// RUN: mlir-opt %s --transform-interpreter --split-input-file -canonicalize | FileCheck %s
// CHECK-LABEL: func.func @fuse_unary
func.func @fuse_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[RES:.*]] = scf.for
// CHECK: scf.for
// CHECK: linalg.elemwise_unary
// CHECK: linalg.elemwise_binary
// CHECK: return %[[RES]]
%0 = linalg.elemwise_unary ins(%arg0 : tensor<?x?xf32>)
outs(%arg1: tensor<?x?xf32>) -> tensor<?x?xf32>
%1 = linalg.elemwise_binary ins(%0, %arg0 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg1: tensor<?x?xf32>) -> 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{["linalg.elemwise_binary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [32, 32], tile_interchange = [0, 1]}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// CHECK-LABEL: func.func @fuse_unary
func.func @fuse_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[PARTIAL_RES:.*]] = scf.for
// CHECK: scf.for
// CHECK: linalg.elemwise_unary
// CHECK: linalg.elemwise_binary
// CHECK: %[[RES:.*]] = scf.for {{.*}}%[[PARTIAL_RES]]
// CHECK: scf.for
// CHECK: linalg.elemwise_unary
// CHECK: linalg.elemwise_binary
// CHECK: return %[[RES]]
%0 = linalg.elemwise_unary ins(%arg0 : tensor<?x?xf32>)
outs(%arg1: tensor<?x?xf32>) -> tensor<?x?xf32>
%1 = linalg.elemwise_binary ins(%0, %arg0 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg1: tensor<?x?xf32>) -> 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{["linalg.elemwise_binary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [32, 32], tile_interchange = [0, 1]}
: (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">, !transform.any_op)
transform.loop.peel %loops#0 : (!transform.op<"scf.for">) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// CHECK-LABEL: func.func @interchange_reduction
// CHECK-SAME: (%[[INPUT:.+]]: tensor<12x7x25xf32>)
func.func @interchange_reduction(%input: tensor<12x7x25xf32>) -> tensor<12x25xf32> {
%five = arith.constant 5.0 : f32
%init = tensor.empty() : tensor<12x25xf32>
// CHECK-DAG: %[[INIT:.+]] = tensor.empty()
// CHECK-DAG: %[[C5:.+]] = arith.constant 5 : index
// CHECK-DAG: %[[C7:.+]] = arith.constant 7 : index
// CHECK-DAG: %[[C4:.+]] = arith.constant 4 : index
// CHECK: %[[RES:.*]] = scf.for %[[IV0:.+]] = %{{.+}} to %{{.+}} step %[[C5]] iter_args(%[[FOR_ARG0:.+]] = %[[INIT]])
// CHECK: scf.for %[[IV1:.+]] = %{{.+}} to %{{.+}} step %[[C7]] iter_args(%[[FOR_ARG1:.+]] = %[[FOR_ARG0]])
// CHECK: %[[OUT_SLICE0:.+]] = tensor.extract_slice %[[INPUT]][%[[IV0]], 0, %[[IV1]]]
// CHECK: %[[OUT_SLICE1:.+]] = tensor.extract_slice %[[FOR_ARG1]][%[[IV0]], %[[IV1]]]
// CHECK: %[[FILL:.+]] = linalg.fill {{.+}} outs(%[[OUT_SLICE1]] : tensor<?x?xf32>)
// CHECK: scf.for %[[IV2:.+]] = %{{.+}} to %{{.+}} step %[[C4]] iter_args(%[[FOR_ARG2:.+]] = %[[FILL]])
// CHECK: %[[IN_SLICE:.+]] = tensor.extract_slice %[[OUT_SLICE0]]
// CHECK: %[[OUT_SLICE2:.+]] = tensor.extract_slice %[[FOR_ARG2]][0, 0]
// CHECK: linalg.generic {{.+}} ins(%[[IN_SLICE]] : tensor<?x?x?xf32>) outs(%[[OUT_SLICE2]] : tensor<?x?xf32>)
// CHECK: return %[[RES]]
%fill = linalg.fill ins(%five : f32) outs(%init : tensor<12x25xf32>) -> tensor<12x25xf32>
%0 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d2)>],
iterator_types = ["parallel", "reduction", "parallel"]
} ins(%input : tensor<12x7x25xf32>) outs(%fill : tensor<12x25xf32>) {
^bb0(%arg0: f32, %arg1: f32):
%2 = arith.addf %arg0, %arg1 : f32
linalg.yield %2 : f32
} -> tensor<12x25xf32>
func.return %0 : tensor<12x25xf32>
}
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, %loops:2 = transform.structured.fuse %0 {tile_sizes = [5, 0, 7], tile_interchange = [0, 2, 1]}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
%2, %loops_2 = transform.structured.tile_using_for %1 tile_sizes [0, 4]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// CHECK-LABEL: func.func @unpack_elemwise
// CHECK: %[[RES:.*]] = scf.for
// CHECK: scf.for
// CHECK: tensor.unpack
// CHECK: linalg.elemwise_unary
// CHECK: return %[[RES]]
func.func @unpack_elemwise(%arg0: tensor<16x48x8x8xf32>, %arg1: tensor<128x384xf32>) -> tensor<128x384xf32> {
%0 = tensor.empty() : tensor<128x384xf32>
%1 = tensor.unpack %arg0 inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %0
: tensor<16x48x8x8xf32> -> tensor<128x384xf32>
%2 = linalg.elemwise_unary ins(%1: tensor<128x384xf32>)
outs(%arg1: tensor<128x384xf32>) -> tensor<128x384xf32>
return %2 : tensor<128x384xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [16, 32], tile_interchange = [0, 1]}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// CHECK-LABEL: func.func @pack_elemwise
// CHECK: %[[RES:.*]] = scf.for
// CHECK: scf.for
// CHECK: tensor.pack
// CHECK: linalg.elemwise_unary
// CHECK: return %[[RES]]
func.func @pack_elemwise(%arg0: tensor<128x384xf32>, %arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32> {
%0 = tensor.empty() : tensor<16x48x8x8xf32>
%1 = tensor.pack %arg0 inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %0
: tensor<128x384xf32> -> tensor<16x48x8x8xf32>
%2 = linalg.elemwise_unary ins(%1: tensor<16x48x8x8xf32>)
outs(%arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32>
return %2 : tensor<16x48x8x8xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [3, 5, 0, 0]}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// CHECK-LABEL: func.func @nofuse_pack_elemwise
// CHECK: tensor.pack
// CHECK: %[[RES:.*]] = scf.for
// CHECK: scf.for
// CHECK: linalg.elemwise_unary
// CHECK: return %[[RES]]
func.func @nofuse_pack_elemwise(%arg0: tensor<128x384xf32>, %arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32> {
%0 = tensor.empty() : tensor<16x48x8x8xf32>
%1 = tensor.pack %arg0 inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %0
: tensor<128x384xf32> -> tensor<16x48x8x8xf32>
%2 = linalg.elemwise_unary ins(%1: tensor<16x48x8x8xf32>)
outs(%arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32>
return %2 : tensor<16x48x8x8xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:3 = transform.structured.fuse %0 {tile_sizes = [3, 5, 2, 0]}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
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