[Mlir-commits] [mlir] [mlir][linalg] Support `ParamType` in `vector_sizes` option of `VectorizeOp` transform (PR #87557)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Apr 4 12:52:12 PDT 2024
https://github.com/srcarroll updated https://github.com/llvm/llvm-project/pull/87557
>From 223f5e0e9845e4b7b460b1d550c06edeb7eee57a Mon Sep 17 00:00:00 2001
From: Sam <srcarroll314 at gmail.com>
Date: Wed, 3 Apr 2024 15:45:19 -0500
Subject: [PATCH 1/2] Support `ParamType` in `vector_sizes` option of
`VectorizeOp` transform
---
.../Linalg/TransformOps/LinalgTransformOps.td | 7 +-
.../TransformOps/LinalgTransformOps.cpp | 6 ++
mlir/test/Dialect/Linalg/vectorization.mlir | 80 +++++++++++++++++++
3 files changed, 89 insertions(+), 4 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
index c260fe3f7a46a5..7220e6e077e59c 100644
--- a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
@@ -2138,12 +2138,11 @@ 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 = [{
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index 88819cd964354b..9c284ca309a455 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -3136,6 +3136,12 @@ 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>());
+ assert(params.size() == 1 && "expected a single param");
+ vectorSizes.push_back(
+ cast<IntegerAttr>(params.front()).getValue().getSExtValue());
+ continue;
}
auto szPayloads = state.getPayloadOps(sz.get<Value>());
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index 2d01d57304013c..64e5935a90a4c4 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -36,6 +36,43 @@ module attributes {transform.with_named_sequence} {
// -----
+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.param<i64>] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
func.func @vectorize_dynamic_1d_broadcast(%arg0: tensor<?xf32>,
%arg1: tensor<?xf32>,
%arg2: tensor<?xf32>) -> tensor<?xf32> {
@@ -231,6 +268,49 @@ 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 : !transform.param<i64>, 8, %vector_size_2: !transform.param<i64>] : !transform.any_op
+ 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> {
>From 3b8640e7f2ad91d7907d17b1590f13a95ed6e6a5 Mon Sep 17 00:00:00 2001
From: Sam <srcarroll314 at gmail.com>
Date: Thu, 4 Apr 2024 12:10:02 -0500
Subject: [PATCH 2/2] address review comments
---
.../Linalg/TransformOps/LinalgTransformOps.td | 3 +-
.../TransformOps/LinalgTransformOps.cpp | 3 +-
mlir/test/Dialect/Linalg/vectorization.mlir | 38 +++++++++++++++++++
3 files changed, 42 insertions(+), 2 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
index 7220e6e077e59c..5230a7716398a3 100644
--- a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
@@ -2139,7 +2139,8 @@ def VectorizeOp : Op<Transform_Dialect, "structured.vectorize",
let arguments = (ins TransformHandleTypeInterface:$target,
Variadic<TransformAnyParamTypeOrAnyHandle>:$vector_sizes,
- DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$static_vector_sizes,
+ DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:
+ $static_vector_sizes,
OptionalAttr<UnitAttr>:$vectorize_nd_extract,
DefaultValuedOptionalAttr<DenseBoolArrayAttr, "{}">:
$scalable_sizes);
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index 9c284ca309a455..bfcea723680726 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -3138,7 +3138,8 @@ DiagnosedSilenceableFailure transform::VectorizeOp::apply(
continue;
} else if (sz.is<Value>() && isa<ParamType>(sz.get<Value>().getType())) {
ArrayRef<Attribute> params = state.getParams(sz.get<Value>());
- assert(params.size() == 1 && "expected a single param");
+ if (params.size() != 1)
+ return emitSilenceableFailure(getLoc()) << "expected a single param";
vectorSizes.push_back(
cast<IntegerAttr>(params.front()).getValue().getSExtValue());
continue;
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index 64e5935a90a4c4..807fa681036435 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -36,6 +36,44 @@ 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> {
More information about the Mlir-commits
mailing list