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llvm/mlir/test/python/dialects/transform.py

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# RUN: %PYTHON %s | FileCheck %s
from mlir.ir import *
from mlir.dialects import transform
from mlir.dialects.transform import pdl as transform_pdl
def run(f):
with Context(), Location.unknown():
module = Module.create()
with InsertionPoint(module.body):
print("\nTEST:", f.__name__)
f(module)
print(module)
return f
@run
def testTypes(module: Module):
# CHECK-LABEL: TEST: testTypes
# CHECK: !transform.any_op
any_op = transform.AnyOpType.get()
print(any_op)
# CHECK: !transform.any_param
any_param = transform.AnyParamType.get()
print(any_param)
# CHECK: !transform.any_value
any_value = transform.AnyValueType.get()
print(any_value)
# CHECK: !transform.op<"foo.bar">
# CHECK: foo.bar
concrete_op = transform.OperationType.get("foo.bar")
print(concrete_op)
print(concrete_op.operation_name)
# CHECK: !transform.param<i32>
# CHECK: i32
param = transform.ParamType.get(IntegerType.get_signless(32))
print(param)
print(param.type)
@run
def testSequenceOp(module: Module):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[transform.AnyOpType.get()],
transform.AnyOpType.get(),
)
with InsertionPoint(sequence.body):
res = transform.CastOp(transform.AnyOpType.get(), sequence.bodyTarget)
res2 = transform.cast(transform.any_op_t(), res.result)
transform.YieldOp([res2])
# CHECK-LABEL: TEST: testSequenceOp
# CHECK: transform.sequence
# CHECK: ^{{.*}}(%[[ARG0:.+]]: !transform.any_op):
# CHECK: %[[RES:.+]] = cast %[[ARG0]] : !transform.any_op to !transform.any_op
# CHECK: %[[RES2:.+]] = cast %[[RES]] : !transform.any_op to !transform.any_op
# CHECK: yield %[[RES2]] : !transform.any_op
# CHECK: }
@run
def testSequenceOp(module: Module):
sequence = transform.SequenceOp(
[mlir][python bindings] generate all the enums This PR implements python enum bindings for *all* the enums - this includes `I*Attrs` (including positional/bit) and `Dialect/EnumAttr`. There are a few parts to this: 1. CMake: a small addition to `declare_mlir_dialect_python_bindings` and `declare_mlir_dialect_extension_python_bindings` to generate the enum, a boolean arg `GEN_ENUM_BINDINGS` to make it opt-in (even though it works for basically all of the dialects), and an optional `GEN_ENUM_BINDINGS_TD_FILE` for handling corner cases. 2. EnumPythonBindingGen.cpp: there are two weedy aspects here that took investigation: 1. If an enum attribute is not a `Dialect/EnumAttr` then the `EnumAttrInfo` record is canonical, as far as both the cases of the enum **and the `AttrDefName`**. On the otherhand, if an enum is a `Dialect/EnumAttr` then the `EnumAttr` record has the correct `AttrDefName` ("load bearing", i.e., populates `ods.ir.AttributeBuilder('<NAME>')`) but its `enum` field contains the cases, which is an instance of `EnumAttrInfo`. The solution is to generate an one enum class for both `Dialect/EnumAttr` and "independent" `EnumAttrInfo` but to make that class interopable with two builder registrations that both do the right thing (see next sub-bullet). 2. Because we don't have a good connection to cpp `EnumAttr`, i.e., only the `enum class` getters are exposed (like `DimensionAttr::get(Dimension value)`), we have to resort to parsing e.g., `Attribute.parse(f'#gpu<dim {x}>')`. This means that the set of supported `assemblyFormat`s (for the enum) is fixed at compile of MLIR (currently 2, the only 2 I saw). There might be some things that could be done here but they would require quite a bit more C API work to support generically (e.g., casting ints to enum cases and binding all the getters or going generically through the `symbolize*` methods, like `symbolizeDimension(uint32_t)` or `symbolizeDimension(StringRef)`). A few small changes: 1. In addition, since this patch registers default builders for attributes where people might've had their own builders already written, I added a `replace` param to `AttributeBuilder.insert` (`False` by default). 2. `makePythonEnumCaseName` can't handle all the different ways in which people write their enum cases, e.g., `llvm.CConv.Intel_OCL_BI`, which gets turned into `INTEL_O_C_L_B_I` (because `llvm::convertToSnakeFromCamelCase` doesn't look for runs of caps). So I dropped it. On the otherhand regularization does need to done because some enums have `None` as a case (and others might have other python keywords). 3. I turned on `llvm` dialect generation here in order to test `nvvm.WGMMAScaleIn`, which is an enum with [[ https://github.com/llvm/llvm-project/blob/d7e26b56207cbd8995296c5bb7c11ce676b649da/mlir/include/mlir/IR/EnumAttr.td#L22-L25 | no explicit discriminator ]] for the `neg` case. Note, dialects that didn't get a `GEN_ENUM_BINDINGS` don't have any enums to generate. Let me know if I should add more tests (the three trivial ones I added exercise both the supported `assemblyFormat`s and `replace=True`). Reviewed By: stellaraccident Differential Revision: https://reviews.llvm.org/D157934
2023-08-23 13:27:08 -05:00
transform.FailurePropagationMode.Propagate,
[transform.AnyOpType.get()],
transform.AnyOpType.get(),
)
with InsertionPoint(sequence.body):
transform.YieldOp([sequence.bodyTarget])
# CHECK-LABEL: TEST: testSequenceOp
# CHECK: = transform.sequence -> !transform.any_op failures(propagate) {
# CHECK: ^{{.*}}(%[[ARG0:.+]]: !transform.any_op):
# CHECK: yield %[[ARG0]] : !transform.any_op
# CHECK: }
@run
def testNestedSequenceOp(module: Module):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate, [], transform.AnyOpType.get()
)
with InsertionPoint(sequence.body):
nested = transform.SequenceOp(
transform.FailurePropagationMode.Propagate, [], sequence.bodyTarget
)
with InsertionPoint(nested.body):
doubly_nested = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[transform.AnyOpType.get()],
nested.bodyTarget,
)
with InsertionPoint(doubly_nested.body):
transform.YieldOp([doubly_nested.bodyTarget])
transform.YieldOp()
transform.YieldOp()
# CHECK-LABEL: TEST: testNestedSequenceOp
# CHECK: transform.sequence failures(propagate) {
# CHECK: ^{{.*}}(%[[ARG0:.+]]: !transform.any_op):
# CHECK: sequence %[[ARG0]] : !transform.any_op failures(propagate) {
# CHECK: ^{{.*}}(%[[ARG1:.+]]: !transform.any_op):
# CHECK: = sequence %[[ARG1]] : !transform.any_op -> !transform.any_op failures(propagate) {
# CHECK: ^{{.*}}(%[[ARG2:.+]]: !transform.any_op):
# CHECK: yield %[[ARG2]] : !transform.any_op
# CHECK: }
# CHECK: }
# CHECK: }
@run
def testSequenceOpWithExtras(module: Module):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[],
transform.AnyOpType.get(),
[transform.AnyOpType.get(), transform.OperationType.get("foo.bar")],
)
with InsertionPoint(sequence.body):
transform.YieldOp()
# CHECK-LABEL: TEST: testSequenceOpWithExtras
# CHECK: transform.sequence failures(propagate)
# CHECK: ^{{.*}}(%{{.*}}: !transform.any_op, %{{.*}}: !transform.any_op, %{{.*}}: !transform.op<"foo.bar">):
sequence = transform.sequence(
transform.FailurePropagationMode.Propagate,
[],
transform.AnyOpType.get(),
[transform.AnyOpType.get(), transform.OperationType.get("foo.bar")],
)
with InsertionPoint(sequence.body):
transform.yield_()
# CHECK: transform.sequence failures(propagate)
# CHECK: ^{{.*}}(%{{.*}}: !transform.any_op, %{{.*}}: !transform.any_op, %{{.*}}: !transform.op<"foo.bar">):
@run
def testNestedSequenceOpWithExtras(module: Module):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[],
transform.AnyOpType.get(),
[transform.AnyOpType.get(), transform.OperationType.get("foo.bar")],
)
with InsertionPoint(sequence.body):
nested = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[],
sequence.bodyTarget,
sequence.bodyExtraArgs,
)
with InsertionPoint(nested.body):
transform.YieldOp()
transform.YieldOp()
# CHECK-LABEL: TEST: testNestedSequenceOpWithExtras
# CHECK: transform.sequence failures(propagate)
# CHECK: ^{{.*}}(%[[ARG0:.*]]: !transform.any_op, %[[ARG1:.*]]: !transform.any_op, %[[ARG2:.*]]: !transform.op<"foo.bar">):
# CHECK: sequence %[[ARG0]], %[[ARG1]], %[[ARG2]] : (!transform.any_op, !transform.any_op, !transform.op<"foo.bar">)
@run
def testTransformPDLOps(module: Module):
withPdl = transform_pdl.WithPDLPatternsOp(transform.AnyOpType.get())
with InsertionPoint(withPdl.body):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[transform.AnyOpType.get()],
withPdl.bodyTarget,
)
with InsertionPoint(sequence.body):
match = transform_pdl.PDLMatchOp(
transform.AnyOpType.get(), sequence.bodyTarget, "pdl_matcher"
)
transform.YieldOp(match)
# CHECK-LABEL: TEST: testTransformPDLOps
# CHECK: transform.with_pdl_patterns {
# CHECK: ^{{.*}}(%[[ARG0:.+]]: !transform.any_op):
# CHECK: = sequence %[[ARG0]] : !transform.any_op -> !transform.any_op failures(propagate) {
# CHECK: ^{{.*}}(%[[ARG1:.+]]: !transform.any_op):
# CHECK: %[[RES:.+]] = pdl_match @pdl_matcher in %[[ARG1]]
# CHECK: yield %[[RES]] : !transform.any_op
# CHECK: }
# CHECK: }
@run
def testNamedSequenceOp(module: Module):
module.operation.attributes["transform.with_named_sequence"] = UnitAttr.get()
named_sequence = transform.NamedSequenceOp(
"__transform_main",
[transform.AnyOpType.get()],
[transform.AnyOpType.get()],
arg_attrs=[{"transform.consumed": UnitAttr.get()}],
)
with InsertionPoint(named_sequence.body):
transform.YieldOp([named_sequence.bodyTarget])
# CHECK-LABEL: TEST: testNamedSequenceOp
# CHECK: module attributes {transform.with_named_sequence} {
# CHECK: transform.named_sequence @__transform_main(%[[ARG0:.+]]: !transform.any_op {transform.consumed}) -> !transform.any_op {
# CHECK: yield %[[ARG0]] : !transform.any_op
named_sequence = transform.named_sequence(
"other_seq",
[transform.AnyOpType.get()],
[transform.AnyOpType.get()],
arg_attrs=[{"transform.consumed": UnitAttr.get()}],
)
with InsertionPoint(named_sequence.body):
transform.yield_([named_sequence.bodyTarget])
# CHECK: transform.named_sequence @other_seq(%[[ARG1:.+]]: !transform.any_op {transform.consumed}) -> !transform.any_op {
# CHECK: yield %[[ARG1]] : !transform.any_op
@run
def testGetParentOp(module: Module):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate, [], transform.AnyOpType.get()
)
with InsertionPoint(sequence.body):
transform.GetParentOp(
transform.AnyOpType.get(),
sequence.bodyTarget,
isolated_from_above=True,
nth_parent=2,
)
transform.get_parent_op(
transform.AnyOpType.get(),
sequence.bodyTarget,
isolated_from_above=True,
nth_parent=2,
allow_empty_results=True,
op_name="func.func",
deduplicate=True,
)
transform.YieldOp()
# CHECK-LABEL: TEST: testGetParentOp
# CHECK: transform.sequence
# CHECK: ^{{.*}}(%[[ARG1:.+]]: !transform.any_op):
# CHECK: = get_parent_op %[[ARG1]] {isolated_from_above, nth_parent = 2 : i64}
# CHECK: = get_parent_op %[[ARG1]] {allow_empty_results, deduplicate, isolated_from_above, nth_parent = 2 : i64, op_name = "func.func"}
@run
def testMergeHandlesOp(module: Module):
sequence = transform.SequenceOp(
[mlir][python bindings] generate all the enums This PR implements python enum bindings for *all* the enums - this includes `I*Attrs` (including positional/bit) and `Dialect/EnumAttr`. There are a few parts to this: 1. CMake: a small addition to `declare_mlir_dialect_python_bindings` and `declare_mlir_dialect_extension_python_bindings` to generate the enum, a boolean arg `GEN_ENUM_BINDINGS` to make it opt-in (even though it works for basically all of the dialects), and an optional `GEN_ENUM_BINDINGS_TD_FILE` for handling corner cases. 2. EnumPythonBindingGen.cpp: there are two weedy aspects here that took investigation: 1. If an enum attribute is not a `Dialect/EnumAttr` then the `EnumAttrInfo` record is canonical, as far as both the cases of the enum **and the `AttrDefName`**. On the otherhand, if an enum is a `Dialect/EnumAttr` then the `EnumAttr` record has the correct `AttrDefName` ("load bearing", i.e., populates `ods.ir.AttributeBuilder('<NAME>')`) but its `enum` field contains the cases, which is an instance of `EnumAttrInfo`. The solution is to generate an one enum class for both `Dialect/EnumAttr` and "independent" `EnumAttrInfo` but to make that class interopable with two builder registrations that both do the right thing (see next sub-bullet). 2. Because we don't have a good connection to cpp `EnumAttr`, i.e., only the `enum class` getters are exposed (like `DimensionAttr::get(Dimension value)`), we have to resort to parsing e.g., `Attribute.parse(f'#gpu<dim {x}>')`. This means that the set of supported `assemblyFormat`s (for the enum) is fixed at compile of MLIR (currently 2, the only 2 I saw). There might be some things that could be done here but they would require quite a bit more C API work to support generically (e.g., casting ints to enum cases and binding all the getters or going generically through the `symbolize*` methods, like `symbolizeDimension(uint32_t)` or `symbolizeDimension(StringRef)`). A few small changes: 1. In addition, since this patch registers default builders for attributes where people might've had their own builders already written, I added a `replace` param to `AttributeBuilder.insert` (`False` by default). 2. `makePythonEnumCaseName` can't handle all the different ways in which people write their enum cases, e.g., `llvm.CConv.Intel_OCL_BI`, which gets turned into `INTEL_O_C_L_B_I` (because `llvm::convertToSnakeFromCamelCase` doesn't look for runs of caps). So I dropped it. On the otherhand regularization does need to done because some enums have `None` as a case (and others might have other python keywords). 3. I turned on `llvm` dialect generation here in order to test `nvvm.WGMMAScaleIn`, which is an enum with [[ https://github.com/llvm/llvm-project/blob/d7e26b56207cbd8995296c5bb7c11ce676b649da/mlir/include/mlir/IR/EnumAttr.td#L22-L25 | no explicit discriminator ]] for the `neg` case. Note, dialects that didn't get a `GEN_ENUM_BINDINGS` don't have any enums to generate. Let me know if I should add more tests (the three trivial ones I added exercise both the supported `assemblyFormat`s and `replace=True`). Reviewed By: stellaraccident Differential Revision: https://reviews.llvm.org/D157934
2023-08-23 13:27:08 -05:00
transform.FailurePropagationMode.Propagate, [], transform.AnyOpType.get()
)
with InsertionPoint(sequence.body):
res = transform.MergeHandlesOp([sequence.bodyTarget])
transform.merge_handles([res.result], deduplicate=True)
transform.YieldOp()
# CHECK-LABEL: TEST: testMergeHandlesOp
# CHECK: transform.sequence
# CHECK: ^{{.*}}(%[[ARG1:.+]]: !transform.any_op):
# CHECK: %[[RES1:.+]] = merge_handles %[[ARG1]] : !transform.any_op
# CHECK: = merge_handles deduplicate %[[RES1]] : !transform.any_op
@run
def testApplyPatternsOpCompact(module: Module):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate, [], transform.AnyOpType.get()
)
with InsertionPoint(sequence.body):
with InsertionPoint(transform.ApplyPatternsOp(sequence.bodyTarget).patterns):
transform.ApplyCanonicalizationPatternsOp()
with InsertionPoint(
transform.apply_patterns(
sequence.bodyTarget,
apply_cse=True,
max_iterations=3,
max_num_rewrites=5,
).patterns
):
transform.ApplyCanonicalizationPatternsOp()
transform.YieldOp()
# CHECK-LABEL: TEST: testApplyPatternsOpCompact
# CHECK: apply_patterns to
# CHECK: transform.apply_patterns.canonicalization
# CHECK: } : !transform.any_op
# CHECK: apply_patterns to
# CHECK: transform.apply_patterns.canonicalization
# CHECK: } {apply_cse, max_iterations = 3 : i64, max_num_rewrites = 5 : i64} : !transform.any_op
@run
def testApplyPatternsOpWithType(module: Module):
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[],
transform.OperationType.get("test.dummy"),
)
with InsertionPoint(sequence.body):
with InsertionPoint(transform.ApplyPatternsOp(sequence.bodyTarget).patterns):
transform.ApplyCanonicalizationPatternsOp()
transform.YieldOp()
# CHECK-LABEL: TEST: testApplyPatternsOp
# CHECK: apply_patterns to
# CHECK: transform.apply_patterns.canonicalization
# CHECK: !transform.op<"test.dummy">
@run
def testReplicateOp(module: Module):
with_pdl = transform_pdl.WithPDLPatternsOp(transform.AnyOpType.get())
with InsertionPoint(with_pdl.body):
sequence = transform.SequenceOp(
[mlir][python bindings] generate all the enums This PR implements python enum bindings for *all* the enums - this includes `I*Attrs` (including positional/bit) and `Dialect/EnumAttr`. There are a few parts to this: 1. CMake: a small addition to `declare_mlir_dialect_python_bindings` and `declare_mlir_dialect_extension_python_bindings` to generate the enum, a boolean arg `GEN_ENUM_BINDINGS` to make it opt-in (even though it works for basically all of the dialects), and an optional `GEN_ENUM_BINDINGS_TD_FILE` for handling corner cases. 2. EnumPythonBindingGen.cpp: there are two weedy aspects here that took investigation: 1. If an enum attribute is not a `Dialect/EnumAttr` then the `EnumAttrInfo` record is canonical, as far as both the cases of the enum **and the `AttrDefName`**. On the otherhand, if an enum is a `Dialect/EnumAttr` then the `EnumAttr` record has the correct `AttrDefName` ("load bearing", i.e., populates `ods.ir.AttributeBuilder('<NAME>')`) but its `enum` field contains the cases, which is an instance of `EnumAttrInfo`. The solution is to generate an one enum class for both `Dialect/EnumAttr` and "independent" `EnumAttrInfo` but to make that class interopable with two builder registrations that both do the right thing (see next sub-bullet). 2. Because we don't have a good connection to cpp `EnumAttr`, i.e., only the `enum class` getters are exposed (like `DimensionAttr::get(Dimension value)`), we have to resort to parsing e.g., `Attribute.parse(f'#gpu<dim {x}>')`. This means that the set of supported `assemblyFormat`s (for the enum) is fixed at compile of MLIR (currently 2, the only 2 I saw). There might be some things that could be done here but they would require quite a bit more C API work to support generically (e.g., casting ints to enum cases and binding all the getters or going generically through the `symbolize*` methods, like `symbolizeDimension(uint32_t)` or `symbolizeDimension(StringRef)`). A few small changes: 1. In addition, since this patch registers default builders for attributes where people might've had their own builders already written, I added a `replace` param to `AttributeBuilder.insert` (`False` by default). 2. `makePythonEnumCaseName` can't handle all the different ways in which people write their enum cases, e.g., `llvm.CConv.Intel_OCL_BI`, which gets turned into `INTEL_O_C_L_B_I` (because `llvm::convertToSnakeFromCamelCase` doesn't look for runs of caps). So I dropped it. On the otherhand regularization does need to done because some enums have `None` as a case (and others might have other python keywords). 3. I turned on `llvm` dialect generation here in order to test `nvvm.WGMMAScaleIn`, which is an enum with [[ https://github.com/llvm/llvm-project/blob/d7e26b56207cbd8995296c5bb7c11ce676b649da/mlir/include/mlir/IR/EnumAttr.td#L22-L25 | no explicit discriminator ]] for the `neg` case. Note, dialects that didn't get a `GEN_ENUM_BINDINGS` don't have any enums to generate. Let me know if I should add more tests (the three trivial ones I added exercise both the supported `assemblyFormat`s and `replace=True`). Reviewed By: stellaraccident Differential Revision: https://reviews.llvm.org/D157934
2023-08-23 13:27:08 -05:00
transform.FailurePropagationMode.Propagate, [], with_pdl.bodyTarget
)
with InsertionPoint(sequence.body):
m1 = transform_pdl.PDLMatchOp(
transform.AnyOpType.get(), sequence.bodyTarget, "first"
)
m2 = transform_pdl.PDLMatchOp(
transform.AnyOpType.get(), sequence.bodyTarget, "second"
)
transform.ReplicateOp(m1, [m2])
transform.replicate(m1, [m2])
transform.YieldOp()
# CHECK-LABEL: TEST: testReplicateOp
# CHECK: %[[FIRST:.+]] = pdl_match
# CHECK: %[[SECOND:.+]] = pdl_match
# CHECK: %{{.*}} = replicate num(%[[FIRST]]) %[[SECOND]]
# CHECK: %{{.*}} = replicate num(%[[FIRST]]) %[[SECOND]]
# CHECK-LABEL: TEST: testApplyRegisteredPassOp
@run
def testApplyRegisteredPassOp(module: Module):
# CHECK: transform.sequence
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate, [], transform.AnyOpType.get()
)
with InsertionPoint(sequence.body):
# CHECK: %{{.*}} = apply_registered_pass "canonicalize" to {{.*}} : (!transform.any_op) -> !transform.any_op
mod = transform.ApplyRegisteredPassOp(
transform.AnyOpType.get(), sequence.bodyTarget, "canonicalize"
)
# CHECK: %{{.*}} = apply_registered_pass "canonicalize"
# CHECK-SAME: with options = {"top-down" = false}
# CHECK-SAME: to {{.*}} : (!transform.any_op) -> !transform.any_op
mod = transform.ApplyRegisteredPassOp(
transform.AnyOpType.get(),
mod.result,
"canonicalize",
options={"top-down": BoolAttr.get(False)},
)
# CHECK: %[[MAX_ITER:.+]] = transform.param.constant
max_iter = transform.param_constant(
transform.AnyParamType.get(),
IntegerAttr.get(IntegerType.get_signless(64), 10),
)
# CHECK: %[[MAX_REWRITE:.+]] = transform.param.constant
max_rewrites = transform.param_constant(
transform.AnyParamType.get(),
IntegerAttr.get(IntegerType.get_signless(64), 1),
)
# CHECK: %{{.*}} = apply_registered_pass "canonicalize"
# NB: MLIR has sorted the dict lexicographically by key:
# CHECK-SAME: with options = {"max-iterations" = %[[MAX_ITER]],
# CHECK-SAME: "max-rewrites" = %[[MAX_REWRITE]],
# CHECK-SAME: "test-convergence" = true,
# CHECK-SAME: "top-down" = false}
# CHECK-SAME: to %{{.*}} : (!transform.any_op, !transform.any_param, !transform.any_param) -> !transform.any_op
mod = transform.apply_registered_pass(
transform.AnyOpType.get(),
mod,
"canonicalize",
options={
"top-down": BoolAttr.get(False),
"max-iterations": max_iter,
"test-convergence": True,
"max-rewrites": max_rewrites,
},
)
# CHECK: %{{.*}} = apply_registered_pass "symbol-privatize"
# CHECK-SAME: with options = {"exclude" = ["a", "b"]}
# CHECK-SAME: to %{{.*}} : (!transform.any_op) -> !transform.any_op
mod = transform.apply_registered_pass(
transform.AnyOpType.get(),
mod,
"symbol-privatize",
options={"exclude": ("a", "b")},
)
# CHECK: %[[SYMBOL_A:.+]] = transform.param.constant
symbol_a = transform.param_constant(
transform.AnyParamType.get(), StringAttr.get("a")
)
# CHECK: %[[SYMBOL_B:.+]] = transform.param.constant
symbol_b = transform.param_constant(
transform.AnyParamType.get(), StringAttr.get("b")
)
# CHECK: %{{.*}} = apply_registered_pass "symbol-privatize"
# CHECK-SAME: with options = {"exclude" = [%[[SYMBOL_A]], %[[SYMBOL_B]]]}
# CHECK-SAME: to %{{.*}} : (!transform.any_op, !transform.any_param, !transform.any_param) -> !transform.any_op
mod = transform.apply_registered_pass(
transform.AnyOpType.get(),
mod,
"symbol-privatize",
options={"exclude": (symbol_a, symbol_b)},
)
transform.YieldOp()
# CHECK-LABEL: TEST: testForeachOp
@run
def testForeachOp(module: Module):
# CHECK: transform.sequence
sequence = transform.SequenceOp(
transform.FailurePropagationMode.Propagate,
[transform.AnyOpType.get()],
transform.AnyOpType.get(),
)
with InsertionPoint(sequence.body):
# CHECK: {{.*}} = foreach %{{.*}} : !transform.any_op -> !transform.any_op
foreach1 = transform.ForeachOp(
(transform.AnyOpType.get(),), (sequence.bodyTarget,)
)
with InsertionPoint(foreach1.body):
# CHECK: transform.yield {{.*}} : !transform.any_op
transform.yield_(foreach1.bodyTargets)
a_val = transform.get_operand(
transform.AnyValueType.get(), foreach1.result, [0]
)
a_param = transform.param_constant(
transform.AnyParamType.get(), StringAttr.get("a_param")
)
# CHECK: {{.*}} = foreach %{{.*}}, %{{.*}}, %{{.*}} : !transform.any_op, !transform.any_value, !transform.any_param -> !transform.any_value, !transform.any_param
foreach2 = transform.foreach(
(transform.AnyValueType.get(), transform.AnyParamType.get()),
(sequence.bodyTarget, a_val, a_param),
)
with InsertionPoint(foreach2.owner.body):
# CHECK: transform.yield {{.*}} : !transform.any_value, !transform.any_param
transform.yield_(foreach2.owner.bodyTargets[1:3])
another_param = transform.param_constant(
transform.AnyParamType.get(), StringAttr.get("another_param")
)
params = transform.merge_handles([a_param, another_param])
# CHECK: {{.*}} = foreach %{{.*}}, %{{.*}}, %{{.*}} with_zip_shortest : !transform.any_op, !transform.any_param, !transform.any_param -> !transform.any_op
foreach3 = transform.foreach(
(transform.AnyOpType.get(),),
(foreach1.result, foreach2[1], params),
with_zip_shortest=True,
)
with InsertionPoint(foreach3.owner.body):
# CHECK: transform.yield {{.*}} : !transform.any_op
transform.yield_((foreach3.owner.bodyTargets[0],))
transform.yield_((foreach3,))