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b0e00ca6a605b88e83129c8c6be4e177f93cbfea
clang-p2996/mlir/test/python/dialects/python_test.py
Maksim Levental b0e00ca6a6 [mlir][python] fix replace=True for register_operation and register_type_caster (#70264) 
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src="https://github.com/llvm/llvm-project/assets/5657668/443852b6-ac25-45bb-a38b-5dfbda09d5a7"
height="400" />
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So turns out that none of the `replace=True` things actually work
because of the map caches (except for
`register_attribute_builder(replace=True)`, which doesn't use such a
cache). This was hidden by a series of unfortunate events:

1. `register_type_caster` failure was hidden because it was the same
`TestIntegerRankedTensorType` being replaced with itself (d'oh).
2. `register_operation` failure was hidden behind the "order of events"
in the lifecycle of typical extension import/use. Since extensions are
loaded/registered almost immediately after generated builders are
registered, there is no opportunity for the `operationClassMapCache` to
be populated (through e.g., `module.body.operations[2]` or
`module.body.operations[2].opview` or something). Of course as soon as
you as actually do "late-bind/late-register" the extension, you see it's
not successfully replacing the stale one in `operationClassMapCache`.

I'll take this opportunity to propose we ditch the caches all together.
I've been cargo-culting them but I really don't understand how they
work. There's this comment above `operationClassMapCache`

```cpp
  /// Cache of operation name to external operation class object. This is
  /// maintained on lookup as a shadow of operationClassMap in order for repeat
  /// lookups of the classes to only incur the cost of one hashtable lookup.
  llvm::StringMap<pybind11::object> operationClassMapCache;
```

But I don't understand how that's true given that the canonical thing
`operationClassMap` is already a map:

```cpp
  /// Map of full operation name to external operation class object.
  llvm::StringMap<pybind11::object> operationClassMap;
```

Maybe it wasn't always the case? Anyway things work now but it seems
like an unnecessary layer of complexity for not much gain? But maybe I'm
wrong.
2023-10-30 20:22:27 -05:00

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# 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
def run(f):
print("\nTEST:", f.__name__)
f()
return f
# CHECK-LABEL: TEST: testAttributes
@run
def testAttributes():
with Context() as ctx, Location.unknown():
ctx.allow_unregistered_dialects = True
#
# 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():
ctx.allow_unregistered_dialects = True
# 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<2xsi32>
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<2xsi64>
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):
test.register_python_test_dialect(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):
test.register_python_test_dialect(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():
test.register_python_test_dialect(ctx)
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:
test.register_python_test_dialect(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:
test.register_python_test_dialect(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():
test.register_python_test_dialect(ctx)
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
# CHECK-LABEL: TEST: inferReturnTypeComponents
@run
def inferReturnTypeComponents():
with Context() as ctx, Location.unknown(ctx):
test.register_python_test_dialect(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():
test.register_python_test_dialect(ctx)
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:
def type_caster(pytype):
return test.TestIntegerRankedTensorType(pytype)
register_type_caster(c.typeid, type_caster)
except RuntimeError as e:
print(e)
def type_caster(pytype):
return RankedTensorType(pytype)
# 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, type_caster, replace=True)
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))
def type_caster(pytype):
return test.TestIntegerRankedTensorType(pytype)
register_type_caster(c.typeid, type_caster, replace=True)
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):
test.register_python_test_dialect(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)
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