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[mlir][linalg] Enable Vectorization of 0-D tensor.extract #119079

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Dec 11, 2024
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[mlir][linalg] Enable Vectorization of 0-D tensor.extract #119079

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5 changes: 0 additions & 5 deletions mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1115,11 +1115,6 @@ vectorizeTensorExtract(RewriterBase &rewriter, VectorizationState &state,
// b. contiguous loads.
// Both cases use vector.transfer_read.

assert(llvm::count_if(resultType.getShape(),
[](uint64_t dim) { return dim != 1; }) &&
"Contiguous loads and scalar loads + broadcast only support 1-D "
"vectors ATM!");

// Collect indices for `vector.transfer_read`. At this point, the indices will
// either be scalars or would have been broadcast to vectors matching the
// result type. For indices that are vectors, there are two options:
Expand Down
48 changes: 45 additions & 3 deletions mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
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Expand Up @@ -122,6 +122,48 @@ module attributes {transform.with_named_sequence} {

// -----

#map = affine_map<() -> ()>

// CHECK-LABEL: func.func @generic_0d(
// CHECK-SAME: %[[ARG_0:.*]]: tensor<f32>, %[[ARG_1:.*]]: tensor<f32>, %[[ARG_2:.*]]: tensor<f32>)
func.func @generic_0d(%arg0: tensor<f32>, %arg1: tensor<f32>,
%arg2: tensor<f32>) -> tensor<f32> {
// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[READ_0:.*]] = vector.transfer_read %[[ARG_0]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: %[[ARG_0_AS_SCALAR:.*]] = vector.extract %[[READ_0]][] : f32 from vector<f32>
// CHECK: %[[READ_1:.*]] = vector.transfer_read %[[ARG_1]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: %[[ARG_1_AS_SCALAR:.*]] = vector.extract %[[READ_1]][] : f32 from vector<f32>
// CHECK: %[[READ_2:.*]] = vector.transfer_read %[[ARG_2]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: %[[ARG_2_AS_SCALAR:.*]] = vector.extract %[[READ_2]][] : f32 from vector<f32>
// CHECK: %[[MULF:.*]] = arith.mulf %[[ARG_0_AS_SCALAR]], %[[ARG_1_AS_SCALAR]] : f32
// CHECK: %[[ADDF:.*]] = arith.addf %[[ARG_2_AS_SCALAR]], %[[MULF]] : f32
// CHECK: %[[ADDF_BCAST:.*]] = vector.broadcast %[[ADDF]] : f32 to vector<f32>
// CHECK: vector.transfer_write %[[ADDF_BCAST]], %[[ARG_2]][] : vector<f32>, tensor<f32>
%res = linalg.generic {
indexing_maps = [#map, #map, #map],
iterator_types = []
} ins(%arg0, %arg1 : tensor<f32>, tensor<f32>)
outs(%arg2 : tensor<f32>) {
^bb(%a: f32, %b: f32, %c: f32) :
%d = arith.mulf %a, %b: f32
%e = arith.addf %c, %d: f32
linalg.yield %e : f32
} -> tensor<f32>

return %res : tensor<f32>
}

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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}

// -----

#matmul_transpose_out_trait = {
indexing_maps = [
affine_map<(m, n, k) -> (m, k)>,
Expand Down Expand Up @@ -372,7 +414,7 @@ module attributes {transform.with_named_sequence} {
// -----

// CHECK-LABEL: func @test_vectorize_fill
func.func @test_vectorize_fill_scalar(%A : memref<f32>, %arg0 : f32) {
func.func @test_vectorize_fill_0d(%A : memref<f32>, %arg0 : f32) {
// CHECK-SAME: (%[[M:.*]]: memref<f32>, %[[val:.*]]: f32)
// CHECK: %[[VEC:.*]] = vector.broadcast %[[val]] : f32 to vector<f32>
// CHECK: vector.transfer_write %[[VEC]], %[[M]][] : vector<f32>, memref<f32>
Expand Down Expand Up @@ -410,8 +452,8 @@ module attributes {transform.with_named_sequence} {

// -----

// CHECK-LABEL: func @test_vectorize_copy_scalar
func.func @test_vectorize_copy_scalar(%A : memref<f32>, %B : memref<f32>) {
// CHECK-LABEL: func @test_vectorize_copy_0d
func.func @test_vectorize_copy_0d(%A : memref<f32>, %B : memref<f32>) {
// CHECK-SAME: (%[[A:.*]]: memref<f32>, %[[B:.*]]: memref<f32>)
// CHECK: %[[V:.*]] = vector.transfer_read %[[A]][]{{.*}} : memref<f32>, vector<f32>
// CHECK: %[[val:.*]] = vector.extract %[[V]][] : f32 from vector<f32>
Expand Down
58 changes: 48 additions & 10 deletions mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
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Expand Up @@ -39,29 +39,67 @@ module attributes {transform.with_named_sequence} {
// -----

#map = affine_map<() -> ()>
func.func @negative_no_loops(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
%1 = linalg.generic {
func.func @extract_scalar_from_0d_into_0d(%src: tensor<f32>, %init: tensor<f32>) -> tensor<f32> {
%res = linalg.generic {
indexing_maps = [#map],
iterator_types = []
} outs(%arg1 : tensor<f32>) {
^bb0(%arg4: f32):
%2 = tensor.extract %arg0[] : tensor<f32>
linalg.yield %2 : f32
} outs(%init : tensor<f32>) {
^bb0(%in: f32):
%1 = tensor.extract %src[] : tensor<f32>
linalg.yield %1 : f32
} -> tensor<f32>
return %1 : tensor<f32>

return %res : tensor<f32>
}
// CHECK-LABEL: func.func @negative_no_loops
// CHECK: tensor.extract

// CHECK-LABEL: func.func @extract_scalar_from_0d_into_0d(
// CHECK-SAME: %[[SRC:.*]]: tensor<f32>,
// CHECK-SAME: %[[INIT:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: vector.transfer_write %[[READ]], %[[INIT]][] : vector<f32>, tensor<f32>

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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}

// -----

#map = affine_map<(n) -> (n)>
func.func @extract_scalar_from_0d_into_1d(%src: tensor<f32>, %init: tensor<1xf32>) -> tensor<1xf32> {
%res = linalg.generic {
indexing_maps = [#map],
iterator_types = ["parallel"]
} outs(%init : tensor<1xf32>) {
^bb0(%in: f32):
%1 = tensor.extract %src[] : tensor<f32>
linalg.yield %1 : f32
} -> tensor<1xf32>

return %res : tensor<1xf32>
}
// CHECK-LABEL: func.func @extract_scalar_from_0d_into_1d(
// CHECK-SAME: %[[SRC:.*]]: tensor<f32>,
// CHECK-SAME: %[[INIT:.*]]: tensor<1xf32>) -> tensor<1xf32> {
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<1xf32>
// CHECK: vector.transfer_write %[[READ_BCAST]], %[[INIT]][%[[C0]]] {in_bounds = [true]} : vector<1xf32>, tensor<1xf32>

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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}

// -----

Expand Down
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