shylie/llvm-project
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

[mlir][linalg] Support ParamType in vector_sizes option of VectorizeOp transform (#87557)

Browse Source
This commit is contained in:
srcarroll 2024-04-09 15:52:40 -05:00 committed by GitHub
parent 470aefb240
commit b79db39659
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
6 changed files with 249 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
View File

@ -2138,25 +2138,16 @@ def VectorizeOp : Op<Transform_Dialect, "structured.vectorize",
}];
let arguments = (ins TransformHandleTypeInterface:$target,
Variadic<TransformHandleTypeInterface>:$vector_sizes,
Variadic<TransformAnyParamTypeOrAnyHandle>:$vector_sizes,
DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:
$static_vector_sizes,
OptionalAttr<UnitAttr>:$vectorize_nd_extract,
DefaultValuedOptionalAttr<DenseBoolArrayAttr, "{}">:
$scalable_sizes,
DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:
$static_vector_sizes);
$scalable_sizes);
let results = (outs);
let assemblyFormat = [{
$target oilist(
`vector_sizes` custom<DynamicIndexList>($vector_sizes,
$static_vector_sizes,
type($vector_sizes),
$scalable_sizes) |
`vectorize_nd_extract` $vectorize_nd_extract
)
attr-dict
`:` type($target)
}];
let hasCustomAssemblyFormat = 1;
let hasVerifier = 1;
let extraClassDeclaration = [{

82
mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
View File

@ -3122,6 +3122,81 @@ transform::VectorizeChildrenAndApplyPatternsOp::applyToOne(
//===----------------------------------------------------------------------===//
// VectorizeOp
//===----------------------------------------------------------------------===//
static const StringLiteral kVectorSizesKeyword = "vector_sizes";
ParseResult transform::VectorizeOp::parse(OpAsmParser &parser,
OperationState &result) {
OpAsmParser::UnresolvedOperand target;
SmallVector<OpAsmParser::UnresolvedOperand> dynamicSizes;
DenseI64ArrayAttr staticSizes;
SmallVector<Type> operandTypes;
llvm::SMLoc operandLoc;
DenseBoolArrayAttr scalableVals;
if (parser.parseOperand(target) || parser.getCurrentLocation(&operandLoc))
return ParseResult::failure();
if (succeeded(parser.parseOptionalKeyword(kVectorSizesKeyword))) {
if (failed(parseDynamicIndexList(parser, dynamicSizes, staticSizes,
scalableVals)))
return ParseResult::failure();
}
if (succeeded(parser.parseOptionalKeyword(
getVectorizeNdExtractAttrName(result.name))))
result.addAttribute(getVectorizeNdExtractAttrName(result.name),
parser.getBuilder().getUnitAttr());
if (parser.parseOptionalAttrDict(result.attributes) ||
parser.parseColonTypeList(operandTypes))
return ParseResult::failure();
if (operandTypes.size() != dynamicSizes.size() + 1) {
return parser.emitError(operandLoc)
<< "expected " << dynamicSizes.size() + 1 << " operand type(s)";
}
if (parser.resolveOperand(target, operandTypes.front(), result.operands) ||
parser.resolveOperands(dynamicSizes, ArrayRef(operandTypes).drop_front(),
operandLoc, result.operands)) {
return failure();
}
if (scalableVals)
result.addAttribute(getScalableSizesAttrName(result.name), scalableVals);
if (staticSizes)
result.addAttribute(getStaticVectorSizesAttrName(result.name), staticSizes);
return success();
}
void transform::VectorizeOp::print(OpAsmPrinter &p) {
p << ' ' << getTarget() << ' ';
if (!getMixedVectorSizes().empty()) {
p << kVectorSizesKeyword << ' ';
printDynamicIndexList(p, getOperation(), getVectorSizes(),
getStaticVectorSizesAttr(),
/*valueTypes=*/{}, getScalableSizesAttr(),
OpAsmParser::Delimiter::Square);
}
if (getVectorizeNdExtract())
p << getVectorizeNdExtractAttrName() << ' ';
p.printOptionalAttrDict(
(*this)->getAttrs(),
/*elidedAttrs=*/{
getScalableSizesAttrName(getOperation()->getName()),
getStaticVectorSizesAttrName(getOperation()->getName())});
p << " : ";
p << getTarget().getType();
if (!getVectorSizes().empty()) {
p << ", ";
llvm::interleaveComma(getVectorSizes(), p,
[&](Value operand) { p << operand.getType(); });
}
}
DiagnosedSilenceableFailure transform::VectorizeOp::apply(
transform::TransformRewriter &rewriter,
mlir::transform::TransformResults &transformResults,
@ -3136,6 +3211,13 @@ DiagnosedSilenceableFailure transform::VectorizeOp::apply(
auto attr = sz.get<Attribute>();
vectorSizes.push_back(cast<IntegerAttr>(attr).getInt());
continue;
} else if (sz.is<Value>() && isa<ParamType>(sz.get<Value>().getType())) {
ArrayRef<Attribute> params = state.getParams(sz.get<Value>());
if (params.size() != 1)
return emitSilenceableFailure(getLoc()) << "expected a single param";
vectorSizes.push_back(
cast<IntegerAttr>(params.front()).getValue().getSExtValue());
continue;
}
auto szPayloads = state.getPayloadOps(sz.get<Value>());

21
mlir/test/Dialect/Linalg/transform-ops-invalid.mlir
View File

@ -71,3 +71,24 @@ transform.sequence failures(propagate) {
: (!transform.any_op) -> !transform.op<"linalg.generic">
}
// -----
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op):
%0 = transform.param.constant 2 : i64 -> !transform.param<i64>
// expected-error@below {{custom op 'transform.structured.vectorize' expected 2 operand type(s)}}
transform.structured.vectorize %arg0 vector_sizes [%0, 2] : !transform.any_op, !transform.param<i64>, !transform.param<i64>
}
// -----
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op):
%0 = transform.param.constant 2 : i64 -> !transform.param<i64>
// expected-error@below {{expected ']' in dynamic index list}}
// expected-error@below {{custom op 'transform.structured.vectorize' expected SSA value or integer}}
transform.structured.vectorize %arg0 vector_sizes [%0 : !transform.param<i64>, 2] : !transform.any_op, !transform.param<i64>
}

11
mlir/test/Dialect/Linalg/transform-ops.mlir
View File

@ -1,4 +1,4 @@
// RUN: mlir-opt %s | mlir-opt | FileCheck %s
// RUN: mlir-opt %s --split-input-file | mlir-opt | FileCheck %s
transform.sequence failures(propagate) {
^bb1(%arg0: !transform.any_op):
@ -57,3 +57,12 @@ transform.sequence failures(propagate) {
%1:2 = transform.structured.fuse_into_containing_op %arg2 into %loop
: (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
}
// -----
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op):
// CHECK: transform.structured.vectorize %arg0 : !transform.any_op
transform.structured.vectorize %arg0 vector_sizes [] : !transform.any_op
}

118
mlir/test/Dialect/Linalg/vectorization.mlir
View File

@ -36,6 +36,81 @@ module attributes {transform.with_named_sequence} {
// -----
func.func @vectorize_dynamic_identity_with_constant(%arg0: tensor<?xf32>,
%arg1: tensor<?xf32>,
%arg2: tensor<?xf32>) -> tensor<?xf32> {
%c4 = arith.constant 4 : index
%0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (d0)>,
affine_map<(d0) -> (d0)>,
affine_map<(d0) -> (d0)>],
iterator_types = ["parallel"] }
ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
outs(%arg2 : tensor<?xf32>) {
^bb(%in0: f32, %in1: f32, %out: f32) :
%0 = arith.addf %in0, %in1 : f32
linalg.yield %0 : f32
} -> tensor<?xf32>
return %0 : tensor<?xf32>
}
// CHECK-LABEL: @vectorize_dynamic_identity_with_constant
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32>
// CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_4]] : vector<4xi1>
// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<4xf32>
// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%size = transform.structured.match ops{["arith.constant"]} in %arg1 : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [%size] : !transform.any_op, !transform.any_op
transform.yield
}
}
// -----
func.func @vectorize_dynamic_identity_with_param(%arg0: tensor<?xf32>,
%arg1: tensor<?xf32>,
%arg2: tensor<?xf32>) -> tensor<?xf32> {
%0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (d0)>,
affine_map<(d0) -> (d0)>,
affine_map<(d0) -> (d0)>],
iterator_types = ["parallel"] }
ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
outs(%arg2 : tensor<?xf32>) {
^bb(%in0: f32, %in1: f32, %out: f32) :
%0 = arith.addf %in0, %in1 : f32
linalg.yield %0 : f32
} -> tensor<?xf32>
return %0 : tensor<?xf32>
}
// CHECK-LABEL: @vectorize_dynamic_identity_with_param
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32>
// CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_4]] : vector<4xi1>
// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<4xf32>
// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%vector_size = transform.param.constant 4 : i64 -> !transform.param<i64>
transform.structured.vectorize %0 vector_sizes [%vector_size] : !transform.any_op, !transform.param<i64>
transform.yield
}
}
// -----
func.func @vectorize_dynamic_1d_broadcast(%arg0: tensor<?xf32>,
%arg1: tensor<?xf32>,
%arg2: tensor<?xf32>) -> tensor<?xf32> {
@ -231,6 +306,49 @@ module attributes {transform.with_named_sequence} {
// -----
func.func @vectorize_dynamic_transpose_reduction_with_params(%arg0: tensor<?x?x?xf32>,
%arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = linalg.generic { indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d2, d1)>],
iterator_types = ["reduction", "parallel", "parallel"] }
ins(%arg0 : tensor<?x?x?xf32>)
outs(%arg1 : tensor<?x?xf32>) {
^bb(%in: f32, %out: f32) :
%0 = arith.addf %in, %out : f32
linalg.yield %0 : f32
} -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%vector_size_0 = transform.param.constant 4 : i64 -> !transform.param<i64>
%vector_size_2 = transform.param.constant 16 : i64 -> !transform.param<i64>
transform.structured.vectorize %0 vector_sizes
[%vector_size_0, 8, %vector_size_2] : !transform.any_op, !transform.param<i64>, !transform.param<i64>
transform.yield
}
}
// CHECK-LABEL: @vectorize_dynamic_transpose_reduction_with_params(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_6:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]], %[[VAL_7]] : vector<4x8x16xi1>
// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_0]]{{.*}} {in_bounds = [true, true, true]} : tensor<?x?x?xf32>, vector<4x8x16xf32> } : vector<4x8x16xi1> -> vector<4x8x16xf32>
// CHECK: %[[VAL_13:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_5]] : vector<16x8xi1>
// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_13]] { vector.transfer_read %[[VAL_1]]{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<8x16xf32> } : vector<16x8xi1> -> vector<8x16xf32>
// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_10]] { vector.multi_reduction <add>, %[[VAL_11]], %[[VAL_14]] [0] : vector<4x8x16xf32> to vector<8x16xf32> } : vector<4x8x16xi1> -> vector<8x16xf32>
// CHECK: %[[VAL_17:.*]] = vector.mask %[[VAL_13]] { vector.transfer_write %[[VAL_15]], %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : vector<8x16xf32>, tensor<?x?xf32> } : vector<16x8xi1> -> tensor<?x?xf32>
// -----
func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>,
%arg1: tensor<8x?xf32>,
%arg2: tensor<8x?xf32>) -> tensor<8x?xf32> {

14
mlir/test/python/dialects/transform_structured_ext.py
View File

@ -210,7 +210,17 @@ def testVectorizeMixed(target):
# CHECK: transform.sequence
# CHECK: %[[V0:.*]] = transform.structured.match
# CHECK: transform.structured.vectorize
# CHECK-SAME: vector_sizes [%[[V0]] : !transform.any_op, 4]
# CHECK-SAME: vector_sizes [%[[V0]], 4]
@run
@create_sequence
def testVectorizeEmpty(target):
structured.VectorizeOp(target, [])
# CHECK-LABEL: TEST: testVectorizeEmpty
# CHECK: transform.sequence
# CHECK: transform.structured.vectorize
# CHECK-NOT: vector_sizes
@run
@ -223,7 +233,7 @@ def testVectorizeScalable(target):
# CHECK: transform.sequence
# CHECK-DAG: %[[V0:.*]] = transform.structured.match
# CHECK-DAG: transform.structured.vectorize
# CHECK-SAME: vector_sizes [16, [%[[V0]] : !transform.any_op], [4], [8]]
# CHECK-SAME: vector_sizes [16, [%[[V0]]], [4], [8]]
@run