Update deprecated type hinting in vllm/profiler

pyproject.toml

@@ -84,7 +84,6 @@ exclude = [
 "vllm/model_executor/models/**/*.py" = ["UP006", "UP035"]
 "vllm/platforms/**/*.py" = ["UP006", "UP035"]
 "vllm/plugins/**/*.py" = ["UP006", "UP035"]
-"vllm/profiler/**/*.py" = ["UP006", "UP035"]
 "vllm/prompt_adapter/**/*.py" = ["UP006", "UP035"]
 "vllm/spec_decode/**/*.py" = ["UP006", "UP035"]
 "vllm/transformers_utils/**/*.py" = ["UP006", "UP035"]

vllm/profiler/layerwise_profile.py

@@ -3,7 +3,7 @@
 import copy
 from collections import defaultdict
 from dataclasses import asdict, dataclass, field
-from typing import Any, Callable, Dict, List, Optional, Tuple, TypeAlias, Union
+from typing import Any, Callable, Optional, TypeAlias, Union
 
 import pandas as pd
 from torch._C._autograd import DeviceType, _KinetoEvent, _ProfilerResult
@@ -20,7 +20,7 @@
 class _ModuleTreeNode:
     event: _ProfilerEvent
     parent: Optional['_ModuleTreeNode'] = None
-    children: List['_ModuleTreeNode'] = field(default_factory=list)
+    children: list['_ModuleTreeNode'] = field(default_factory=list)
     trace: str = ""
 
     @property
@@ -60,19 +60,19 @@ class ModelStatsEntry:
 @dataclass
 class _StatsTreeNode:
     entry: StatsEntry
-    children: List[StatsEntry]
+    children: list[StatsEntry]
     parent: Optional[StatsEntry]
 
 
 @dataclass
 class LayerwiseProfileResults(profile):
     _kineto_results: _ProfilerResult
-    _kineto_event_correlation_map: Dict[int,
-                                        List[_KinetoEvent]] = field(init=False)
-    _event_correlation_map: Dict[int, List[FunctionEvent]] = field(init=False)
-    _module_tree: List[_ModuleTreeNode] = field(init=False)
-    _model_stats_tree: List[_StatsTreeNode] = field(init=False)
-    _summary_stats_tree: List[_StatsTreeNode] = field(init=False)
+    _kineto_event_correlation_map: dict[int,
+                                        list[_KinetoEvent]] = field(init=False)
+    _event_correlation_map: dict[int, list[FunctionEvent]] = field(init=False)
+    _module_tree: list[_ModuleTreeNode] = field(init=False)
+    _model_stats_tree: list[_StatsTreeNode] = field(init=False)
+    _summary_stats_tree: list[_StatsTreeNode] = field(init=False)
 
     # profile metadata
     num_running_seqs: Optional[int] = None
@@ -82,7 +82,7 @@ def __post_init__(self):
         self._build_module_tree()
         self._build_stats_trees()
 
-    def print_model_table(self, column_widths: Dict[str, int] = None):
+    def print_model_table(self, column_widths: dict[str, int] = None):
         _column_widths = dict(name=60,
                               cpu_time_us=12,
                               cuda_time_us=12,
@@ -100,7 +100,7 @@ def print_model_table(self, column_widths: Dict[str, int] = None):
                 filtered_model_table,
                 indent_style=lambda indent: "|" + "-" * indent + " "))
 
-    def print_summary_table(self, column_widths: Dict[str, int] = None):
+    def print_summary_table(self, column_widths: dict[str, int] = None):
         _column_widths = dict(name=80,
                               cuda_time_us=12,
                               pct_cuda_time=12,
@@ -142,7 +142,7 @@ def convert_stats_to_dict(self) -> dict[str, Any]:
         }
 
     @staticmethod
-    def _indent_row_names_based_on_depth(depths_rows: List[Tuple[int,
+    def _indent_row_names_based_on_depth(depths_rows: list[tuple[int,
                                                                  StatsEntry]],
                                          indent_style: Union[Callable[[int],
                                                                       str],
@@ -229,7 +229,7 @@ def _total_cuda_time(self):
             [self._cumulative_cuda_time(root) for root in self._module_tree])
 
     def _build_stats_trees(self):
-        summary_dict: Dict[str, _StatsTreeNode] = {}
+        summary_dict: dict[str, _StatsTreeNode] = {}
         total_cuda_time = self._total_cuda_time()
 
         def pct_cuda_time(cuda_time_us):
@@ -238,7 +238,7 @@ def pct_cuda_time(cuda_time_us):
         def build_summary_stats_tree_df(
             node: _ModuleTreeNode,
             parent: Optional[_StatsTreeNode] = None,
-            summary_trace: Tuple[str] = ()):
+            summary_trace: tuple[str] = ()):
 
             if event_has_module(node.event):
                 name = event_module_repr(node.event)
@@ -313,8 +313,8 @@ def build_model_stats_tree_df(node: _ModuleTreeNode,
             self._model_stats_tree.append(build_model_stats_tree_df(root))
 
     def _flatten_stats_tree(
-            self, tree: List[_StatsTreeNode]) -> List[Tuple[int, StatsEntry]]:
-        entries: List[Tuple[int, StatsEntry]] = []
+            self, tree: list[_StatsTreeNode]) -> list[tuple[int, StatsEntry]]:
+        entries: list[tuple[int, StatsEntry]] = []
 
         def df_traversal(node: _StatsTreeNode, depth=0):
             entries.append((depth, node.entry))
@@ -327,10 +327,10 @@ def df_traversal(node: _StatsTreeNode, depth=0):
         return entries
 
     def _convert_stats_tree_to_dict(self,
-                                    tree: List[_StatsTreeNode]) -> List[Dict]:
-        root_dicts: List[Dict] = []
+                                    tree: list[_StatsTreeNode]) -> list[dict]:
+        root_dicts: list[dict] = []
 
-        def df_traversal(node: _StatsTreeNode, curr_json_list: List[Dict]):
+        def df_traversal(node: _StatsTreeNode, curr_json_list: list[dict]):
             curr_json_list.append({
                 "entry": asdict(node.entry),
                 "children": []

vllm/profiler/utils.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import dataclasses
-from typing import Callable, Dict, List, Type, Union
+from typing import Callable, Union
 
 from torch._C._profiler import _EventType, _ProfilerEvent, _TensorMetadata
 
@@ -30,14 +30,14 @@ def trim_string_back(string, width):
 
 class TablePrinter:
 
-    def __init__(self, row_cls: Type[dataclasses.dataclass],
-                 column_widths: Dict[str, int]):
+    def __init__(self, row_cls: type[dataclasses.dataclass],
+                 column_widths: dict[str, int]):
         self.row_cls = row_cls
         self.fieldnames = [x.name for x in dataclasses.fields(row_cls)]
         self.column_widths = column_widths
         assert set(self.column_widths.keys()) == set(self.fieldnames)
 
-    def print_table(self, rows: List[dataclasses.dataclass]):
+    def print_table(self, rows: list[dataclasses.dataclass]):
         self._print_header()
         self._print_line()
         for row in rows: