading
-from typing import *
+from typing import Any, Callable
 
 import json_tricks
 import nni
 from nni.runtime.msg_dispatcher_base import MsgDispatcherBase
-from nni.runtime.protocol import send, CommandType
+from nni.runtime.protocol import CommandType, send
 from nni.utils import MetricType
 
-from . import utils
 from .graph import MetricData
 
-
 _logger = logging.getLogger('nni.msg_dispatcher_base')
 
 
@@ -44,6 +41,7 @@ class RetiariiAdvisor(MsgDispatcherBase):
 
     final_metric_callback
     """
+
     def __init__(self):
         super(RetiariiAdvisor, self).__init__()
         register_advisor(self)  # register the current advisor as the "global only" advisor
@@ -88,28 +86,28 @@ class RetiariiAdvisor(MsgDispatcherBase):
             'parameters': parameters,
             'parameter_source': 'algorithm'
         }
-        _logger.info('New trial sent: {}'.format(new_trial))
+        _logger.info('New trial sent: %s', new_trial)
         send(CommandType.NewTrialJob, json_tricks.dumps(new_trial))
         if self.send_trial_callback is not None:
             self.send_trial_callback(parameters)  # pylint: disable=not-callable
         return self.parameters_count
 
     def handle_request_trial_jobs(self, num_trials):
-        _logger.info('Request trial jobs: {}'.format(num_trials))
+        _logger.info('Request trial jobs: %s', num_trials)
         if self.request_trial_jobs_callback is not None:
             self.request_trial_jobs_callback(num_trials)  # pylint: disable=not-callable
 
     def handle_update_search_space(self, data):
-        _logger.info('Received search space: {}'.format(data))
+        _logger.info('Received search space: %s', data)
         self.search_space = data
 
     def handle_trial_end(self, data):
-        _logger.info('Trial end: {}'.format(data))  # do nothing
+        _logger.info('Trial end: %s', data)
         self.trial_end_callback(json_tricks.loads(data['hyper_params'])['parameter_id'],  # pylint: disable=not-callable
                                 data['event'] == 'SUCCEEDED')
 
     def handle_report_metric_data(self, data):
-        _logger.info('Metric reported: {}'.format(data))
+        _logger.info('Metric reported: %s', data)
         if data['type'] == MetricType.REQUEST_PARAMETER:
             raise ValueError('Request parameter not supported')
         elif data['type'] == MetricType.PERIODICAL:

nni/retiarii/mutator.py

@@ -13,6 +13,7 @@ class Sampler:
     """
     Handles `Mutator.choice()` calls.
     """
+
     def choice(self, candidates: List[Choice], mutator: 'Mutator', model: Model, index: int) -> Choice:
         raise NotImplementedError()
 
@@ -35,6 +36,7 @@ class Mutator:
     For certain mutator subclasses, strategy or sampler can use `Mutator.dry_run()` to predict choice candidates.
     # Method names are open for discussion.
     """
+
     def __init__(self, sampler: Optional[Sampler] = None):
         self.sampler: Optional[Sampler] = sampler
         self._cur_model: Optional[Model] = None
@@ -77,7 +79,6 @@ class Mutator:
         self.sampler = sampler_backup
         return recorder.recorded_candidates, new_model
 
-
     def mutate(self, model: Model) -> None:
         """
         Abstract method to be implemented by subclass.
@@ -105,6 +106,7 @@ class _RecorderSampler(Sampler):
 
 # the following is for inline mutation
 
+
 class LayerChoiceMutator(Mutator):
     def __init__(self, node_name: str, candidates: List):
         super().__init__()
@@ -118,6 +120,7 @@ class LayerChoiceMutator(Mutator):
         chosen_cand = self.candidates[chosen_index]
         target.update_operation(chosen_cand['type'], chosen_cand['parameters'])
 
+
 class InputChoiceMutator(Mutator):
     def __init__(self, node_name: str, n_chosen: int):
         super().__init__()