[mlir][linalg] Move vectorization tests for Tensor Ops (nfc) #140877
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@llvm/pr-subscribers-mlir-linalg @llvm/pr-subscribers-mlir Author: Andrzej Warzyński (banach-space) ChangesThis patch moves vectorization tests for This change is also a preparatory step for upcoming work: I’ll soon be
Patch is 98.83 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/140877.diff 6 Files Affected:
diff --git a/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
index 9f2ee47b45b3e..b282c57e3e4cb 100644
--- a/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
@@ -889,207 +889,6 @@ module attributes {transform.with_named_sequence} {
// -----
-// CHECK-LABEL: func @pad_static(
-// CHECK-SAME: %[[ARG0:.*]]: tensor<2x?x2xf32>, %[[PAD:.*]]: f32
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[INIT:.*]] = tensor.empty() : tensor<2x3x4xf32>
-// CHECK-DAG: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x3x4xf32>
-// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]]{{.*}} : vector<2x3x4xf32>, tensor<2x3x4xf32>
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, false, true]} : tensor<2x?x2xf32>, vector<2x3x2xf32>
-// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x3x2xf32>, tensor<2x3x4xf32>
-// CHECK: return %[[RESULT]]
-func.func @pad_static(%arg0: tensor<2x?x2xf32>, %pad_value: f32) -> tensor<2x3x4xf32> {
- %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index):
- tensor.yield %pad_value : f32
- } : tensor<2x?x2xf32> to tensor<2x3x4xf32>
- return %0 : tensor<2x3x4xf32>
-}
-
-
-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
- %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_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-// CHECK-LABEL: func @pad_static_source(
-// CHECK-SAME: %[[ARG0:.*]]: tensor<2x5x2xf32>, %[[PAD:.*]]: f32
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK: %[[INIT:.*]] = tensor.empty() : tensor<2x6x4xf32>
-// CHECK: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x6x4xf32>
-// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<2x6x4xf32>, tensor<2x6x4xf32>
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true, true]} : tensor<2x5x2xf32>, vector<2x5x2xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x5x2xf32>, tensor<2x6x4xf32>
-// CHECK: return %[[WRITE]]
-func.func @pad_static_source(%arg0: tensor<2x5x2xf32>, %pad_value: f32) -> tensor<2x6x4xf32> {
- %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index):
- tensor.yield %pad_value : f32
- } : tensor<2x5x2xf32> to tensor<2x6x4xf32>
- return %0 : tensor<2x6x4xf32>
-}
-
-
-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
- %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_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-
-// -----
-
-// CHECK-LABEL: func @pad_static_dynamic(
-// CHECK-SAME: %[[SRC:.*]]: tensor<1x2x2x?xf32>, %[[LOW:.*]]: index, %[[HIGH:.*]]: index
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
-// CHECK-DAG: %[[C5:.*]] = arith.constant 5 : index
-// CHECK: %[[V0:.*]] = arith.addi %[[LOW]], %[[C2]] : index
-// CHECK: %[[V1:.*]] = arith.addi %[[V0]], %[[C3]] : index
-// CHECK: %[[V2:.*]] = arith.addi %[[HIGH]], %[[C5]] : index
-// CHECK: %[[DIM3:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
-// CHECK: %[[V4:.*]] = arith.addi %[[DIM3]], %[[C3]] : index
-// CHECK: %[[V5:.*]] = arith.addi %[[V4]], %[[C2]] : index
-// CHECK: %[[INIT:.*]] = tensor.empty(%[[V1]], %[[V2]], %[[V5]]) : tensor<6x?x?x?xf32>
-// CHECK: %[[FILL:.*]] = linalg.fill ins(%{{.*}} : f32) outs(%[[INIT]] : tensor<6x?x?x?xf32>) -> tensor<6x?x?x?xf32>
-// CHECK: %[[SRCDIM:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
-// CHECK: %[[RESULT:.*]] = tensor.insert_slice %[[SRC]] into %[[FILL]][2, %[[LOW]], 3, 3] [1, 2, 2, %[[SRCDIM]]] [1, 1, 1, 1] : tensor<1x2x2x?xf32> into tensor<6x?x?x?xf32>
-// CHECK: return %[[RESULT]]
-func.func @pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
- %pad_value: f32) -> tensor<6x?x?x?xf32> {
- %0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
- tensor.yield %pad_value : f32
- } : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
- return %0 : tensor<6x?x?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
- %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_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-// CHECK-LABEL: func @pad_static_complex(
-// CHECK-NOT: vector<
-func.func @pad_static_complex(%arg0: tensor<2x5x2xcomplex<f32>>, %pad_value: complex<f32>) -> tensor<2x6x4xcomplex<f32>> {
- %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index):
- tensor.yield %pad_value : complex<f32>
- } : tensor<2x5x2xcomplex<f32>> to tensor<2x6x4xcomplex<f32>>
- return %0 : tensor<2x6x4xcomplex<f32>>
-}
-
-
-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
- %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_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-func.func private @make_vector() -> tensor<12x13xf32>
-
-// CHECK-LABEL: func.func @pad_and_insert_slice_dest(
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x5x6xf32>) -> tensor<1x12x13xf32> {
-// CHECK: %[[C0:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[CST:.*]] = arith.constant dense<5.000000e+00> : vector<1x12x13xf32>
-// CHECK: %[[C0_IDX:.*]] = arith.constant 0 : index
-// CHECK: %[[PAD_VAL:.*]] = arith.constant 5.000000e+00 : f32
-// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<1x12x13xf32>
-// CHECK: %[[WRITE_1:.*]] = vector.transfer_write %[[CST]], %[[EMPTY]]{{\[}}%[[C0_IDX]], %[[C0_IDX]], %[[C0_IDX]]] {in_bounds = [true, true, true]} : vector<1x12x13xf32>, tensor<1x12x13xf32>
-// CHECK: %[[READ_1:.*]] = vector.transfer_read %[[ARG_0]]{{\[}}%[[C0_IDX]], %[[C0_IDX]], %[[C0_IDX]]], %[[PAD_VAL]] {in_bounds = [true, true, true]} : tensor<1x5x6xf32>, vector<1x5x6xf32>
-// CHECK: %[[WRITE_2:.*]] = vector.transfer_write %[[READ_1]], %[[WRITE_1]]{{\[}}%[[C0_IDX]], %[[C0_IDX]], %[[C0_IDX]]] {in_bounds = [true, true, true]} : vector<1x5x6xf32>, tensor<1x12x13xf32>
-// CHECK: %[[MAKE_VEC:.*]] = call @make_vector() : () -> tensor<12x13xf32>
-// CHECK: %[[READ_2:.*]] = vector.transfer_read %[[MAKE_VEC]]{{\[}}%[[C0_IDX]], %[[C0_IDX]]], %[[C0]] {in_bounds = [true, true]} : tensor<12x13xf32>, vector<12x13xf32>
-// CHECK: %[[RES:.*]] = vector.transfer_write %[[READ_2]], %[[WRITE_2]]{{\[}}%[[C0_IDX]], %[[C0_IDX]], %[[C0_IDX]]] {in_bounds = [true, true]} : vector<12x13xf32>, tensor<1x12x13xf32>
-// CHECK: return %[[RES]] : tensor<1x12x13xf32>
-func.func @pad_and_insert_slice_dest(
- %arg0: tensor<1x5x6xf32>) -> tensor<1x12x13xf32> {
- %c5 = arith.constant 5.0 : f32
- %0 = tensor.pad %arg0 low[0, 0, 0] high[0, 7, 7] {
- ^bb0(%arg2: index, %arg3: index, %arg4: index):
- tensor.yield %c5 : f32
- } : tensor<1x5x6xf32> to tensor<1x12x13xf32>
- %1 = call @make_vector() : () -> tensor<12x13xf32>
- %r = tensor.insert_slice %1 into %0[0, 0, 0][1, 12, 13][1, 1, 1] : tensor<12x13xf32> into tensor<1x12x13xf32>
- return %r : tensor<1x12x13xf32>
-}
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-// CHECK-LABEL: func @pad_tensor_non_const_pad_value
-// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
-// CHECK-DAG: %[[C4:.*]] = arith.constant 4 : index
-// CHECK: %[[FILL:.*]] = tensor.generate
-// CHECK: %[[RES:.*]] = arith.mulf
-// CHECK: tensor.yield %[[RES]] : f32
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true]} : tensor<5x6xf32>, vector<5x6xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C3]], %[[C4]]] {in_bounds = [true, true]} : vector<5x6xf32>, tensor<12x13xf32>
-// CHECK: return %[[WRITE]]
-func.func @pad_tensor_non_const_pad_value(%arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
- %c0 = arith.constant 0 : index
- %c5 = arith.constant 5.0 : f32
- %0 = tensor.pad %arg0 low[3, 4] high[4, 3] {
- ^bb0(%arg1: index, %arg2: index):
- %i1 = arith.index_cast %arg1 : index to i32
- %i2 = arith.index_cast %arg2 : index to i32
- %f1 = arith.sitofp %i1 : i32 to f32
- %f2 = arith.sitofp %i2 : i32 to f32
- %m = arith.mulf %f1, %f2 : f32
- tensor.yield %m : f32
- } : tensor<5x6xf32> to tensor<12x13xf32>
- return %0 : tensor<12x13xf32>
-}
-
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
// CHECK-LABEL: func @sum_exp
func.func @sum_exp(%input: tensor<4x16x8xf32>, %output: tensor<4x16xf32>)
-> tensor<4x16xf32>
@@ -1805,29 +1604,6 @@ module attributes {transform.with_named_sequence} {
// -----
-// CHECK-LABEL: func @test_masked_pad_static_dynamic
-func.func @test_masked_pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
- %pad_value: f32) -> tensor<6x?x?x?xf32> {
- // CHECK: tensor.pad
- %0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
- tensor.yield %pad_value : f32
- } : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
- return %0 : tensor<6x?x?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
- %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_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
func.func @zero_dim_tensor(%input: tensor<f32>, %output: tensor<f32>) -> tensor<f32>
{
%0 = linalg.generic { indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ],
@@ -2001,94 +1777,3 @@ module attributes {transform.with_named_sequence} {
transform.yield
}
}
-
-// -----
-
-///----------------------------------------------------------------------------------------
-/// tensor.insert_slice
-///----------------------------------------------------------------------------------------
-
-// The pad value for xfer-read is neither needed nor available - use the default (0.0).
-
-// CHECK-LABEL: func @insert_static_slice_default_pad
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x2x3xf32>,
-// CHECK-SAME: %[[ARG_1:.*]]: tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32> {
-// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[C0:.*]] = arith.constant 0 : index
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG_0]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x2x3xf32>, vector<1x2x3xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[ARG_1]]{{\[}}%[[C0]], %[[C0]], %[[C0]], %[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
-// CHECK: return %[[WRITE]] : tensor<9x8x7x1x2x3xf32>
-func.func @insert_static_slice_default_pad(%arg0: tensor<1x2x3xf32>, %arg1: tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32> {
- %res = tensor.insert_slice %arg0 into %arg1[0, 0, 0, 0, 0, 0] [1, 1, 1, 1, 2, 3][1, 1, 1, 1, 1, 1] : tensor<1x2x3xf32> into tensor<9x8x7x1x2x3xf32>
- return %res : tensor<9x8x7x1x2x3xf32>
-}
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["tensor.insert_slice"]} 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_padding } : (!transform.any_op) -> !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-// Same as above, but there's a pad value available that should be used instead of the default value.
-
-// CHECK-LABEL: func.func @insert_static_slice_non_zero_pad
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x2x3xf32>,
-// CHECK-SAME: %[[PAD:.*]]: f32) -> tensor<9x8x7x1x2x3xf32> {
-// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<9x8x7x1x2x3xf32>
-// CHECK: %[[BC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<9x8x7x1x2x3xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[BC]], %[[EMPTY]]{{.*}} {in_bounds = [true, true, true, true, true, true]} : vector<9x8x7x1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG_0]]{{.*}}, %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x2x3xf32>, vector<1x2x3xf32>
-// CHECK: %[[RES:.*]] = vector.transfer_write %[[READ]], %[[WRITE]]{{.*}} {in_bounds = [true, true, true]} : vector<1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
-// CHECK: return %[[RES]] : tensor<9x8x7x1x2x3xf32>
-func.func @insert_static_slice_non_zero_pad(%arg0: tensor<1x2x3xf32>, %pad : f32) -> tensor<9x8x7x1x2x3xf32> {
- %init = tensor.empty() : tensor<9x8x7x1x2x3xf32>
- %fill = linalg.fill ins(%pad : f32) outs(%init : tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32>
- %res = tensor.insert_slice %arg0 into %fill[0, 0, 0, 0, 0, 0] [1, 1, 1, 1, 2, 3][1, 1, 1, 1, 1, 1] : tensor<1x2x3xf32> into tensor<9x8x7x1x2x3xf32>
- return %res : tensor<9x8x7x1x2x3xf32>
-}
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["tensor.insert_slice"]} 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
- transform.yield
- }
-}
-
-// -----
-
-// Same as above, but the source type has is dynamically shaped. This means
-// that the pad value is now required and the vector dim corresponding to the
-// dynamic shape has to be inferred from the shape of the destination tensor.
-
-// CHECK-LABEL: func.func @insert_dynamic_slice_non_zero_pad(
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x?x3xf32>,
-// CHECK-SAME: %[[PAD:.*]]: f32,
-// CHECK-SAME: %[[SIZE:.*]]: index) -> tensor<9x8x7x1x2x3xf32> {
-// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<9x8x7x1x2x3xf32>
-// CHECK: %[[BC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<9x8x7x1x2x3xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[BC]], %[[EMPTY]]{{.*}} {in_bounds = [true, true, true, true, true, true]} : vector<9x8x7x1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG_0]]{{.*}}, %[[PAD]] {in_bounds = [true, false, true]} : tensor<1x?x3xf32>, vector<1x2x3xf32>
-// CHECK: %[[RES:.*]] = vector.transfer_write %[[READ]], %[[WRITE]]{{.*}} {in_bounds = [true, true, true]} : vector<1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
-// CHECK: return %[[RES]] : tensor<9x8x7x1x2x3xf32>
-func.func @insert_dynamic_slice_non_zero_pad(%arg0: tensor<1x?x3xf32>, %pad : f32, %size: index) -> tensor<9x8x7x1x2x3xf32> {
- %init = tensor.empty() : tensor<9x8x7x1x2x3xf32>
- %fill = linalg.fill ins(%pad : f32) outs(%init : tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32>
- %res = tensor.insert_slice %arg0 into %fill[0, 0, 0, 0, 0, 0] [1, 1, 1, 1, %size, 3][1, 1, 1, 1, 1, 1] : tensor<1x?x3xf32> into tensor<9x8x7x1x2x3xf32>
- return %res : tensor<9x8x7x1x2x3xf32>
-}
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["tensor.insert_slice"]} 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
- transform.yield
- }
-}
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index 6b760a15afd56..8c6760fa50325 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -580,133 +580,6 @@ 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 {{.*}} : ...
[truncated]
|
javedabsar1
approved these changes
May 21, 2025
hanhanW
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May 21, 2025
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What is the rule of this? Why other vectorization tests are not moved? Will you move them in the future?
I dont have issues of finding tests. I typically just clicked into a few files and search the ops. I'd be confused about where to find tests if you land the change. Because some of them are in Linalg/ and some of them are in Linalg/vectorization. The other point is that I don't know which file I should use if I add a new support for vectorization.
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(I think your rule may be moving all vectorization tests of non-linalg ops to |
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Hey @hanhanW - you've raised a lot of great points, thanks! I do have a target end-state in mind that I’m trying to converge toward, and I should’ve communicated that more clearly - sorry about that. Here's a quick summary of the overall direction: This PR is just a small step in that direction. It’s intended to make one of my upcoming patches a bit easier to review (at least that’s the hope!).
Yes - at a minimum, I’d like to extract non-Linalg ops from "vectorization.mlir" and "vectorization-with-patterns.mlir". We already have a dedicated file for tensor.extract, so doing the same for other non-Linalg ops would bring more consistency. (Btw, let me rename+move "vectorize-tensor-extract-masked.mlir" + "vectorize-tensor-extract.mlir" as part of this change - to avoid sending too many PRs).
That's because you know this stuff too well! 😄 But from a different angle - when updating vectorization logic for something like These changes aren’t critical, but they do address some real friction I’ve experienced while updating or reviewing code. It’s mostly about future-proofing and making long-term maintenance easier. Hope this makes sense! |
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hanhanW
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May 22, 2025
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This patch moves vectorization tests for `tensor.pad` and
`tensor.insert_slice` into dedicated files under a new subdirectory for
the vectorizer. The goal is to better organize the growing set of tests,
which are currently difficult to navigate.
This change is also a preparatory step for upcoming work: I’ll soon be
updating the vectorization logic for `tensor.pad` +
`tensor.insert_slice`. With the new structure in place, two things will
be clear in follow-up changes:
* Only tests related to `tensor.pad` and `tensor.insert_slice` are
being updated.
* Only the relevant tests will be touched (e.g., when changing mask
generation, only tests involving masking will be affected).
… Ops (nfc) Move+rename other tests for Tensor Ops
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…t_slice Ops (nfc) Remove accidental test duplication
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…0877) This patch reorganises vectorisation tests for tensor ops: * Tests for `tensor.pad` and `tensor.insert_slice` are extracted into dedicated files under a new `vectorization/` subdirectory. * Test files for `tensor.extract` are renamed and moved to the same subdirectory. Goals: * Unify test file naming. * Better organise the growing set of tests, which are currently hard to navigate. This is also a preparatory step for upcoming changes. I’ll soon be updating the vectorisation logic for `tensor.pad` and `tensor.insert_slice`. With the new structure in place, follow-up changes will be easier to review: * Only tests related to those ops will be updated. * Changes (e.g., to masking logic) will be isolated to the relevant tests. This patch implements part of llvm#141025 - please see the ticket for full context.
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This patch reorganises vectorisation tests for tensor ops:
tensor.padandtensor.insert_sliceare extracted intodedicated files under a new
vectorization/subdirectory.tensor.extractare renamed and moved to the samesubdirectory.
Goals:
navigate.
This is also a preparatory step for upcoming changes. I’ll soon be updating the
vectorisation logic for
tensor.padandtensor.insert_slice. With the newstructure in place, follow-up changes will be easier to review:
This patch implements part of #141025 - please see the ticket for full context.