Add mypy configuration.

It will be some time before we can enforce this. I triaged some initial issues and sent llvm/llvm-project#66723 upstream to cover some mis-aligned things. We should triage again once that lands.

Author: stellaraccident

mypy.ini

@@ -0,0 +1,4 @@
+[mypy]
+
+mypy_path = $MYPY_CONFIG_FILE_DIR/python
+packages = shark_turbine.aot,shark_turbine.dynamo,shark_turbine.support

python/shark_turbine/aot/builtins/jittable.py

@@ -86,7 +86,7 @@ def resolver(py_value: Any, gni: GraphNodeImporter) -> Optional[Value]:
 
         # Already materialized.
         logger.debug("Resolved defined global for literal %r", mapping)
-        materialized_global: MaterializedGlobal = mapping.value
+        materialized_global: MaterializedGlobal = mapping.value  # type: ignore
 
         # Clone the global into the import module (so that our symbol refs are
         # legal). Note that the merger will ignore these since they already

python/shark_turbine/aot/support/ir_utils.py

@@ -49,7 +49,7 @@
 MLIR_TYPE_ASM_TO_TORCH_DTYPE = {v: k for k, v in TORCH_DTYPE_TO_MLIR_TYPE_ASM.items()}
 
 # When emitting constants, we have to create native IREE types.
-TORCH_DTYPE_TO_IREE_TYPE: Dict[str, Callable[[], IrType]] = {
+TORCH_DTYPE_TO_IREE_TYPE: Dict[torch.dtype, Callable[[], IrType]] = {
     torch.float16: lambda: F16Type.get(),
     torch.bfloat16: lambda: BF16Type.get(),
     torch.float32: lambda: F32Type.get(),
@@ -167,7 +167,8 @@ def create_tensor_global(
             if initialize:
                 detached_tensor = t.detach().contiguous().cpu()
                 array = np.array(detached_tensor)
-                contents = memoryview(array)
+                # We know that a Numpy array is a ReadableBuffer so ignore type error.
+                contents = memoryview(array)  # type: ignore
                 # TODO: Add resource elements to Python API and use that.
                 elements_attr = DenseElementsAttr.get(contents, type=tensor_type)
                 attrs["initial_value"] = elements_attr
@@ -200,10 +201,10 @@ def __init__(
         self.func_op = func_op
         self.context = func_op.context
         self.ip = InsertionPoint(self.func_op.entry_block)
-        self.return_types = None
+        self.return_types: Optional[Sequence[IrType]] = None
         self.loc = self.func_op.location
 
-    def emit_return(self, *ir_values: Sequence[Value]):
+    def emit_return(self, *ir_values: Value):
         with self.loc, self.ip:
             func_d.ReturnOp(ir_values)
             # Check or rewrite the function return type.

python/shark_turbine/aot/support/procedural.py

@@ -91,7 +91,7 @@ def current_ir_trace() -> IrTrace:
 class Intrinsic:
     """Objects which interact natively with the tracing system implement this."""
 
-    __slots__ = []
+    __slots__: List[str] = []
 
     def resolve_ir_values(self, proc_trace: "IrTrace") -> Sequence[Value]:
         raise NotImplementedError(
@@ -125,7 +125,7 @@ def __call__(self, *args, **kwargs):
 class AbstractIntrinsic:
     """Base class for descriptor types that can be converted to Python proxies."""
 
-    __slots__ = []
+    __slots__: List[str] = []
 
     def create_intrinsic(self, value: Value) -> Intrinsic:
         """Creates a proxy object that can flow through a procedural trace."""
@@ -192,7 +192,7 @@ def abstractify_single_value(value) -> AbstractTypedef:
     if isinstance(value, AbstractTypedef):
         return value
     if isinstance(value, Abstractifiable):
-        return value.get_abstract_typedef()
+        return value.abstractify()
     if isinstance(value, torch.Tensor):
         return AbstractTensor(*value.shape, dtype=value.dtype)
     raise TypeError(
@@ -494,7 +494,7 @@ def trace_py_func(self, py_f: Callable):
                 self.emit_return()
             else:
                 flat_return_py_values, schema = tree_flatten(return_py_value)
-                flat_return_ir_values = []
+                flat_return_ir_values: List[Value] = []
                 for py_value in flat_return_py_values:
                     flat_return_ir_values.extend(convert_py_value_to_ir(self, py_value))
                 self.func_op.attributes["torch.return_schema"] = StringAttr.get(