[Refactor] Add cook_dtype calls at C++ boundaries (no-op preparatory refactor)

Add cook_dtype() calls at all points where dtype values are passed to C++ code.
Make PyQuadrants.default_fp/ip/up into properties that always store DataTypeCxx.
Rename shadowed dtype var in create_field_member. All changes are behavioral
no-ops with current code, preparing for a future refactor of primitive dtypes
into Python classes.

Author: hughperkins

python/quadrants/lang/expr.py

@@ -7,7 +7,12 @@
 from quadrants.lang.common_ops import QuadrantsOperations
 from quadrants.lang.exception import QuadrantsCompilationError, QuadrantsTypeError
 from quadrants.lang.matrix import make_matrix
-from quadrants.lang.util import is_matrix_class, is_quadrants_class, to_numpy_type
+from quadrants.lang.util import (
+    cook_dtype,
+    is_matrix_class,
+    is_quadrants_class,
+    to_numpy_type,
+)
 from quadrants.types import primitive_types
 from quadrants.types.primitive_types import integer_types, real_types
 
@@ -109,12 +114,16 @@ def _clamp_unsigned_to_range(npty, val: np.integer | int) -> np.integer | int:
 
 
 def make_constant_expr(val, dtype):
+    if dtype is not None:
+        dtype = cook_dtype(dtype)
+
     if isinstance(val, (bool, np.bool_)):
-        constant_dtype = primitive_types.u1
+        constant_dtype = cook_dtype(primitive_types.u1)
         return Expr(_qd_core.make_const_expr_bool(constant_dtype, val))
 
     if isinstance(val, (float, np.floating)):
         constant_dtype = impl.get_runtime().default_fp if dtype is None else dtype
+        constant_dtype = cook_dtype(constant_dtype)
         if constant_dtype not in real_types:
             raise QuadrantsTypeError(
                 "Floating-point literals must be annotated with a floating-point type. For type casting, use `qd.cast`."
@@ -123,6 +132,7 @@ def make_constant_expr(val, dtype):
 
     if isinstance(val, (int, np.integer)):
         constant_dtype = impl.get_runtime().default_ip if dtype is None else dtype
+        constant_dtype = cook_dtype(constant_dtype)
         if constant_dtype not in integer_types:
             raise QuadrantsTypeError(
                 "Integer literals must be annotated with a integer type. For type casting, use `qd.cast`."

python/quadrants/lang/impl.py

@@ -83,6 +83,7 @@
 def expr_init_shared_array(shape, element_type):
     ast_builder = get_runtime().compiling_callable.ast_builder()
     debug_info = _qd_core.DebugInfo(get_runtime().get_current_src_info())
+    element_type = cook_dtype(element_type)
     return ast_builder.expr_alloca_shared_array(shape, element_type, debug_info)
 
 
@@ -355,9 +356,9 @@ def __init__(self, kernels=None):
         self.grad_vars = []
         self.dual_vars = []
         self.matrix_fields = []
-        self.default_fp = f32
-        self.default_ip = i32
-        self.default_up = u32
+        self._default_fp = cook_dtype(f32)
+        self._default_ip = cook_dtype(i32)
+        self._default_up = cook_dtype(u32)
         self.print_full_traceback: bool = False
         self.target_tape = None
         self.fwd_mode_manager = None
@@ -371,6 +372,30 @@ def __init__(self, kernels=None):
         self.unrolling_limit: int = 0
         self.src_ll_cache: bool = True
 
+    @property
+    def default_fp(self) -> DataTypeCxx:
+        return self._default_fp
+
+    @default_fp.setter
+    def default_fp(self, value: Any) -> None:
+        self._default_fp = cook_dtype(value)
+
+    @property
+    def default_ip(self) -> DataTypeCxx:
+        return self._default_ip
+
+    @default_ip.setter
+    def default_ip(self, value: Any) -> None:
+        self._default_ip = cook_dtype(value)
+
+    @property
+    def default_up(self) -> DataTypeCxx:
+        return self._default_up
+
+    @default_up.setter
+    def default_up(self, value: Any) -> None:
+        self._default_up = cook_dtype(value)
+
     @property
     def compiling_callable(self) -> KernelCxx | Kernel | Function:
         if self._compiling_callable is None:
@@ -737,10 +762,10 @@ def create_field_member(dtype, name, needs_grad, needs_dual):
         if prog.config().debug:
             # adjoint checkbit
             x_grad_checkbit = Expr(prog.make_id_expr(""))
-            dtype = u8
+            checkbit_dtype = u8
             if prog.config().arch == _qd_core.vulkan:
-                dtype = i32
-            x_grad_checkbit.ptr = _qd_core.expr_field(x_grad_checkbit.ptr, cook_dtype(dtype))
+                checkbit_dtype = i32
+            x_grad_checkbit.ptr = _qd_core.expr_field(x_grad_checkbit.ptr, cook_dtype(checkbit_dtype))
             x_grad_checkbit.ptr.set_name(name + ".grad_checkbit")
             x_grad_checkbit.ptr.set_grad_type(SNodeGradType.ADJOINT_CHECKBIT)
             x.ptr.set_adjoint_checkbit(x_grad_checkbit.ptr)

python/quadrants/lang/matrix.py

@@ -176,7 +176,7 @@ def make_matrix(arr, dt=None):
     if len(arr) == 0:
         # the only usage of an empty vector is to serve as field indices
         shape = [0]
-        dt = primitive_types.i32
+        dt = cook_dtype(primitive_types.i32)
     else:
         if isinstance(arr[0], Iterable):  # matrix
             shape = [len(arr), len(arr[0])]

python/quadrants/linalg/sparse_matrix.py

@@ -9,6 +9,7 @@
 from quadrants.lang.exception import QuadrantsRuntimeError
 from quadrants.lang.field import Field
 from quadrants.lang.impl import get_runtime
+from quadrants.lang.util import cook_dtype
 from quadrants.types import f32
 
 
@@ -24,11 +25,12 @@ class SparseMatrix:
     """
 
     def __init__(self, n=None, m=None, sm=None, dtype=f32, storage_format="col_major"):
-        self.dtype = dtype
+        dtype_cxx = cook_dtype(dtype)
+        self.dtype = dtype_cxx
         if sm is None:
             self.n = n
             self.m = m if m else n
-            self.matrix = get_runtime().prog.create_sparse_matrix(n, m, dtype, storage_format)
+            self.matrix = get_runtime().prog.create_sparse_matrix(n, m, dtype_cxx, storage_format)
         else:
             self.n = sm.num_rows()
             self.m = sm.num_cols()
@@ -247,7 +249,8 @@ def __init__(
     ):
         self.num_rows = num_rows
         self.num_cols = num_cols if num_cols else num_rows
-        self.dtype = dtype
+        dtype_cxx = cook_dtype(dtype)
+        self.dtype = dtype_cxx
         if num_rows is not None:
             quadrants_arch = get_runtime().prog.config().arch
             if quadrants_arch in [
@@ -259,7 +262,7 @@ def __init__(
                     num_rows,
                     num_cols,
                     max_num_triplets,
-                    dtype,
+                    dtype_cxx,
                     storage_format,
                 )
                 self.ptr.create_ndarray(get_runtime().prog)

python/quadrants/linalg/sparse_solver.py

@@ -8,6 +8,7 @@
 from quadrants.lang.exception import QuadrantsRuntimeError
 from quadrants.lang.field import Field
 from quadrants.lang.impl import get_runtime
+from quadrants.lang.util import cook_dtype
 from quadrants.linalg.sparse_matrix import SparseMatrix
 from quadrants.types.primitive_types import f32
 
@@ -24,7 +25,8 @@ class SparseSolver:
 
     def __init__(self, dtype=f32, solver_type="LLT", ordering="AMD"):
         self.matrix = None
-        self.dtype = dtype
+        dtype_cxx = cook_dtype(dtype)
+        self.dtype = dtype_cxx
         solver_type_list = ["LLT", "LDLT", "LU"]
         solver_ordering = ["AMD", "COLAMD"]
         if solver_type in solver_type_list and ordering in solver_ordering:
@@ -35,9 +37,9 @@ def __init__(self, dtype=f32, solver_type="LLT", ordering="AMD"):
                 or quadrants_arch == _qd_core.Arch.cuda
             ), "SparseSolver only supports CPU and CUDA for now."
             if quadrants_arch == _qd_core.Arch.cuda:
-                self.solver = _qd_core.make_cusparse_solver(dtype, solver_type, ordering)
+                self.solver = _qd_core.make_cusparse_solver(dtype_cxx, solver_type, ordering)
             else:
-                self.solver = _qd_core.make_sparse_solver(dtype, solver_type, ordering)
+                self.solver = _qd_core.make_sparse_solver(dtype_cxx, solver_type, ordering)
         else:
             raise QuadrantsRuntimeError(
                 f"The solver type {solver_type} with {ordering} is not supported for now. Only {solver_type_list} with {solver_ordering} are supported."