Reapply "[mlir][py] better support for arith.constant construction" (#84142)

Arithmetic constants for vector types can be constructed from objects
implementing Python buffer protocol such as `array.array`. Note that
until Python 3.12, there is no typing support for buffer protocol
implementers, so the annotations use array explicitly.

Reverts #84103

Author: ftynse

mlir/python/mlir/dialects/arith.py

@@ -5,6 +5,8 @@
 from ._arith_ops_gen import *
 from ._arith_ops_gen import _Dialect
 from ._arith_enum_gen import *
+from array import array as _array
+from typing import overload
 
 try:
     from ..ir import *
@@ -43,13 +45,37 @@ def _is_float_type(type: Type):
 class ConstantOp(ConstantOp):
     """Specialization for the constant op class."""
 
+    @overload
+    def __init__(self, value: Attribute, *, loc=None, ip=None):
+        ...
+
+    @overload
     def __init__(
-        self, result: Type, value: Union[int, float, Attribute], *, loc=None, ip=None
+        self, result: Type, value: Union[int, float, _array], *, loc=None, ip=None
     ):
+        ...
+
+    def __init__(self, result, value, *, loc=None, ip=None):
+        if value is None:
+            assert isinstance(result, Attribute)
+            super().__init__(result, loc=loc, ip=ip)
+            return
+
         if isinstance(value, int):
             super().__init__(IntegerAttr.get(result, value), loc=loc, ip=ip)
         elif isinstance(value, float):
             super().__init__(FloatAttr.get(result, value), loc=loc, ip=ip)
+        elif isinstance(value, _array):
+            if 8 * value.itemsize != result.element_type.width:
+                raise ValueError(
+                    f"Mismatching array element ({8 * value.itemsize}) and type ({result.element_type.width}) width."
+                )
+            if value.typecode in ["i", "l", "q"]:
+                super().__init__(DenseIntElementsAttr.get(value, type=result))
+            elif value.typecode in ["f", "d"]:
+                super().__init__(DenseFPElementsAttr.get(value, type=result))
+            else:
+                raise ValueError(f'Unsupported typecode: "{value.typecode}".')
         else:
             super().__init__(value, loc=loc, ip=ip)
 
@@ -79,6 +105,6 @@ def literal_value(self) -> Union[int, float]:
 
 
 def constant(
-    result: Type, value: Union[int, float, Attribute], *, loc=None, ip=None
+    result: Type, value: Union[int, float, Attribute, _array], *, loc=None, ip=None
 ) -> Value:
     return _get_op_result_or_op_results(ConstantOp(result, value, loc=loc, ip=ip))

mlir/test/python/dialects/arith_dialect.py

@@ -4,6 +4,7 @@
 from mlir.ir import *
 import mlir.dialects.arith as arith
 import mlir.dialects.func as func
+from array import array
 
 
 def run(f):
@@ -92,3 +93,42 @@ def __str__(self):
             b = a * a
             # CHECK: ArithValue(%2 = arith.mulf %cst_1, %cst_1 : f64)
             print(b)
+
+
+# CHECK-LABEL: TEST: testArrayConstantConstruction
+@run
+def testArrayConstantConstruction():
+    with Context(), Location.unknown():
+        module = Module.create()
+        with InsertionPoint(module.body):
+            i32_array = array("i", [1, 2, 3, 4])
+            i32 = IntegerType.get_signless(32)
+            vec_i32 = VectorType.get([2, 2], i32)
+            arith.constant(vec_i32, i32_array)
+            arith.ConstantOp(vec_i32, DenseIntElementsAttr.get(i32_array, type=vec_i32))
+
+            # "q" is the equivalent of `long long` in C and requires at least
+            # 64 bit width integers on both Linux and Windows.
+            i64_array = array("q", [5, 6, 7, 8])
+            i64 = IntegerType.get_signless(64)
+            vec_i64 = VectorType.get([1, 4], i64)
+            arith.constant(vec_i64, i64_array)
+            arith.ConstantOp(vec_i64, DenseIntElementsAttr.get(i64_array, type=vec_i64))
+
+            f32_array = array("f", [1.0, 2.0, 3.0, 4.0])
+            f32 = F32Type.get()
+            vec_f32 = VectorType.get([4, 1], f32)
+            arith.constant(vec_f32, f32_array)
+            arith.ConstantOp(vec_f32, DenseFPElementsAttr.get(f32_array, type=vec_f32))
+
+            f64_array = array("d", [1.0, 2.0, 3.0, 4.0])
+            f64 = F64Type.get()
+            vec_f64 = VectorType.get([2, 1, 2], f64)
+            arith.constant(vec_f64, f64_array)
+            arith.ConstantOp(vec_f64, DenseFPElementsAttr.get(f64_array, type=vec_f64))
+
+        # CHECK-COUNT-2: arith.constant dense<[{{\[}}1, 2], [3, 4]]> : vector<2x2xi32>
+        # CHECK-COUNT-2: arith.constant dense<[{{\[}}5, 6, 7, 8]]> : vector<1x4xi64>
+        # CHECK-COUNT-2: arith.constant dense<[{{\[}}1.000000e+00], [2.000000e+00], [3.000000e+00], [4.000000e+00]]> : vector<4x1xf32>
+        # CHECK-COUNT-2: arith.constant dense<[{{\[}}[1.000000e+00, 2.000000e+00]], [{{\[}}3.000000e+00, 4.000000e+00]]]> : vector<2x1x2xf64>
+        print(module)