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[mlir][Vector] Enable masking for ops with index semantics
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Masking was already supported for linalg.index and n-D extract but
disabled while waiting for some n-D extract vectorization patches to
land. This patch is just enabling masking for them and adding a couple
of tests.

Reviewed By: ThomasRaoux

Differential Revision: https://reviews.llvm.org/D147359
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@dcaballe
dcaballe committed Apr 3, 2023
1 parent 04798db commit 5217782
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Showing 2 changed files with 181 additions and 4 deletions.
4 changes: 0 additions & 4 deletions mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
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Expand Up @@ -1285,10 +1285,6 @@ static LogicalResult vectorizeDynamicLinalgOpPrecondition(linalg::LinalgOp op) {
if (!isa<linalg::GenericOp, linalg::FillOp>(op))
return failure();

// TODO: Index vectorization assumes static shape.
if (op.hasIndexSemantics())
return failure();

LDBG("Dynamically-shaped op meets vectorization pre-conditions\n");
return success();
}
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181 changes: 181 additions & 0 deletions mlir/test/Dialect/Linalg/vectorization.mlir
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Expand Up @@ -1834,6 +1834,94 @@ transform.sequence failures(propagate) {

// -----

func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous(%6: tensor<80x16xf32>, %arg0: index, %extracted_slice : tensor<1x3xf32>) -> tensor<1x3xf32> {
%c79 = arith.constant 79 : index
%1 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]
} outs(%extracted_slice : tensor<1x3xf32>) {
^bb0(%out: f32):
%2 = linalg.index 1 : index
%3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0)
%extracted = tensor.extract %6[%c79, %3] : tensor<80x16xf32>
linalg.yield %extracted : f32
} -> tensor<1x3xf32>
return %1 : tensor<1x3xf32>
}

// CHECK-LABEL: func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_5]] : vector<1x4xi1>
// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<1x3xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_11:.*]] = vector.broadcast {{.*}} : index to vector<4xindex>
// CHECK: %[[VAL_12:.*]] = arith.addi {{.*}} : vector<4xindex>
// CHECK: %[[VAL_20:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_22:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32>

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 [1, 4] { vectorize_nd_extract }
}

// -----

func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous(%6: tensor<?x?xf32>, %arg0: index, %extracted_slice : tensor<?x?xf32>) -> tensor<?x?xf32> {
%c79 = arith.constant 79 : index
%1 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]
} outs(%extracted_slice : tensor<?x?xf32>) {
^bb0(%out: f32):
%2 = linalg.index 1 : index
%3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0)
%extracted = tensor.extract %6[%c79, %3] : tensor<?x?xf32>
linalg.yield %extracted : f32
} -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}

// CHECK-LABEL: func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: index,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 79 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32>
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<1x4xi1>
// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_2]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_12:.*]] = arith.constant dense<[0, 1, 2, 3]> : vector<4xindex>
// CHECK: %[[VAL_13:.*]] = vector.broadcast %[[VAL_1]] : index to vector<4xindex>
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : vector<4xindex>
// CHECK-DAG: %[[VAL_15:.*]] = arith.constant dense<true> : vector<1x4xi1>
// CHECK-DAG: %[[VAL_16:.*]] = arith.constant dense<0.000000e+00> : vector<1x4xf32>
// CHECK-DAG: %[[VAL_17:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_18:.*]] = arith.constant dense<79> : vector<1x4xindex>
// CHECK-DAG: %[[VAL_19:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_20:.*]] = tensor.dim %[[VAL_0]], %[[VAL_19]] : tensor<?x?xf32>
// CHECK: %[[VAL_21:.*]] = vector.broadcast %[[VAL_20]] : index to vector<1x4xindex>
// CHECK: %[[VAL_22:.*]] = arith.muli %[[VAL_18]], %[[VAL_21]] : vector<1x4xindex>
// CHECK: %[[VAL_23:.*]] = vector.broadcast %[[VAL_14]] : vector<4xindex> to vector<1x4xindex>
// CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : vector<1x4xindex>
// CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_10]] { vector.gather %[[VAL_0]]{{\[}}%[[VAL_17]], %[[VAL_17]]] {{\[}}%[[VAL_24]]], %[[VAL_15]], %[[VAL_16]] : tensor<?x?xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_26:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_27:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_25]], %[[VAL_2]]{{\[}}%[[VAL_26]], %[[VAL_26]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<?x?xf32> } : vector<1x4xi1> -> tensor<?x?xf32>
// CHECK: return %[[VAL_27]] : tensor<?x?xf32>
// CHECK: }

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 [1, 4] { vectorize_nd_extract }
}

// -----

// The vectorizer converts `affine.apply` so that the subsequent Ops can be vectorised based on the converted ops. Gather load.
func.func @vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<80x16xf32>, %arg0: index, %extracted_slice : tensor<1x4xf32>) -> tensor<1x4xf32> {
%c16 = arith.constant 16 : index
Expand Down Expand Up @@ -1878,6 +1966,99 @@ transform.sequence failures(propagate) {

// -----

func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<80x16xf32>, %arg0: index, %extracted_slice : tensor<1x3xf32>) -> tensor<1x3xf32> {
%c16 = arith.constant 16 : index
%1 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]
} outs(%extracted_slice : tensor<1x3xf32>) {
^bb0(%out: f32):
%2 = linalg.index 1 : index
%3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0)
%extracted = tensor.extract %6[%3, %c16] : tensor<80x16xf32>
linalg.yield %extracted : f32
} -> tensor<1x3xf32>
return %1 : tensor<1x3xf32>
}

// CHECK-LABEL: func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_5]] : vector<1x4xi1>
// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<1x3xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_11:.*]] = vector.broadcast {{.*}} : index to vector<4xindex>
// CHECK: %[[VAL_12:.*]] = arith.addi {{.*}} : vector<4xindex>
// CHECK: %[[VAL_16:.*]] = vector.broadcast {{.*}} : vector<4xindex> to vector<1x4xindex>
// CHECK: %[[VAL_18:.*]] = tensor.dim {{.*}} : tensor<80x16xf32>
// CHECK: %[[VAL_19:.*]] = vector.broadcast {{.*}} : index to vector<1x4xindex>
// CHECK: %[[VAL_20:.*]] = arith.muli {{.*}} : vector<1x4xindex>
// CHECK: %[[VAL_22:.*]] = arith.addi {{.*}} : vector<1x4xindex>
// CHECK: %[[VAL_23:.*]] = vector.mask %[[VAL_8]] { vector.gather {{.*}} : tensor<80x16xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32>

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 [1, 4] { vectorize_nd_extract }
}

// -----

func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<?x?xf32>, %arg0: index, %extracted_slice : tensor<?x?xf32>) -> tensor<?x?xf32> {
%c16 = arith.constant 16 : index
%1 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]
} outs(%extracted_slice : tensor<?x?xf32>) {
^bb0(%out: f32):
%2 = linalg.index 1 : index
%3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0)
%extracted = tensor.extract %6[%3, %c16] : tensor<?x?xf32>
linalg.yield %extracted : f32
} -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}

// CHECK-LABEL: func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: index,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32>
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32>
// CHECK: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<1x4xi1>
// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_2]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_12:.*]] = arith.constant dense<[0, 1, 2, 3]> : vector<4xindex>
// CHECK: %[[VAL_13:.*]] = vector.broadcast %[[VAL_1]] : index to vector<4xindex>
// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : vector<4xindex>
// CHECK: %[[VAL_15:.*]] = arith.constant dense<true> : vector<1x4xi1>
// CHECK: %[[VAL_16:.*]] = arith.constant dense<0.000000e+00> : vector<1x4xf32>
// CHECK: %[[VAL_17:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_18:.*]] = vector.broadcast %[[VAL_14]] : vector<4xindex> to vector<1x4xindex>
// CHECK: %[[VAL_19:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_20:.*]] = tensor.dim %[[VAL_0]], %[[VAL_19]] : tensor<?x?xf32>
// CHECK: %[[VAL_21:.*]] = vector.broadcast %[[VAL_20]] : index to vector<1x4xindex>
// CHECK: %[[VAL_22:.*]] = arith.muli %[[VAL_18]], %[[VAL_21]] : vector<1x4xindex>
// CHECK: %[[VAL_23:.*]] = arith.constant dense<16> : vector<1x4xindex>
// CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : vector<1x4xindex>
// CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_10]] { vector.gather %[[VAL_0]]{{\[}}%[[VAL_17]], %[[VAL_17]]] {{\[}}%[[VAL_24]]], %[[VAL_15]], %[[VAL_16]] : tensor<?x?xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_26:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_27:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_25]], %[[VAL_2]]{{\[}}%[[VAL_26]], %[[VAL_26]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<?x?xf32> } : vector<1x4xi1> -> tensor<?x?xf32>
// CHECK: return %[[VAL_27]] : tensor<?x?xf32>
// CHECK: }

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 [1, 4] { vectorize_nd_extract }
}

// -----

// Make sure that non-linear arithmetic operations (e.g. arith.maxsi) are allowed when calculating indices for load operations. Gather load.
func.func @vectorize_nd_tensor_extract_with_maxsi_gather(%arg0: tensor<80x16xf32>, %extracted_slice : tensor<1x4xf32>) -> tensor<1x4xf32> {
%c79 = arith.constant 79 : index
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