Snapchat · svij-sc · Apr 13, 2026 · Apr 15, 2026 · Apr 15, 2026 · Apr 15, 2026
@@ -2,7 +2,7 @@
 
 Do not suppress errors with workarounds like `# type: ignore`:
 
-1. `make type_check`
+1. `make type_check` — runs **ty** static type checker (config in `pyproject.toml` under `[tool.ty]`)
 2. `make unit_test_py PY_TEST_FILES="relevant_test.py"`
 3. `make integration_test PY_TEST_FILES="relevant_test.py"` (if cross-component behavior changed)
 4. `make check_format` (or `make format` to auto-fix)

@@ -4,6 +4,7 @@
 **/*.pyo
 **/*.pyd
 **/*.pkl
+.ty
 .mypy_cache
 _autosummary
 

@@ -17,6 +17,7 @@ dist/
 *.egg-info/
 miniconda/
 .mypy_cache/
+.ty/
 tools/
 .metals/
 .bloop/

@@ -32,7 +32,7 @@ make format_py           # Auto-fix Python only
 make format_scala        # Auto-fix Scala only
 make format_md           # Auto-fix Markdown only
 make check_format        # Check without fixing
-make type_check          # mypy static type checking
+make type_check          # ty static type checking
 
 # Build
 make compile_protos      # Regenerate protobuf code after .proto changes

@@ -146,7 +146,7 @@ format_md:
 format: format_py format_scala format_md
 
 type_check:
-	uv run mypy ${PYTHON_DIRS} --check-untyped-defs
+	uv run ty check ${PYTHON_DIRS}
 
 lint_test: check_format assert_yaml_configs_parse
 	@echo "Lint checks pass!"

@@ -206,7 +206,7 @@ make unit_test_py PY_TEST_FILES="eval_metrics_test.py"
 # Runs just Scala unit tests
 make unit_test_scala
 
-# Run the python static type checker `mypy`
+# Run the python static type checker `ty`
 make type_check
 
 # Run all formatting/linting tests

@@ -606,7 +606,7 @@ def build_dataset_from_task_config_uri(
     )
 
     # Need to do this "backwards" so the parent class can be defined first.
-    # Otherwise, mypy complains that:
+    # Otherwise, the type checker complains that:
     # "expression has type "type[DistPartitioner]", variable has type "type[DistRangePartitioner]"
     if not should_use_range_partitioning:
         partitioner_class = DistPartitioner

diff --git a/gigl/distributed/dist_partitioner.py b/gigl/distributed/dist_partitioner.py
@@ -1268,7 +1268,7 @@ def _label_pfn(source_node_ids, _):
         del label_edge_index
 
         if is_positive:
-            # This assert is added to pass mypy type check, in practice we will not see this fail
+            # This assert is added to pass the type checker, in practice we will not see this fail
             assert (
                 self._positive_label_edge_index is not None
             ), "Must register positive labels prior to partitioning them"
@@ -1277,7 +1277,7 @@ def _label_pfn(source_node_ids, _):
             if len(self._positive_label_edge_index) == 0:
                 self._positive_label_edge_index = None
         else:
-            # This assert is added to pass mypy type check, in practice we will not see this fail
+            # This assert is added to pass the type checker, in practice we will not see this fail
             assert (
                 self._negative_label_edge_index is not None
             ), "Must register negative labels prior to partitioning them"

@@ -268,7 +268,7 @@ def _build_job_config(
         if vertex_ai_resource_config.timeout
         else None,
         # This should be `aiplatform.gapic.Scheduling.Strategy[inferencer_resource_config.scheduling_strategy]`
-        # But mypy complains otherwise...
+        # But the type checker complains otherwise...
         # gigl/src/inference/v2/glt_inferencer.py:124: error: The type "type[Strategy]" is not generic and not indexable  [misc]
         # TODO(kmonte): Fix this
         scheduling_strategy=getattr(

@@ -44,7 +44,7 @@ def _get_bigquery_ptransform(
         )
 
 
-# Below type ignores are due to mypy star expansion issues: https://github.com/python/mypy/issues/6799
+# Below type ignores are due to star expansion issues with the type checker: https://github.com/python/mypy/issues/6799
 def yield_instance_dict_ptransform(self, *args, **kwargs) -> InstanceDictPTransform: 
     return _get_bigquery_ptransform( 
         self.reference_uri, 
         self.sharded_read_config, 
         *args, 
         **kwargs, 
     ) 
 def yield_instance_dict_ptransform(self, *args, **kwargs) -> InstanceDictPTransform: 
     return _get_bigquery_ptransform( 
         self.reference_uri, 
         self.sharded_read_config, 
         *args, 
         **kwargs, 
     ) 
 @dataclass(frozen=True)
 class BigqueryNodeDataReference(NodeDataReference):
     """

@@ -7,7 +7,7 @@
 from gigl.src.common.types.graph_data import EdgeType, EdgeUsageType, NodeType
 from gigl.src.data_preprocessor.lib.types import InstanceDictPTransform
 
-# Type hints for abstract dataclasses are currently not supported. https://github.com/python/mypy/issues/5374
+# Type hints for abstract dataclasses may have limited support in type checkers. https://github.com/python/mypy/issues/5374
 @dataclass(frozen=True) 
 class DataReference(ABC): 
     """ 
     Contains a URI string to the data reference, and provides a means of yielding 
  
  
 @dataclass(frozen=True) 
 class DataReference(ABC): 
     """ 
     Contains a URI string to the data reference, and provides a means of yielding 
 
 
 @dataclass(frozen=True)  # type: ignore

@@ -387,8 +387,8 @@ def select_label_edge_types(
     str,
     int,
     # TODO(kmonte): Add GLT Partition book here
-    # We cannot at the moment as we mypy ignore GLT
-    # And adding it as a type here will break mypy.
+    # We cannot at the moment as we type-ignore GLT
+    # And adding it as a type here will break the type checker.
     # PartitionBook
 _GraphEntity = TypeVar( 
     "_GraphEntity", 
     torch.Tensor, 
     GraphPartitionData, 
     FeaturePartitionData, 
     SerializedTFRecordInfo, 
     Optional[SerializedTFRecordInfo], 
     list, 
     str, 
     int, 
     PartitionBook, 
 ) 
 _GraphEntity = TypeVar( 
     "_GraphEntity", 
     torch.Tensor, 
     GraphPartitionData, 
     FeaturePartitionData, 
     SerializedTFRecordInfo, 
     Optional[SerializedTFRecordInfo], 
     list, 
     str, 
     int, 
     PartitionBook, 
 ) 
 )
 

@@ -142,9 +142,8 @@ lint = [
   "isort~=5.12.0",
   "mdformat==0.7.22",
   "mdformat_tables==1.0.0",
-  "mypy==1.8.0",
-  "mypy-extensions",
-  "mypy-protobuf==3.3.0",
+  "ty~=0.0.29",
+  "mypy-protobuf==3.3.0",  # Used for protobuf stub generation (protoc-gen-mypy), not type checking
 ]
 
 
@@ -283,3 +282,84 @@ exclude =  [
 remove-all-unused-imports = true
 in-place = true
 recursive = true
+
+[tool.ty.environment]
+python-version = "3.11"
+
+[tool.ty.src]
+exclude = ["*_pb2.py", "*_pb2.pyi", "**/*.ipynb"]
+
+[tool.ty.analysis]
+# Migrated from mypy.ini per-module ignore_missing_imports sections,
+# plus additional modules that are optional or not installed in all environments.
+allowed-unresolved-imports = [
+  "absl.**",
+  "apache_beam.**",
+  "common.**",
+  "fastavro.**",
+  "google.cloud.**",
+  "google_cloud_pipeline_components.**",
+  "graphlearn_torch.**",
+  "hydra.**",
+  "kfp.**",
+  "kfp_server_api.**",
+  "matplotlib.**",
+  "msgpack",
+  "networkx.**",
+  "ogb.**",
+  "orjson",
+  "parameterized.**",
+  "pyarrow.**",
+  "setuptools",
+  "tensorflow.**",
+  "tensorflow_data_validation.**",
+  "tensorflow_metadata.**",
+  "tensorflow_transform.**",
+  "tfx_bsl.**",
+  # torch lacks inline types/stubs; ty cannot resolve torch types in all environments
+  "torch.**",
+  "torch_geometric.**",
+  "torch_sparse.**",
+  "torchrec.**",
+]
+# When mypy's ignore_missing_imports=True was set for a module, mypy would also
+# implicitly treat all types from that module as Any, suppressing downstream
+# type errors (e.g. unresolved-attribute, invalid-argument-type) on values from
+# those modules. ty's allowed-unresolved-imports only suppresses the import
+# error but leaves the type as Unknown, causing cascading errors. This setting
+# restores the mypy behavior by replacing imports from untyped libraries with Any.
+replace-imports-with-any = [
+  "apache_beam.**",
+  "fastavro.**",
+  "google.cloud.**",
+  "google_cloud_pipeline_components.**",
+  "graphlearn_torch.**",
+  "hydra.**",
+  "kfp.**",
+  "kfp_server_api.**",
+  "matplotlib.**",
+  "ogb.**",
+  "tensorflow.**",
+  "tensorflow_data_validation.**",
+  "tensorflow_metadata.**",
+  "tensorflow_transform.**",
+  "tfx_bsl.**",
+  "torch.**",
+  "torch_geometric.**",
+  "torch_sparse.**",
+  "torchrec.**",
+]
+
+[tool.ty.rules]
+# Suppress noise from existing type: ignore comments that ty may not need.
+unused-type-ignore-comment = "ignore"
+unused-ignore-comment = "ignore"
+
+# Generated protobuf files trigger type errors from the google-protobuf stubs
+# (e.g. RegisterMessage argument types). These files are auto-generated by
+# protoc and cannot be fixed — suppress all type errors in them.
+[[tool.ty.overrides]]
+include = ["**/*_pb2.py"]
+[tool.ty.overrides.rules]
+invalid-argument-type = "ignore"
+unresolved-attribute = "ignore"
@@ -297,7 +297,7 @@ def test_translator_correctness(self, _, mocked_dataset_info: MockedDatasetInfo)
                 ):
                     assert (
                         seralized_positive_label_info is not None
-                    )  # We use assert instead of self.assertIsNotNone since this allows type narrowing with mypy
+                    )  # We use assert instead of self.assertIsNotNone since this allows type narrowing with the type checker
 
                     edge_info = preprocessed_metadata_pb_wrapper.preprocessed_metadata_pb.condensed_edge_type_to_preprocessed_metadata[
                         condensed_edge_type
@@ -391,7 +391,7 @@ def test_translator_correctness(self, _, mocked_dataset_info: MockedDatasetInfo)
                 ):
                     assert (
                         serialized_negative_label_info is not None
-                    )  # We use assert instead of self.assertIsNotNone since this allows type narrowing with mypy
+                    )  # We use assert instead of self.assertIsNotNone since this allows type narrowing with the type checker
 
                     edge_info = preprocessed_metadata_pb_wrapper.preprocessed_metadata_pb.condensed_edge_type_to_preprocessed_metadata[
                         condensed_edge_type

@@ -185,7 +185,7 @@ def test_launch_training_graph_store_cuda(self, mock_vertex_ai_service_class):
             process_command.split(),
         )
         self.assertIsNotNone(compute_job_config.args)
-        assert compute_job_config.args is not None  # Type narrowing for mypy
+        assert compute_job_config.args is not None  # Type narrowing for the type checker
         self.assertIn(f"--job_name={job_name}", compute_job_config.args)
         self.assertIn(
             f"--learning_rate={process_runtime_args['learning_rate']}",
@@ -298,7 +298,7 @@ def test_launch_inference_single_pool_cpu(self, mock_vertex_ai_service_class):
         # Verify command and args
         self.assertEqual(job_config.command, process_command.split())
         self.assertIsNotNone(job_config.args)
-        assert job_config.args is not None  # Type narrowing for mypy
+        assert job_config.args is not None  # Type narrowing for the type checker
         self.assertIn(f"--job_name={job_name}", job_config.args)
         self.assertIn(f"--task_config_uri={task_config_uri}", job_config.args)
         self.assertIn(f"--resource_config_uri={resource_config_uri}", job_config.args)

@@ -654,7 +654,7 @@ def test_gradient_flow(self):
 
         # Check that gradients exist and are not NaN
         self.assertIsNotNone(sequences.grad)
-        assert sequences.grad is not None  # Type narrowing for mypy
+        assert sequences.grad is not None  # Type narrowing for the type checker
         self.assertFalse(torch.isnan(sequences.grad).any())
 
     def test_transformer_with_classification_head(self):