| # RUN: %PYTHON %s | FileCheck %s |
| |
| from mlir.ir import * |
| import mlir.dialects.func as func |
| import mlir.dialects.python_test as test |
| import mlir.dialects.tensor as tensor |
| import mlir.dialects.arith as arith |
| |
| test.register_python_test_dialect(get_dialect_registry()) |
| |
| |
| def run(f): |
| print("\nTEST:", f.__name__) |
| f() |
| return f |
| |
| |
| # CHECK-LABEL: TEST: testAttributes |
| @run |
| def testAttributes(): |
| with Context() as ctx, Location.unknown(): |
| # |
| # Check op construction with attributes. |
| # |
| |
| i32 = IntegerType.get_signless(32) |
| one = IntegerAttr.get(i32, 1) |
| two = IntegerAttr.get(i32, 2) |
| unit = UnitAttr.get() |
| |
| # CHECK: python_test.attributed_op { |
| # CHECK-DAG: mandatory_i32 = 1 : i32 |
| # CHECK-DAG: optional_i32 = 2 : i32 |
| # CHECK-DAG: unit |
| # CHECK: } |
| op = test.AttributedOp(one, optional_i32=two, unit=unit) |
| print(f"{op}") |
| |
| # CHECK: python_test.attributed_op { |
| # CHECK: mandatory_i32 = 2 : i32 |
| # CHECK: } |
| op2 = test.AttributedOp(two) |
| print(f"{op2}") |
| |
| # |
| # Check generic "attributes" access and mutation. |
| # |
| |
| assert "additional" not in op.attributes |
| |
| # CHECK: python_test.attributed_op { |
| # CHECK-DAG: additional = 1 : i32 |
| # CHECK-DAG: mandatory_i32 = 2 : i32 |
| # CHECK: } |
| op2.attributes["additional"] = one |
| print(f"{op2}") |
| |
| # CHECK: python_test.attributed_op { |
| # CHECK-DAG: additional = 2 : i32 |
| # CHECK-DAG: mandatory_i32 = 2 : i32 |
| # CHECK: } |
| op2.attributes["additional"] = two |
| print(f"{op2}") |
| |
| # CHECK: python_test.attributed_op { |
| # CHECK-NOT: additional = 2 : i32 |
| # CHECK: mandatory_i32 = 2 : i32 |
| # CHECK: } |
| del op2.attributes["additional"] |
| print(f"{op2}") |
| |
| try: |
| print(op.attributes["additional"]) |
| except KeyError: |
| pass |
| else: |
| assert False, "expected KeyError on unknown attribute key" |
| |
| # |
| # Check accessors to defined attributes. |
| # |
| |
| # CHECK: Mandatory: 1 |
| # CHECK: Optional: 2 |
| # CHECK: Unit: True |
| print(f"Mandatory: {op.mandatory_i32.value}") |
| print(f"Optional: {op.optional_i32.value}") |
| print(f"Unit: {op.unit}") |
| |
| # CHECK: Mandatory: 2 |
| # CHECK: Optional: None |
| # CHECK: Unit: False |
| print(f"Mandatory: {op2.mandatory_i32.value}") |
| print(f"Optional: {op2.optional_i32}") |
| print(f"Unit: {op2.unit}") |
| |
| # CHECK: Mandatory: 2 |
| # CHECK: Optional: None |
| # CHECK: Unit: False |
| op.mandatory_i32 = two |
| op.optional_i32 = None |
| op.unit = False |
| print(f"Mandatory: {op.mandatory_i32.value}") |
| print(f"Optional: {op.optional_i32}") |
| print(f"Unit: {op.unit}") |
| assert "optional_i32" not in op.attributes |
| assert "unit" not in op.attributes |
| |
| try: |
| op.mandatory_i32 = None |
| except ValueError: |
| pass |
| else: |
| assert False, "expected ValueError on setting a mandatory attribute to None" |
| |
| # CHECK: Optional: 2 |
| op.optional_i32 = two |
| print(f"Optional: {op.optional_i32.value}") |
| |
| # CHECK: Optional: None |
| del op.optional_i32 |
| print(f"Optional: {op.optional_i32}") |
| |
| # CHECK: Unit: False |
| op.unit = None |
| print(f"Unit: {op.unit}") |
| assert "unit" not in op.attributes |
| |
| # CHECK: Unit: True |
| op.unit = True |
| print(f"Unit: {op.unit}") |
| |
| # CHECK: Unit: False |
| del op.unit |
| print(f"Unit: {op.unit}") |
| |
| |
| # CHECK-LABEL: TEST: attrBuilder |
| @run |
| def attrBuilder(): |
| with Context() as ctx, Location.unknown(): |
| # CHECK: python_test.attributes_op |
| op = test.AttributesOp( |
| # CHECK-DAG: x_affinemap = affine_map<() -> (2)> |
| x_affinemap=AffineMap.get_constant(2), |
| # CHECK-DAG: x_affinemaparr = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>] |
| x_affinemaparr=[AffineMap.get_identity(3)], |
| # CHECK-DAG: x_arr = [true, "x"] |
| x_arr=[BoolAttr.get(True), StringAttr.get("x")], |
| x_boolarr=[False, True], # CHECK-DAG: x_boolarr = [false, true] |
| x_bool=True, # CHECK-DAG: x_bool = true |
| x_dboolarr=[True, False], # CHECK-DAG: x_dboolarr = array<i1: true, false> |
| x_df16arr=[21, 22], # CHECK-DAG: x_df16arr = array<i16: 21, 22> |
| # CHECK-DAG: x_df32arr = array<f32: 2.300000e+01, 2.400000e+01> |
| x_df32arr=[23, 24], |
| # CHECK-DAG: x_df64arr = array<f64: 2.500000e+01, 2.600000e+01> |
| x_df64arr=[25, 26], |
| x_di32arr=[0, 1], # CHECK-DAG: x_di32arr = array<i32: 0, 1> |
| # CHECK-DAG: x_di64arr = array<i64: 1, 2> |
| x_di64arr=[1, 2], |
| x_di8arr=[2, 3], # CHECK-DAG: x_di8arr = array<i8: 2, 3> |
| # CHECK-DAG: x_dictarr = [{a = false}] |
| x_dictarr=[{"a": BoolAttr.get(False)}], |
| x_dict={"b": BoolAttr.get(True)}, # CHECK-DAG: x_dict = {b = true} |
| x_f32=-2.25, # CHECK-DAG: x_f32 = -2.250000e+00 : f32 |
| # CHECK-DAG: x_f32arr = [2.000000e+00 : f32, 3.000000e+00 : f32] |
| x_f32arr=[2.0, 3.0], |
| x_f64=4.25, # CHECK-DAG: x_f64 = 4.250000e+00 : f64 |
| x_f64arr=[4.0, 8.0], # CHECK-DAG: x_f64arr = [4.000000e+00, 8.000000e+00] |
| # CHECK-DAG: x_f64elems = dense<[3.952530e-323, 7.905050e-323]> : tensor<2xf64> |
| x_f64elems=[8.0, 16.0], |
| # CHECK-DAG: x_flatsymrefarr = [@symbol1, @symbol2] |
| x_flatsymrefarr=["symbol1", "symbol2"], |
| x_flatsymref="symbol3", # CHECK-DAG: x_flatsymref = @symbol3 |
| x_i1=0, # CHECK-DAG: x_i1 = false |
| x_i16=42, # CHECK-DAG: x_i16 = 42 : i16 |
| x_i32=6, # CHECK-DAG: x_i32 = 6 : i32 |
| x_i32arr=[4, 5], # CHECK-DAG: x_i32arr = [4 : i32, 5 : i32] |
| x_i32elems=[5, 6], # CHECK-DAG: x_i32elems = dense<[5, 6]> : tensor<2xi32> |
| x_i64=9, # CHECK-DAG: x_i64 = 9 : i64 |
| x_i64arr=[7, 8], # CHECK-DAG: x_i64arr = [7, 8] |
| x_i64elems=[8, 9], # CHECK-DAG: x_i64elems = dense<[8, 9]> : tensor<2xi64> |
| x_i64svecarr=[10, 11], # CHECK-DAG: x_i64svecarr = [10, 11] |
| x_i8=11, # CHECK-DAG: x_i8 = 11 : i8 |
| x_idx=10, # CHECK-DAG: x_idx = 10 : index |
| # CHECK-DAG: x_idxelems = dense<[11, 12]> : tensor<2xindex> |
| x_idxelems=[11, 12], |
| # CHECK-DAG: x_idxlistarr = [{{\[}}13], [14, 15]] |
| x_idxlistarr=[[13], [14, 15]], |
| x_si1=-1, # CHECK-DAG: x_si1 = -1 : si1 |
| x_si16=-2, # CHECK-DAG: x_si16 = -2 : si16 |
| x_si32=-3, # CHECK-DAG: x_si32 = -3 : si32 |
| x_si64=-123, # CHECK-DAG: x_si64 = -123 : si64 |
| x_si8=-4, # CHECK-DAG: x_si8 = -4 : si8 |
| x_strarr=["hello", "world"], # CHECK-DAG: x_strarr = ["hello", "world"] |
| x_str="hello world!", # CHECK-DAG: x_str = "hello world!" |
| # CHECK-DAG: x_symrefarr = [@flatsym, @deep::@sym] |
| x_symrefarr=["flatsym", ["deep", "sym"]], |
| x_symref=["deep", "sym2"], # CHECK-DAG: x_symref = @deep::@sym2 |
| x_sym="symbol", # CHECK-DAG: x_sym = "symbol" |
| x_typearr=[F32Type.get()], # CHECK-DAG: x_typearr = [f32] |
| x_type=F64Type.get(), # CHECK-DAG: x_type = f64 |
| x_ui1=1, # CHECK-DAG: x_ui1 = 1 : ui1 |
| x_ui16=2, # CHECK-DAG: x_ui16 = 2 : ui16 |
| x_ui32=3, # CHECK-DAG: x_ui32 = 3 : ui32 |
| x_ui64=4, # CHECK-DAG: x_ui64 = 4 : ui64 |
| x_ui8=5, # CHECK-DAG: x_ui8 = 5 : ui8 |
| x_unit=True, # CHECK-DAG: x_unit |
| ) |
| op.verify() |
| op.print(use_local_scope=True) |
| |
| |
| # CHECK-LABEL: TEST: inferReturnTypes |
| @run |
| def inferReturnTypes(): |
| with Context() as ctx, Location.unknown(ctx): |
| module = Module.create() |
| with InsertionPoint(module.body): |
| op = test.InferResultsOp() |
| dummy = test.DummyOp() |
| |
| # CHECK: [Type(i32), Type(i64)] |
| iface = InferTypeOpInterface(op) |
| print(iface.inferReturnTypes()) |
| |
| # CHECK: [Type(i32), Type(i64)] |
| iface_static = InferTypeOpInterface(test.InferResultsOp) |
| print(iface.inferReturnTypes()) |
| |
| assert isinstance(iface.opview, test.InferResultsOp) |
| assert iface.opview == iface.operation.opview |
| |
| try: |
| iface_static.opview |
| except TypeError: |
| pass |
| else: |
| assert False, ( |
| "not expected to be able to obtain an opview from a static" " interface" |
| ) |
| |
| try: |
| InferTypeOpInterface(dummy) |
| except ValueError: |
| pass |
| else: |
| assert False, "not expected dummy op to implement the interface" |
| |
| try: |
| InferTypeOpInterface(test.DummyOp) |
| except ValueError: |
| pass |
| else: |
| assert False, "not expected dummy op class to implement the interface" |
| |
| |
| # CHECK-LABEL: TEST: resultTypesDefinedByTraits |
| @run |
| def resultTypesDefinedByTraits(): |
| with Context() as ctx, Location.unknown(ctx): |
| module = Module.create() |
| with InsertionPoint(module.body): |
| inferred = test.InferResultsOp() |
| same = test.SameOperandAndResultTypeOp([inferred.results[0]]) |
| # CHECK-COUNT-2: i32 |
| print(same.one.type) |
| print(same.two.type) |
| |
| first_type_attr = test.FirstAttrDeriveTypeAttrOp( |
| inferred.results[1], TypeAttr.get(IndexType.get()) |
| ) |
| # CHECK-COUNT-2: index |
| print(first_type_attr.one.type) |
| print(first_type_attr.two.type) |
| |
| first_attr = test.FirstAttrDeriveAttrOp(FloatAttr.get(F32Type.get(), 3.14)) |
| # CHECK-COUNT-3: f32 |
| print(first_attr.one.type) |
| print(first_attr.two.type) |
| print(first_attr.three.type) |
| |
| implied = test.InferResultsImpliedOp() |
| # CHECK: i32 |
| print(implied.integer.type) |
| # CHECK: f64 |
| print(implied.flt.type) |
| # CHECK: index |
| print(implied.index.type) |
| |
| |
| # CHECK-LABEL: TEST: testOptionalOperandOp |
| @run |
| def testOptionalOperandOp(): |
| with Context() as ctx, Location.unknown(): |
| module = Module.create() |
| with InsertionPoint(module.body): |
| op1 = test.OptionalOperandOp() |
| # CHECK: op1.input is None: True |
| print(f"op1.input is None: {op1.input is None}") |
| |
| op2 = test.OptionalOperandOp(input=op1) |
| # CHECK: op2.input is None: False |
| print(f"op2.input is None: {op2.input is None}") |
| |
| |
| # CHECK-LABEL: TEST: testCustomAttribute |
| @run |
| def testCustomAttribute(): |
| with Context() as ctx: |
| a = test.TestAttr.get() |
| # CHECK: #python_test.test_attr |
| print(a) |
| |
| # The following cast must not assert. |
| b = test.TestAttr(a) |
| |
| unit = UnitAttr.get() |
| try: |
| test.TestAttr(unit) |
| except ValueError as e: |
| assert "Cannot cast attribute to TestAttr" in str(e) |
| else: |
| raise |
| |
| # The following must trigger a TypeError from our adaptors and must not |
| # crash. |
| try: |
| test.TestAttr(42) |
| except TypeError as e: |
| assert "Expected an MLIR object" in str(e) |
| else: |
| raise |
| |
| # The following must trigger a TypeError from pybind (therefore, not |
| # checking its message) and must not crash. |
| try: |
| test.TestAttr(42, 56) |
| except TypeError: |
| pass |
| else: |
| raise |
| |
| |
| @run |
| def testCustomType(): |
| with Context() as ctx: |
| a = test.TestType.get() |
| # CHECK: !python_test.test_type |
| print(a) |
| |
| # The following cast must not assert. |
| b = test.TestType(a) |
| # Instance custom types should have typeids |
| assert isinstance(b.typeid, TypeID) |
| # Subclasses of ir.Type should not have a static_typeid |
| # CHECK: 'TestType' object has no attribute 'static_typeid' |
| try: |
| b.static_typeid |
| except AttributeError as e: |
| print(e) |
| |
| i8 = IntegerType.get_signless(8) |
| try: |
| test.TestType(i8) |
| except ValueError as e: |
| assert "Cannot cast type to TestType" in str(e) |
| else: |
| raise |
| |
| # The following must trigger a TypeError from our adaptors and must not |
| # crash. |
| try: |
| test.TestType(42) |
| except TypeError as e: |
| assert "Expected an MLIR object" in str(e) |
| else: |
| raise |
| |
| # The following must trigger a TypeError from pybind (therefore, not |
| # checking its message) and must not crash. |
| try: |
| test.TestType(42, 56) |
| except TypeError: |
| pass |
| else: |
| raise |
| |
| |
| @run |
| # CHECK-LABEL: TEST: testTensorValue |
| def testTensorValue(): |
| with Context() as ctx, Location.unknown(): |
| i8 = IntegerType.get_signless(8) |
| |
| class Tensor(test.TestTensorValue): |
| def __str__(self): |
| return super().__str__().replace("Value", "Tensor") |
| |
| module = Module.create() |
| with InsertionPoint(module.body): |
| t = tensor.EmptyOp([10, 10], i8).result |
| |
| # CHECK: Value(%{{.*}} = tensor.empty() : tensor<10x10xi8>) |
| print(Value(t)) |
| |
| tt = Tensor(t) |
| # CHECK: Tensor(%{{.*}} = tensor.empty() : tensor<10x10xi8>) |
| print(tt) |
| |
| # CHECK: False |
| print(tt.is_null()) |
| |
| # Classes of custom types that inherit from concrete types should have |
| # static_typeid |
| assert isinstance(test.TestIntegerRankedTensorType.static_typeid, TypeID) |
| # And it should be equal to the in-tree concrete type |
| assert test.TestIntegerRankedTensorType.static_typeid == t.type.typeid |
| |
| d = tensor.EmptyOp([1, 2, 3], IntegerType.get_signless(5)).result |
| # CHECK: Value(%{{.*}} = tensor.empty() : tensor<1x2x3xi5>) |
| print(d) |
| # CHECK: TestTensorValue |
| print(repr(d)) |
| |
| |
| # CHECK-LABEL: TEST: inferReturnTypeComponents |
| @run |
| def inferReturnTypeComponents(): |
| with Context() as ctx, Location.unknown(ctx): |
| module = Module.create() |
| i32 = IntegerType.get_signless(32) |
| with InsertionPoint(module.body): |
| resultType = UnrankedTensorType.get(i32) |
| operandTypes = [ |
| RankedTensorType.get([1, 3, 10, 10], i32), |
| UnrankedTensorType.get(i32), |
| ] |
| f = func.FuncOp( |
| "test_inferReturnTypeComponents", (operandTypes, [resultType]) |
| ) |
| entry_block = Block.create_at_start(f.operation.regions[0], operandTypes) |
| with InsertionPoint(entry_block): |
| ranked_op = test.InferShapedTypeComponentsOp( |
| resultType, entry_block.arguments[0] |
| ) |
| unranked_op = test.InferShapedTypeComponentsOp( |
| resultType, entry_block.arguments[1] |
| ) |
| |
| # CHECK: has rank: True |
| # CHECK: rank: 4 |
| # CHECK: element type: i32 |
| # CHECK: shape: [1, 3, 10, 10] |
| iface = InferShapedTypeOpInterface(ranked_op) |
| shaped_type_components = iface.inferReturnTypeComponents( |
| operands=[ranked_op.operand] |
| )[0] |
| print("has rank:", shaped_type_components.has_rank) |
| print("rank:", shaped_type_components.rank) |
| print("element type:", shaped_type_components.element_type) |
| print("shape:", shaped_type_components.shape) |
| |
| # CHECK: has rank: False |
| # CHECK: rank: None |
| # CHECK: element type: i32 |
| # CHECK: shape: None |
| iface = InferShapedTypeOpInterface(unranked_op) |
| shaped_type_components = iface.inferReturnTypeComponents( |
| operands=[unranked_op.operand] |
| )[0] |
| print("has rank:", shaped_type_components.has_rank) |
| print("rank:", shaped_type_components.rank) |
| print("element type:", shaped_type_components.element_type) |
| print("shape:", shaped_type_components.shape) |
| |
| |
| # CHECK-LABEL: TEST: testCustomTypeTypeCaster |
| @run |
| def testCustomTypeTypeCaster(): |
| with Context() as ctx, Location.unknown(): |
| a = test.TestType.get() |
| assert a.typeid is not None |
| |
| b = Type.parse("!python_test.test_type") |
| # CHECK: !python_test.test_type |
| print(b) |
| # CHECK: TestType(!python_test.test_type) |
| print(repr(b)) |
| |
| c = test.TestIntegerRankedTensorType.get([10, 10], 5) |
| # CHECK: tensor<10x10xi5> |
| print(c) |
| # CHECK: TestIntegerRankedTensorType(tensor<10x10xi5>) |
| print(repr(c)) |
| |
| # CHECK: Type caster is already registered |
| try: |
| |
| @register_type_caster(c.typeid) |
| def type_caster(pytype): |
| return test.TestIntegerRankedTensorType(pytype) |
| |
| except RuntimeError as e: |
| print(e) |
| |
| # python_test dialect registers a caster for RankedTensorType in its extension (pybind) module. |
| # So this one replaces that one (successfully). And then just to be sure we restore the original caster below. |
| @register_type_caster(c.typeid, replace=True) |
| def type_caster(pytype): |
| return RankedTensorType(pytype) |
| |
| d = tensor.EmptyOp([10, 10], IntegerType.get_signless(5)).result |
| # CHECK: tensor<10x10xi5> |
| print(d.type) |
| # CHECK: ranked tensor type RankedTensorType(tensor<10x10xi5>) |
| print("ranked tensor type", repr(d.type)) |
| |
| @register_type_caster(c.typeid, replace=True) |
| def type_caster(pytype): |
| return test.TestIntegerRankedTensorType(pytype) |
| |
| d = tensor.EmptyOp([10, 10], IntegerType.get_signless(5)).result |
| # CHECK: tensor<10x10xi5> |
| print(d.type) |
| # CHECK: TestIntegerRankedTensorType(tensor<10x10xi5>) |
| print(repr(d.type)) |
| |
| |
| # CHECK-LABEL: TEST: testInferTypeOpInterface |
| @run |
| def testInferTypeOpInterface(): |
| with Context() as ctx, Location.unknown(ctx): |
| module = Module.create() |
| with InsertionPoint(module.body): |
| i64 = IntegerType.get_signless(64) |
| zero = arith.ConstantOp(i64, 0) |
| |
| one_operand = test.InferResultsVariadicInputsOp(single=zero, doubled=None) |
| # CHECK: i32 |
| print(one_operand.result.type) |
| |
| two_operands = test.InferResultsVariadicInputsOp(single=zero, doubled=zero) |
| # CHECK: f32 |
| print(two_operands.result.type) |
