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[mlir][linalg] Update vectorization tests for tensor.pad and tensor.insert_slice #138267

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[mlir][linalg] Update vectorization tests for tensor.pad and tensor.insert_slice #138267

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277 changes: 0 additions & 277 deletions mlir/test/Dialect/Linalg/vectorization.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -582,132 +582,6 @@ module attributes {transform.with_named_sequence} {

// -----

// CHECK-LABEL: func @test_masked_vectorize_pad
func.func @test_masked_vectorize_pad(
%0 : tensor<?x?xf32>, %h0 : index, %h1 : index)
-> tensor<2x4xf32>
{
// CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c0_0:.*]] = arith.constant 0 : index
// CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
// CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
// CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
// CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
// CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_0]], %[[c0_0]]], %[[c42]]
// CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32>
// CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32>
// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<2x4xf32>
// CHECK: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_1]], %[[c0_1]]]
// CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<2x4xf32>
%cst = arith.constant 42.43 : f32
%c0 = arith.constant 0 : index
%1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] {
^bb0(%hh1: index, %hh2: index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<2x4xf32>
return %1: tensor<2x4xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1
: (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
transform.yield
}
}

// -----

// CHECK: #[[MAP:.+]] = affine_map<()[s0, s1] -> (s0 + s1)>
// CHECK: func @test_masked_vectorize_dynamic_pad
func.func @test_masked_vectorize_dynamic_pad(
%0 : tensor<?x?xf32>, %h0 : index, %h1 : index)
-> tensor<?x?xf32>
{
// CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[res_d0:.+]] = affine.apply #[[MAP]]()
// CHECK-DAG: %[[res_d1:.+]] = affine.apply #[[MAP]]()
// CHECK: %[[c0_2:.*]] = arith.constant 0 : index
// CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
// CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
// CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
// CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
// CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_2]], %[[c0_2]]], %[[c42]]
// CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32>
// CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32>
// CHECK-DAG: %[[empty:.*]] = tensor.empty(%[[res_d0]], %[[res_d1]]) : tensor<?x?xf32>
// CHECK-DAG: %[[c0_3:.*]] = arith.constant 0 : index
// CHECK: %[[mask_2:.*]] = vector.create_mask %[[res_d0]], %[[res_d1]] : vector<2x4xi1>
// CHECK: %[[masked_write:.*]] = vector.mask %[[mask_2]] {
// CHECK-SAME: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_3]], %[[c0_3]]]
// CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<?x?xf32>
// CHECK: return %[[masked_write]] : tensor<?x?xf32>
%cst = arith.constant 42.43 : f32
%c0 = arith.constant 0 : index
%1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] {
^bb0(%hh1: index, %hh2: index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<?x?xf32>
return %1: 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{["tensor.pad"]} in %arg1
: (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
transform.yield
}
}

// -----
// This case is supported because low padding `%l0` is applied on
// a unit dimension which is supported, non unit result dimension low
// padding is currently unsupported.
// CHECK-LABEL: func @test_masked_vectorize_non_zero_low_pad_unit_res_dim
func.func @test_masked_vectorize_non_zero_low_pad_unit_res_dim(
%0 : tensor<?x?xf32>, %h0 : index, %h1 : index, %l0 : index)
-> tensor<1x4xf32>
{
// CHECK-DAG: %[[C42:.*]] = arith.constant 4.243000e+01 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[C0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[D0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
// CHECK-DAG: %[[D1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
// CHECK: %[[MASK:.*]] = vector.create_mask %[[D0]], %[[D1]] : vector<1x4xi1>
// CHECK: %[[MASKED_READ:.*]] = vector.mask %[[MASK]] {
// CHECK-SAME: vector.transfer_read %{{.*}}[%[[C0_1]], %[[C0_1]]], %[[C42]]
// CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32>
// CHECK-SAME: } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK-DAG: %[[EMPTY:.*]] = tensor.empty() : tensor<1x4xf32>
// CHECK-DAG: %[[C0_2:.*]] = arith.constant 0 : index
// CHECK: %[[MASKED_WRITE:.*]] = vector.transfer_write %[[MASKED_READ]], %[[EMPTY]][%[[C0_2]], %[[C0_2]]]
// CHECK-SAME: {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
// CHECK: return %[[MASKED_WRITE]] : tensor<1x4xf32>
%cst = arith.constant 42.43 : f32
%c0 = arith.constant 0 : index
%1 = tensor.pad %0 low[%l0, %c0] high[%h0, %h1] {
^bb0(%hh1: index, %hh2: index):
tensor.yield %cst : f32
} : tensor<?x?xf32> to tensor<1x4xf32>
return %1: tensor<1x4xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1
: (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [1, 4] : !transform.any_op
transform.yield
}
}

// -----

// Input identical as the test in vectorization-with-patterns.mlir. Output is
// different - vector sizes are inferred (rather than user-specified) and hence
// masking was used.
Expand Down Expand Up @@ -1150,154 +1024,3 @@ func.func @test_vectorize_unpack_no_vector_sizes_permute(%source: tensor<4x7x4xf
transform.yield
}
}

// -----

///----------------------------------------------------------------------------------------
/// tensor.insert_slice
///----------------------------------------------------------------------------------------

func.func private @insert_slice_static_sizes(%source: tensor<?x3x?x1xi32>) -> tensor<5x3xi32> {
%c2 = arith.constant 2 : index
%init = tensor.empty() : tensor<5x3xi32>

%source_slice = tensor.extract_slice %source[0, %c2, 0, 0] [1, 1, 5, 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<5x1xi32>
%res = tensor.insert_slice %source_slice into %init[0, %c2] [5, 1] [1, 1] : tensor<5x1xi32> into tensor<5x3xi32>

return %res : tensor<5x3xi32>
}

// CHECK-LABEL: func.func private @insert_slice_static_sizes(
// CHECK-SAME: %[[SEC:.*]]: tensor<?x3x?x1xi32>) -> tensor<5x3xi32> {
// CHECK: %[[C_2:.*]] = arith.constant 2 : index
// CHECK: %[[INIT:.*]] = tensor.empty() : tensor<5x3xi32>
// CHECK: %[[SRC_SLICE:.*]] = tensor.extract_slice %[[SEC]][0, %[[C_2]], 0, 0] [1, 1, 5, 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<5x1xi32>
// CHECK-DAG: %[[PAD:.*]] = arith.constant 0 : i32
// CHECK-DAG: %[[C_5:.*]] = arith.constant 5 : index
// CHECK-DAG: %[[C_1:.*]] = arith.constant 1 : index
// CHECK: %[[MASK:.*]] = vector.create_mask %[[C_5]], %[[C_1]] : vector<8x1xi1>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC_SLICE]][%[[C0]], %[[C0]]], %[[PAD]] : tensor<5x1xi32>, vector<8x1xi32> } : vector<8x1xi1> -> vector<8x1xi32>
// CHECK: %[[C_0:.*]] = arith.constant 0 : index
// CHECK: %[[RES:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[INIT]][%[[C_0]], %[[C_2]]] : vector<8x1xi32>, tensor<5x3xi32> } : vector<8x1xi1> -> tensor<5x3xi32>
// CHECK: return %[[RES]] : tensor<5x3xi32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [8, 1] : !transform.any_op
transform.yield
}
}

// -----

// One of the _source_ dimensions is dynamic (but _destination_ dimensions are static).

func.func private @insert_slice_dynamic_src_dim(%source: tensor<?x3x?x1xi32>, %size: index) -> tensor<5x3xi32> {
%c2 = arith.constant 2 : index
%init = tensor.empty() : tensor<5x3xi32>

%source_slice = tensor.extract_slice %source[0, %c2, 0, 0] [1, 1, %size, 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<?x1xi32>
%res = tensor.insert_slice %source_slice into %init[0, %c2] [%size, 1] [1, 1] : tensor<?x1xi32> into tensor<5x3xi32>

return %res : tensor<5x3xi32>
}

// CHECK-LABEL: func.func private @insert_slice_dynamic_src_dim(
// CHECK-SAME: %[[SRC:.*]]: tensor<?x3x?x1xi32>,
// CHECK-SAME: %[[SIZE:.*]]: index) -> tensor<5x3xi32> {
// CHECK: %[[C_2:.*]] = arith.constant 2 : index
// CHECK: %[[INIT:.*]] = tensor.empty() : tensor<5x3xi32>
// CHECK: %[[SRC_SLICE:.*]] = tensor.extract_slice %[[SRC]][0, %[[C_2]], 0, 0] [1, 1, %[[SIZE]], 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<?x1xi32>
// CHECK-DAG: %[[PAD:.*]] = arith.constant 0 : i32
// CHECK-DAG: %[[C_1:.*]] = arith.constant 1 : index
// CHECK: %[[MASK:.*]] = vector.create_mask %[[SIZE]], %[[C_1]] : vector<8x1xi1>
// CHECK: %[[C_0:.*]] = arith.constant 0 : index
// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC_SLICE]][%[[C_0]], %[[C_0]]], %[[PAD]] : tensor<?x1xi32>, vector<8x1xi32> } : vector<8x1xi1> -> vector<8x1xi32>
// CHECK: %[[C_0_1:.*]] = arith.constant 0 : index
// CHECK: %[[RES:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[INIT]][%[[C_0_1]], %[[C_2]]] : vector<8x1xi32>, tensor<5x3xi32> } : vector<8x1xi1> -> tensor<5x3xi32>
// CHECK: return %[[RES]] : tensor<5x3xi32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [8, 1] : !transform.any_op
transform.yield
}
}

// -----

// One of the _destination_ dimensions is dynamic (but _source_ dimensions are static).

func.func private @insert_slice_dynamic_dest_dim(%source: tensor<?x3x?x1xi32>, %size: index) -> tensor<?x3xi32> {
%c2 = arith.constant 2 : index
%init = tensor.empty(%size) : tensor<?x3xi32>

%source_slice = tensor.extract_slice %source[0, %c2, 0, 0] [1, 1, 5, 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<5x1xi32>
%res = tensor.insert_slice %source_slice into %init[0, %c2] [5, 1] [1, 1] : tensor<5x1xi32> into tensor<?x3xi32>

return %res : tensor<?x3xi32>
}

// CHECK-LABEL: func.func private @insert_slice_dynamic_dest_dim(
// CHECK-SAME: %[[SRC:.*]]: tensor<?x3x?x1xi32>,
// CHECK-SAME: %[[SIZE:.*]]: index) -> tensor<?x3xi32> {
// CHECK: %[[C_2:.*]] = arith.constant 2 : index
// CHECK: %[[INIT:.*]] = tensor.empty(%[[SIZE]]) : tensor<?x3xi32>
// CHECK: %[[SRC_SLICE:.*]] = tensor.extract_slice %[[SRC]][0, %[[C_2]], 0, 0] [1, 1, 5, 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<5x1xi32>
// CHECK: %[[PAD:.*]] = arith.constant 0 : i32
// CHECK: %[[C_5:.*]] = arith.constant 5 : index
// CHECK: %[[C_1:.*]] = arith.constant 1 : index
// CHECK: %[[MASK:.*]] = vector.create_mask %[[C_5]], %[[C_1]] : vector<8x1xi1>
// CHECK: %[[C_0:.*]] = arith.constant 0 : index
// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC_SLICE]][%[[C_0]], %[[C_0]]], %[[PAD]] : tensor<5x1xi32>, vector<8x1xi32> } : vector<8x1xi1> -> vector<8x1xi32>
// CHECK: %[[C_0_1:.*]] = arith.constant 0 : index
// CHECK: %[[WRITE:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[INIT]][%[[C_0_1]], %[[C_2]]] : vector<8x1xi32>, tensor<?x3xi32> } : vector<8x1xi1> -> tensor<?x3xi32>
// CHECK: return %[[WRITE]] : tensor<?x3xi32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [8, 1] : !transform.any_op
transform.yield
}
}

// -----

// At least one _source_ and one _destination_ dimensions are dynamic.

func.func private @insert_slice_dynamic_source_and_dest_dim(%source: tensor<?x3x?x1xi32>, %size: index) -> tensor<?x3xi32> {
%c2 = arith.constant 2 : index
%init = tensor.empty(%size) : tensor<?x3xi32>

%source_slice = tensor.extract_slice %source[0, %c2, 0, 0] [1, 1, %size, 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<?x1xi32>
%res = tensor.insert_slice %source_slice into %init[0, %c2] [%size, 1] [1, 1] : tensor<?x1xi32> into tensor<?x3xi32>

return %res : tensor<?x3xi32>
}

// CHECK-LABEL: func.func private @insert_slice_dynamic_source_and_dest_dim(
// CHECK-SAME: %[[SRC:.*]]: tensor<?x3x?x1xi32>,
// CHECK-SAME: %[[SIZE:.*]]: index) -> tensor<?x3xi32> {
// CHECK: %[[C_2:.*]] = arith.constant 2 : index
// CHECK: %[[INIT:.*]] = tensor.empty(%[[SIZE]]) : tensor<?x3xi32>
// CHECK: %[[SRC_SIZE:.*]] = tensor.extract_slice %[[SRC]][0, %[[C_2]], 0, 0] [1, 1, %[[SIZE]], 1] [1, 1, 1, 1] : tensor<?x3x?x1xi32> to tensor<?x1xi32>
// CHECK: %[[PAD:.*]] = arith.constant 0 : i32
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[MASK:.*]] = vector.create_mask %[[SIZE]], %[[C1]] : vector<8x1xi1>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC_SIZE]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] : tensor<?x1xi32>, vector<8x1xi32> } : vector<8x1xi1> -> vector<8x1xi32>
// CHECK: %[[C_0_1:.*]] = arith.constant 0 : index
// CHECK: %[[WRITE:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[INIT]]{{\[}}%[[C_0_1]], %[[C_2]]] : vector<8x1xi32>, tensor<?x3xi32> } : vector<8x1xi1> -> tensor<?x3xi32>
// CHECK: return %[[WRITE]] : tensor<?x3xi32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [8, 1] : !transform.any_op
transform.yield
}
}
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