[Mlir-commits] [mlir] 1427277 - [mlir][Vector] Enable masking for static shapes
Diego Caballero
llvmlistbot at llvm.org
Tue Feb 14 22:15:30 PST 2023
Author: Diego Caballero
Date: 2023-02-15T06:10:22Z
New Revision: 1427277eed800335ea211cdb94f10b4976a54231
URL: https://github.com/llvm/llvm-project/commit/1427277eed800335ea211cdb94f10b4976a54231
DIFF: https://github.com/llvm/llvm-project/commit/1427277eed800335ea211cdb94f10b4976a54231.diff
LOG: [mlir][Vector] Enable masking for static shapes
Support for masking static shapes was already implemented in the past
but not enabled so this patch is just removing a pre-condition check and
adding some tests with static shapes.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D143937
Added:
Modified:
mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
mlir/test/Dialect/Linalg/vectorization.mlir
Removed:
################################################################################
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index fc36477151d52..75d4595e4f7a3 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1003,11 +1003,6 @@ mlir::linalg::vectorizeLinalgOpPrecondition(LinalgOp linalgOp,
"static sizes");
}
- // TODO: Masking is only supported for dynamic shapes so input vector sizes
- // must be empty if the op is not dynamic.
- if (!linalgOp.hasDynamicShape() && !inputVectorSizes.empty())
- return failure();
-
if (linalgOp.hasDynamicShape() &&
failed(vectorizeDynamicLinalgOpPrecondition(linalgOp))) {
LDBG("Dynamically-shaped op failed vectorization pre-conditions\n");
@@ -1092,8 +1087,10 @@ LogicalResult mlir::linalg::vectorize(RewriterBase &rewriter, LinalgOp linalgOp,
LLVM_DEBUG(llvm::dbgs() << "\n");
if (failed(vectorizeLinalgOpPrecondition(linalgOp, inputVectorSizes,
- vectorizeNDExtract)))
+ vectorizeNDExtract))) {
+ LDBG("Vectorization pre-conditions failed\n");
return failure();
+ }
// Initialize vectorization state.
VectorizationState state(rewriter);
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index fb35746c1019c..a43fd7a9fc5c5 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -2075,3 +2075,111 @@ transform.sequence failures(propagate) {
%2 = transform.structured.vectorize %1
}
+// -----
+
+func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>,
+ %arg1: tensor<8x?xf32>,
+ %arg2: tensor<8x?xf32>) -> tensor<8x?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<8x?xf32>, tensor<8x?xf32>)
+ outs(%arg2 : tensor<8x?xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x?xf32>
+ return %0 : tensor<8x?xf32>
+}
+
+// CHECK-LABEL: func.func @vectorize_partial_dynamic_identity(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x?xf32>, %[[VAL_1:.*]]: tensor<8x?xf32>, %[[VAL_2:.*]]: tensor<8x?xf32>) -> tensor<8x?xf32> {
+// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<8x?xf32>
+// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_7:.*]] = arith.constant 8 : index
+// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_4]] : vector<8x32xi1>
+// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_6]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_10:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_1]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_10]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_12:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_2]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_12]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] : vector<8x32xf32>
+// CHECK: %[[VAL_15:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_16:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write %[[VAL_14]], %[[VAL_2]][%[[VAL_15]], %[[VAL_15]]] {in_bounds = [true, true]} : vector<8x32xf32>, tensor<8x?xf32> } : vector<8x32xi1> -> tensor<8x?xf32>
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+ transform.structured.masked_vectorize %0 vector_sizes [8, 32]
+}
+
+// -----
+
+func.func @do_not_generate_masks(%arg0: tensor<8x32xf32>,
+ %arg1: tensor<8x32xf32>,
+ %arg2: tensor<8x32xf32>) -> tensor<8x32xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<8x32xf32>, tensor<8x32xf32>)
+ outs(%arg2 : tensor<8x32xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x32xf32>
+ return %0 : tensor<8x32xf32>
+}
+
+// CHECK-LABEL: func.func @do_not_generate_masks
+// CHECK-NOT: vector.mask
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+ transform.structured.masked_vectorize %0 vector_sizes [8, 32]
+}
+
+// -----
+
+func.func @vectorize_static_shape_with_mask(%arg0: tensor<8x30xf32>,
+ %arg1: tensor<8x30xf32>,
+ %arg2: tensor<8x30xf32>) -> tensor<8x30xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<8x30xf32>, tensor<8x30xf32>)
+ outs(%arg2 : tensor<8x30xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x30xf32>
+ return %0 : tensor<8x30xf32>
+}
+
+// CHECK-LABEL: func.func @vectorize_static_shape_with_mask(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x30xf32>, %[[VAL_1:.*]]: tensor<8x30xf32>, %[[VAL_2:.*]]: tensor<8x30xf32>) -> tensor<8x30xf32> {
+// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_4:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_5:.*]] = arith.constant 8 : index
+// CHECK: %[[VAL_6:.*]] = arith.constant 30 : index
+// CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_6]] : vector<8x32xi1>
+// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_0]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_4]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_1]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_11:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_2]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_11]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<8x32xf32>
+// CHECK: %[[VAL_14:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %[[VAL_13]], %[[VAL_2]][%[[VAL_14]], %[[VAL_14]]] {in_bounds = [true, true]} : vector<8x32xf32>, tensor<8x30xf32> } : vector<8x32xi1> -> tensor<8x30xf32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+ transform.structured.masked_vectorize %0 vector_sizes [8, 32]
+}
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