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llvm / llvm-project / mlir / ed39b2b36924579036fc70c1b20772a204e4be44 / . / test / Dialect / Linalg / transform-op-pad.mlir
blob: bc684b53c9b61e352f3b9fb07327c9315f280b8f [file] [log] [blame]
// RUN: mlir-opt --transform-interpreter -split-input-file -verify-diagnostics %s | FileCheck %s
#map = affine_map<()[s0] -> (-s0 + 12, 7)>
// CHECK-LABEL: @static_sizes_output_divisible
func.func @static_sizes_output_divisible(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>,
%arg2: tensor<24x25xf32>,
%iv0 : index, %iv1 : index, %iv2 : index) -> tensor<24x25xf32> {
%0 = affine.min #map()[%iv2]
// CHECK: %[[T0:.*]] = tensor.extract_slice %
// CHECK: %[[T1:.*]] = tensor.extract_slice %
// CHECK: %[[T2:.*]] = tensor.extract_slice %
%1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
%2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
%3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.
// CHECK: %[[T3:.*]] = tensor.pad %[[T0]] nofold
// CHECK: tensor.yield %[[CST]]
// CHECK: %[[T4:.*]] = tensor.pad %[[T1]] nofold
// CHECK: %[[T5:.*]] = linalg.matmul
// CHECK-SAME: ins(%[[T3]], %[[T4]] : tensor<4x7xf32>, tensor<7x5xf32>)
// CHECK-SAME: outs(%[[T2]] : tensor<4x5xf32>)
// CHECK: %[[T6:.*]] = tensor.extract_slice %[[T5]]
// CHECK: %[[T7:.*]] = bufferization.materialize_in_destination %[[T6]] in %[[T2]]
%4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
%5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
func.return %5 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 0]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.op<"bufferization.materialize_in_destination">)
%p = transform.num_associations %copy_back : (!transform.op<"bufferization.materialize_in_destination">) -> !transform.param<i64>
// expected-remark @below {{1}}
transform.debug.emit_param_as_remark %p : !transform.param<i64>
transform.yield
}
}
// -----
#map = affine_map<()[s0] -> (-s0 + 12, 7)>
// CHECK-LABEL: @pad_to_multiple
func.func @pad_to_multiple(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>,
%arg2: tensor<24x25xf32>,
%iv0 : index, %iv1 : index, %iv2 : index) -> tensor<24x25xf32> {
%0 = affine.min #map()[%iv2]
%1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
%2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
%3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
// CHECK: linalg.matmul
// CHECK-SAME: ins(%{{.*}}, %{{.*}} : tensor<4x7xf32>, tensor<7x6xf32>)
// CHECK-SAME: outs(%{{.*}} : tensor<4x6xf32>)
%4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
%5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
func.return %5 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0 pad_to_multiple_of [2, 2, 1] {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 0]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
#map = affine_map<()[s0] -> (-s0 + 12, 7)>
// CHECK-LABEL: @parametrized_pad_to_multiple
func.func @parametrized_pad_to_multiple(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>,
%arg2: tensor<24x25xf32>,
%iv0 : index, %iv1 : index, %iv2 : index) -> tensor<24x25xf32> {
%0 = affine.min #map()[%iv2]
%1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
%2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
%3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
// CHECK: linalg.matmul
// CHECK-SAME: ins(%{{.*}}, %{{.*}} : tensor<4x7xf32>, tensor<7x6xf32>)
// CHECK-SAME: outs(%{{.*}} : tensor<4x6xf32>)
%4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
%5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
func.return %5 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%c2 = transform.param.constant 2 : i64 -> !transform.param<i64>
%padded, %pad, %copy_back = transform.structured.pad %0 pad_to_multiple_of [%c2, 2, 1] {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 0]
} : (!transform.any_op, !transform.param<i64>) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
#map = affine_map<()[s0] -> (-s0 + 12, 7)>
// CHECK-LABEL: @static_sizes_output_divisible_on_empty_op
func.func @static_sizes_output_divisible_on_empty_op(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>, %iv0: index,
%iv1: index, %iv2: index) -> tensor<24x25xf32> {
%0 = affine.min #map()[%iv2]
// CHECK: %[[T0:.*]] = tensor.empty
// CHECK: %[[T1:.*]] = tensor.empty
// CHECK: %[[T2:.*]] = tensor.empty
%1 = tensor.empty(%0) : tensor<4x?xf32>
%2 = tensor.empty(%0) : tensor<?x5xf32>
%3 = tensor.empty() : tensor<4x5xf32>
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.
// CHECK: %[[T3:.*]] = tensor.pad %[[T0]] nofold
// CHECK: tensor.yield %[[CST]]
// CHECK: %[[T4:.*]] = tensor.pad %[[T1]] nofold
// CHECK: %[[T5:.*]] = linalg.matmul
// CHECK-SAME: ins(%[[T3]], %[[T4]] : tensor<4x7xf32>, tensor<7x5xf32>)
// CHECK-SAME: outs(%[[T2]] : tensor<4x5xf32>)
%4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
%5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
func.return %5 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 0]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
func.func @pad(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>,
%arg2: tensor<24x25xf32>) -> tensor<24x25xf32> {
// expected-note @below {{when applied to this op}}
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
// expected-error @below {{op expects a padding value of type 'f32', got 0 : i32}}
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0: i32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 0]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
func.func @pad(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>,
%arg2: tensor<24x25xf32>) -> tensor<24x25xf32> {
// expected-note @below {{when applied to this op}}
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
// expected-error @below {{expects a padding that parses to 'f32', got "{foo}"}}
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=["{foo}", 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 0]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// With all padded being static, there's nothing to pad. However, with the
// `nofold` attribute set (see `nofold_flags`), the corresponding pad Ops are
// preserved.
// CHECK-LABEL: @zero_pad_static(
func.func @zero_pad_static(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>,
%arg2: tensor<24x25xf32>) -> tensor<24x25xf32> {
// CHECK-SAME: %[[ARG_0:.*]]: tensor<24x12xf32>,
// CHECK-SAME: %[[ARG_1:.*]]: tensor<12x25xf32>,
// CHECK-SAME: %[[ARG_2:.*]]: tensor<24x25xf32>) -> tensor<24x25xf32> {
// CHECK: %[[PAD_ARG_0:.*]] = tensor.pad %[[ARG_0]] nofold low[0, 0] high[0, 0]
// CHECK: %[[PAD_ARG_1:.*]] = tensor.pad %[[ARG_1]] nofold low[0, 0] high[0, 0]
// CHECK-NOT: tensor.pad
// CHECK: %[[MATMUL:.*]] = linalg.matmul
// CHECK-SAME: ins(%[[PAD_ARG_0:.*]], %[[PAD_ARG_1:.*]] : tensor<24x12xf32>, tensor<12x25xf32>)
// CHECK-SAME: outs(%[[ARG_2]]
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 0]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// With all padded dims being static, there's nothing to pad. However, with the
// `nofold` attribute set (see `nofold_flags`), the corresponding pad Ops are
// preserved. Same as above, but some dims are now dynamic.
// CHECK-LABEL: @zero_pad_dynamic(
func.func @zero_pad_dynamic(%arg0: tensor<?x12xf32>,
%arg1: tensor<12x?xf32>,
%arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x12xf32>,
// CHECK-SAME: %[[ARG_1:.*]]: tensor<12x?xf32>,
// CHECK-SAME: %[[ARG_2:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[PAD_ARG_0:.*]] = tensor.pad %[[ARG_0]] nofold low[0, 0] high[0, 0]
// CHECK: %[[PAD_ARG_1:.*]] = tensor.pad %[[ARG_1]] nofold low[0, 0] high[0, 0]
// CHECK: %[[PAD_ARG_2:.*]] = tensor.pad %[[ARG_2]] nofold low[0, 0] high[0, 0]
// CHECK: %[[MATMUL:.*]] = linalg.matmul
// CHECK-SAME: ins(%[[PAD_ARG_0:.*]], %[[PAD_ARG_1:.*]] : tensor<?x12xf32>, tensor<12x?xf32>)
// CHECK-SAME: outs(%[[PAD_ARG_2]]
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x12xf32>, tensor<12x?xf32>) outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
func.return %0 : 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{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
// Note - only the static dim is padded
padding_dimensions=[2],
nofold_flags=[1, 1, 1]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// Impossible to get a bound for padding - fails
func.func @negative_no_ub_estimate(%arg0: tensor<?x12xf32>,
%arg1: tensor<12x?xf32>,
%arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
// expected-note @below {{target op}}
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x12xf32>, tensor<12x?xf32>) outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
func.return %0 : 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{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
// expected-error @below {{failed to pad op}}
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
// Note - attempting to pad non-static dim
padding_dimensions=[1],
nofold_flags=[1, 1, 1]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// Test dynamic padding using `use_prescribed_tensor_shapes`
// CHECK: #[[MAP:.*]] = affine_map<()[s0] -> (-s0 + (s0 ceildiv 7) * 7)>
// CHECK: @use_prescribed_tensor_shapes
// CHECK: (%[[ARG0:.*]]: tensor<?x12xf32>, %[[ARG1:.*]]: tensor<12x?xf32>
func.func @use_prescribed_tensor_shapes(%arg0: tensor<?x12xf32>,
%arg1: tensor<12x?xf32>,
%arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[C1_0:.*]] = arith.constant 1 : index
// CHECK: %[[DIM_0:.*]] = tensor.dim %[[ARG1]], %[[C1_0]] : tensor<12x?xf32>
// CHECK: %[[PADDING:.*]] = affine.apply #[[MAP]]()[%[[DIM_0]]]
// CHECK: %[[PADDED:.*]] = tensor.pad %[[ARG1]] low[0, 0] high[0, %[[PADDING]]] {
// CHECK: linalg.matmul ins(%[[ARG0]], %[[PADDED]] : tensor<?x12xf32>, tensor<12x?xf32>)
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x12xf32>, tensor<12x?xf32>) outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
func.return %0 : 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{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0
pad_to_multiple_of [7] use_prescribed_tensor_shapes {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[1]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
%func = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.any_op
transform.apply_patterns to %func {
transform.apply_patterns.canonicalization
} {apply_cse} : !transform.any_op
transform.yield
}
}
// -----
// Check that the padding can be applied even when the output argument of the
// linalg op is not produced by an empty op or an extract_slice op.
// CHECK-DAG: #[[$MAP_MIN:.*]] = affine_map<(d0) -> (-d0 + 2044, 16)>
// CHECK-DAG: #[[$MAP_TO_16:.*]] = affine_map<(d0) -> (-d0 + 16)>
// CHECK-LABEL: @outs_not_produced_by_empty_or_extract_slice(
// CHECK-SAME: %[[A:[^: ]*]]: tensor<128x2044xf32>,
// CHECK-SAME: %[[B:[^: ]*]]: tensor<2044x128xf32>)
func.func @outs_not_produced_by_empty_or_extract_slice(%a : tensor<128x2044xf32>, %b : tensor<2044x128xf32>) -> tensor<128x128xf32> {
%cst = arith.constant 0.000000e+00 : f32
%0 = tensor.empty() : tensor<128x128xf32>
%9 = linalg.fill ins(%cst : f32) outs(%0 : tensor<128x128xf32>) -> tensor<128x128xf32>
%c0 = arith.constant 0 : index
%c16 = arith.constant 16 : index
%c2044 = arith.constant 2044 : index
// CHECK: scf.for %[[ARG3:.*]] = {{.*}} iter_args(%[[ARG4:.*]] = %{{.*}})
%10 = scf.for %arg3 = %c0 to %c2044 step %c16 iter_args(%arg4 = %9) -> (tensor<128x128xf32>) {
// CHECK: %[[MIN:.*]] = affine.min #[[$MAP_MIN]](%[[ARG3]])
%11 = affine.min affine_map<(d0) -> (-d0 + 2044, 16)>(%arg3)
// CHECK: %[[A_SLICE:.*]] = tensor.extract_slice %[[A]]
// CHECK: %[[B_SLICE:.*]] = tensor.extract_slice %[[B]]
%extracted_slice_2 = tensor.extract_slice %a[0, %arg3] [128, %11] [1, 1] : tensor<128x2044xf32> to tensor<128x?xf32>
%extracted_slice_3 = tensor.extract_slice %b[%arg3, 0] [%11, 128] [1, 1] : tensor<2044x128xf32> to tensor<?x128xf32>
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.
// CHECK-DAG: %[[TO_16:.*]] = affine.apply #[[$MAP_TO_16]](%[[MIN]])
// CHECK: %[[PADDED_A_SLICE:.*]] = tensor.pad %[[A_SLICE]] nofold low[0, 0] high[0, %[[TO_16]]]
// CHECK: tensor.yield %[[CST]]
// CHECK: %[[PADDED_B_SLICE:.*]] = tensor.pad %[[B_SLICE]] nofold
// The output shape is already padded, so actually we shouldn't
// add anything to the upper bound.
// CHECK: %[[PADDED_ARG4:.*]] = tensor.pad %[[ARG4]] nofold low[{{.*}}] high[0, 0]
// CHECK: %[[T5:.*]] = linalg.matmul
// CHECK-SAME: ins(%[[PADDED_A_SLICE]], %[[PADDED_B_SLICE]] : tensor<128x16xf32>, tensor<16x128xf32>)
// CHECK-SAME: outs(%[[PADDED_ARG4]] : tensor<128x128xf32>)
%res = linalg.matmul ins(%extracted_slice_2, %extracted_slice_3 : tensor<128x?xf32>, tensor<?x128xf32>) outs(%arg4 : tensor<128x128xf32>) -> tensor<128x128xf32>
scf.yield %res : tensor<128x128xf32>
}
return %10 : tensor<128x128xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 1]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
#map = affine_map<()[s0] -> (-s0 + 12, 7)>
// CHECK-LABEL: @pack_everything
func.func @pack_everything(%arg0: tensor<24x12xf32>,
%arg1: tensor<12x25xf32>,
%arg2: tensor<24x25xf32>,
%iv0 : index, %iv1 : index, %iv2 : index) -> tensor<24x25xf32> {
%0 = affine.min #map()[%iv2]
// CHECK: %[[T0:.*]] = tensor.extract_slice %
// CHECK: %[[T1:.*]] = tensor.extract_slice %
// CHECK: %[[T2:.*]] = tensor.extract_slice %
%1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>
%2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>
%3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.
// CHECK: %[[PAD0:.*]] = tensor.pad %[[T0]] nofold
// CHECK: %[[PAD1:.*]] = tensor.pad %[[T1]] nofold
// CHECK: %[[PAD2:.*]] = tensor.pad %[[T2]] nofold
// CHECK: %[[T5:.*]] = linalg.matmul
// CHECK-SAME: ins(%[[PAD0]], %[[PAD1]] : tensor<4x7xf32>, tensor<7x5xf32>)
// CHECK-SAME: outs(%[[PAD2]] : tensor<4x5xf32>)
// Get unpadded result (no-op in this example).
// CHECK: %[[T6:.*]] = tensor.extract_slice %[[T5]]
// Copy back result to the original buffer, so that the destination of the
// computation does not change.
// CHECK: %[[T7:.*]] = bufferization.materialize_in_destination %[[T6]] in %[[T2]]
%4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>
// CHECK: %[[T8:.*]] = tensor.insert_slice %[[T7]] into %{{.*}}
%5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
func.return %5 : tensor<24x25xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%padded, %pad, %copy_back = transform.structured.pad %0 {
padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
nofold_flags=[1, 1, 1]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}