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|
// RUN: mlir-opt %s -linalg-fuse-elementwise-ops="allow-folding-unit-dim-reshapes=false" -split-input-file | FileCheck %s
// RUN: mlir-opt %s -linalg-fuse-elementwise-ops="allow-folding-unit-dim-reshapes=true" -split-input-file | FileCheck %s --check-prefix=FOLDUNITDIM
#map0 = affine_map<(d0, d1, d2) -> (d2, d0, d1)>
#map1 = affine_map<(d0, d1, d2) -> (d1, d2, d0)>
#map2 = affine_map<(d0, d1, d2) -> ()>
func @generic_op_reshape_producer_fusion(%arg0 : tensor<?x?x4x?xf32>,
%arg1 : tensor<?x?x?xf32>,
%arg2 : f32) ->
tensor<?x?x?xf32>
{
%0 = linalg.tensor_collapse_shape %arg0 [[0], [1, 2], [3]] :
tensor<?x?x4x?xf32> into tensor<?x?x?xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1, #map2, #map1],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0, %arg1, %arg2 : tensor<?x?x?xf32>, tensor<?x?x?xf32>, f32)
outs(%0 : tensor<?x?x?xf32>) {
^bb0(%arg3: f32, %arg4: f32, %arg5: f32, %s: f32): // no predecessors
%1 = mulf %arg3, %arg4 : f32
%2 = addf %1, %arg5 : f32
linalg.yield %2 : f32
} -> tensor<?x?x?xf32>
return %1 : tensor<?x?x?xf32>
}
// CHECK-DAG: #[[MAP5:.+]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d1, d2)>
// CHECK-DAG: #[[MAP6:.+]] = affine_map<(d0, d1, d2, d3) -> (d2, d3, d0, d1)>
// CHECK-DAG: #[[MAP7:.+]] = affine_map<(d0, d1, d2, d3) -> ()>
// CHECK: func @generic_op_reshape_producer_fusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x4x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: f32
// CHECK: %[[T0:.+]] = linalg.tensor_collapse_shape %[[ARG0]]
// CHECK-SAME: [0], [1, 2], [3]
// CHECK: %[[T1:.+]] = linalg.tensor_expand_shape %[[ARG1]]
// CHECK-SAME: [0], [1], [2, 3]
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP5]], #[[MAP6]], #[[MAP7]], #[[MAP6]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[ARG0]], %[[T1]], %[[ARG2]] : tensor<?x?x4x?xf32>, tensor<?x?x?x4xf32>, f32)
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x?x4xf32>)
// CHECK: %[[T4:.+]] = linalg.tensor_collapse_shape %[[T3]]
// CHECK-SAME: [0], [1], [2, 3]
// CHECK-SAME: tensor<?x?x?x4xf32> into tensor<?x?x?xf32>
// CHECK: return %[[T4]]
// -----
#map0 = affine_map<(d0, d1) -> (d0, d1)>
#map1 = affine_map<(d0, d1) -> ()>
func @generic_op_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
%arg1 : tensor<?x?xf32>,
%arg2 : f32) ->
tensor<?x4x?x5xf32>
{
%0 = linalg.generic {
indexing_maps = [#map0, #map0, #map1, #map0],
iterator_types = ["parallel", "parallel"]}
ins(%arg0, %arg1, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>, f32)
outs(%arg0 : tensor<?x?xf32>) {
^bb0(%arg3: f32, %arg4: f32, %arg5: f32, %s: f32): // no predecessors
%1 = mulf %arg3, %arg4 : f32
%2 = addf %1, %arg5 : f32
linalg.yield %2 : f32
} -> tensor<?x?xf32>
%1 = linalg.tensor_expand_shape %0 [[0], [1, 2, 3]] :
tensor<?x?xf32> into tensor<?x4x?x5xf32>
return %1 : tensor<?x4x?x5xf32>
}
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3) -> ()>
// CHECK: func @generic_op_reshape_consumer_fusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: f32
// CHECK: %[[T0:.+]] = linalg.tensor_expand_shape %[[ARG0]]
// CHECK-SAME: [0], [1, 2, 3]
// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x?x5xf32>
// CHECK: %[[T1:.+]] = linalg.tensor_expand_shape %[[ARG1]]
// CHECK-SAME: [0], [1, 2, 3]
// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x?x5xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP2]], #[[MAP3]], #[[MAP2]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[T0]], %[[T1]], %[[ARG2]] : tensor<?x4x?x5xf32>, tensor<?x4x?x5xf32>, f32)
// CHECK-SAME: outs(%{{.+}} : tensor<?x4x?x5xf32>)
// CHECK: return %[[T3]] : tensor<?x4x?x5xf32>
// -----
func @reshape_as_consumer_permutation
(%a : tensor<?x?x?xf32>, %b : tensor<?x?xf32>)
-> tensor<?x2x?x3x4x?xf32> {
%c = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d0, d2)>,
affine_map<(d0, d1, d2) -> (d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d2, d1)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%a, %b : tensor<?x?x?xf32>, tensor<?x?xf32>)
outs(%a : tensor<?x?x?xf32>) {
^bb0(%arg0 : f32, %arg1: f32, %s: f32):
%1 = addf %arg0, %arg1 : f32
linalg.yield %1 : f32
} -> tensor<?x?x?xf32>
%d = linalg.tensor_expand_shape %c [[0, 1], [2], [3, 4, 5]]
: tensor<?x?x?xf32> into tensor<?x2x?x3x4x?xf32>
return %d : tensor<?x2x?x3x4x?xf32>
}
// CHECK-DAG: #[[MAP8:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d2, d3, d4, d0, d1, d5)>
// CHECK-DAG: #[[MAP9:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d2, d3, d4, d5)>
// CHECK-DAG: #[[MAP10:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d5, d2, d3, d4)>
// CHECK: func @reshape_as_consumer_permutation
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK: %[[T0:.+]] = linalg.tensor_expand_shape %[[ARG0]]
// CHECK-SAME: [0, 1, 2], [3, 4], [5]
// CHECK-SAME: tensor<?x?x?xf32> into tensor<3x4x?x?x2x?xf32>
// CHECK: %[[T1:.+]] = linalg.tensor_expand_shape %[[ARG1]]
// CHECK-SAME: [0, 1, 2], [3]
// CHECK-SAME: tensor<?x?xf32> into tensor<3x4x?x?xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP8]], #[[MAP9]], #[[MAP10]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[T0]], %[[T1]] : tensor<3x4x?x?x2x?xf32>, tensor<3x4x?x?xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<?x2x?x3x4x?xf32>)
// CHECK: return %[[T3]] : tensor<?x2x?x3x4x?xf32>
// -----
#map0 = affine_map<(d0, d1) -> (d0, d1)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d2)>
func @generic_op_reshape_consumer_static(%arg0: tensor<264x4xf32>)
-> tensor<8x33x4xf32> {
%cst = constant dense<2.000000e+00> : tensor<264x4xf32>
%0 = linalg.init_tensor [264, 4] : tensor<264x4xf32>
%1 = linalg.generic {
indexing_maps = [#map0, #map0, #map0],
iterator_types = ["parallel", "parallel"]}
ins(%arg0, %cst : tensor<264x4xf32>, tensor<264x4xf32>)
outs(%0 : tensor<264x4xf32>) {
^bb0(%arg1: f32, %arg2: f32, %s: f32): // no predecessors
%2 = mulf %arg1, %arg2 : f32
linalg.yield %2 : f32
} -> tensor<264x4xf32>
%2 = linalg.tensor_expand_shape %1 [[0, 1], [2]] :
tensor<264x4xf32> into tensor<8x33x4xf32>
return %2 : tensor<8x33x4xf32>
}
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generic_op_reshape_consumer_static
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<264x4xf32>
// CHECK: %[[T0:.+]] = linalg.tensor_expand_shape %[[ARG0]]
// CHECK-SAME: [0, 1], [2]
// CHECK-SAME: tensor<264x4xf32> into tensor<8x33x4xf32>
// CHECK: %[[T1:.+]] = linalg.init_tensor [8, 33, 4]
// CHECK: %[[T2:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP2]]]
// CHECK-SAME: ["parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[T0]] : tensor<8x33x4xf32>)
// CHECK-SAME: outs(%[[T1]] : tensor<8x33x4xf32>)
// CHECK: return %[[T2]] : tensor<8x33x4xf32>
// -----
#map0 = affine_map<(d0, d1, d2) -> (d2, d0, d1)>
#map1 = affine_map<(d0, d1, d2) -> (d1, d2, d0)>
func @indexed_consumer_reshape_producer_fusion(%arg0 : tensor<?x?x4x?xi32>,
%arg1 : tensor<?x?x?xi32>) ->
tensor<?x?x?xi32>
{
%0 = linalg.tensor_collapse_shape %arg0 [[0], [1, 2], [3]]:
tensor<?x?x4x?xi32> into tensor<?x?x?xi32>
%1 = linalg.generic {
indexing_maps = [#map0, #map1, #map1],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0, %arg1 : tensor<?x?x?xi32>, tensor<?x?x?xi32>)
outs(%0 : tensor<?x?x?xi32>) {
^bb0(%arg3: i32, %arg4: i32, %s: i32):
%idx0 = linalg.index 0 : index
%idx1 = linalg.index 1 : index
%idx2 = linalg.index 2 : index
%1 = muli %arg3, %arg4 : i32
%2 = index_cast %idx0 : index to i32
%3 = addi %1, %2 : i32
%4 = index_cast %idx1 : index to i32
%5 = addi %3, %4 : i32
%6 = index_cast %idx2 : index to i32
%7 = addi %5, %6 : i32
linalg.yield %7 : i32
} -> tensor<?x?x?xi32>
return %1 : tensor<?x?x?xi32>
}
// Only check the body in the indexed version of the test.
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1) -> (d0 + d1 * 4)>
// CHECK: func @indexed_consumer_reshape_producer_fusion
// CHECK: linalg.generic
// CHECK: ^{{.*}}(
// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: i32, %[[ARG4:[a-zA-Z0-9]+]]: i32,
// CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: i32)
// CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
// CHECK-DAG: %[[IDX1:.+]] = linalg.index 1 : index
// CHECK-DAG: %[[IDX2:.+]] = linalg.index 2 : index
// CHECK-DAG: %[[IDX3:.+]] = linalg.index 3 : index
// CHECK-DAG: %[[T3:.+]] = affine.apply #[[MAP]](%[[IDX1]], %[[IDX0]])
// CHECK: %[[T4:.+]] = muli %[[ARG3]], %[[ARG4]]
// CHECK: %[[T5:.+]] = index_cast %[[T3]]
// CHECK: %[[T6:.+]] = addi %[[T4]], %[[T5]]
// CHECK: %[[T7:.+]] = index_cast %[[IDX2]]
// CHECK: %[[T8:.+]] = addi %[[T6]], %[[T7]]
// CHECK: %[[T9:.+]] = index_cast %[[IDX3]]
// CHECK: %[[T10:.+]] = addi %[[T8]], %[[T9]]
// CHECK: linalg.yield %[[T10]]
// -----
#map0 = affine_map<(d0, d1) -> (d0, d1)>
func @indexed_producer_reshape_consumer_fusion(%arg0 : tensor<?x?xi32>,
%arg1 : tensor<?x?xi32>) ->
tensor<?x?x4x5xi32>
{
%0 = linalg.generic {
indexing_maps = [#map0, #map0, #map0],
iterator_types = ["parallel", "parallel"]}
ins(%arg0, %arg1 : tensor<?x?xi32>, tensor<?x?xi32>)
outs(%arg0 : tensor<?x?xi32>) {
^bb0(%arg3: i32, %arg4: i32, %s: i32): // no predecessors
%idx0 = linalg.index 0 : index
%idx1 = linalg.index 1 : index
%1 = muli %arg3, %arg4 : i32
%2 = index_cast %idx0 : index to i32
%3 = addi %1, %2 : i32
%4 = index_cast %idx1 : index to i32
%5 = addi %3, %4 : i32
linalg.yield %5 : i32
} -> tensor<?x?xi32>
%1 = linalg.tensor_expand_shape %0 [[0], [1, 2, 3]] :
tensor<?x?xi32> into tensor<?x?x4x5xi32>
return %1 : tensor<?x?x4x5xi32>
}
// Only check the body in the indexed version of the test.
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0 + d1 * 5 + d2 * 20)>
// CHECK: func @indexed_producer_reshape_consumer_fusion
// CHECK: linalg.generic
// CHECK: ^{{.*}}(
// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: i32, %[[ARG4:[a-zA-Z0-9]+]]: i32,
// CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: i32)
// CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
// CHECK-DAG: %[[IDX1:.+]] = linalg.index 1 : index
// CHECK-DAG: %[[IDX2:.+]] = linalg.index 2 : index
// CHECK-DAG: %[[IDX3:.+]] = linalg.index 3 : index
// CHECK-DAG: %[[T3:.+]] = affine.apply #[[MAP]](%[[IDX3]], %[[IDX2]], %[[IDX1]])
// CHECK: %[[T4:.+]] = muli %[[ARG3]], %[[ARG4]]
// CHECK: %[[T5:.+]] = index_cast %[[IDX0]]
// CHECK: %[[T6:.+]] = addi %[[T4]], %[[T5]]
// CHECK: %[[T7:.+]] = index_cast %[[T3]]
// CHECK: %[[T8:.+]] = addi %[[T6]], %[[T7]]
// CHECK: linalg.yield %[[T8]]
// -----
func @reshape_as_consumer_permutation
(%a : tensor<210x6x4xi32>, %b : tensor<210x4xi32>)
-> tensor<2x3x4x5x6x7xi32> {
%shape = linalg.init_tensor [6, 4, 210] : tensor<6x4x210xi32>
%c = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d1, d0, d2)>,
affine_map<(d0, d1, d2) -> (d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d2, d1)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%a, %b : tensor<210x6x4xi32>, tensor<210x4xi32>)
outs(%shape : tensor<6x4x210xi32>) {
^bb0(%arg3 : i32, %arg4: i32, %s: i32):
%idx0 = linalg.index 0 : index
%idx1 = linalg.index 1 : index
%idx2 = linalg.index 2 : index
%1 = addi %arg3, %arg4 : i32
%2 = index_cast %idx0 : index to i32
%3 = addi %1, %2 : i32
%4 = index_cast %idx1 : index to i32
%5 = addi %3, %4 : i32
%6 = index_cast %idx2 : index to i32
%7 = addi %5, %6 : i32
linalg.yield %7 : i32
} -> tensor<6x4x210xi32>
%d = linalg.tensor_expand_shape %c [[0, 1], [2], [3, 4, 5]]
: tensor<6x4x210xi32> into tensor<2x3x4x5x6x7xi32>
return %d : tensor<2x3x4x5x6x7xi32>
}
// CHECK-DAG: #[[MAP5:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d2, d3, d4, d0, d1, d5)>
// CHECK-DAG: #[[MAP6:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d2, d3, d4, d5)>
// CHECK-DAG: #[[MAP7:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d5, d2, d3, d4)>
// CHECK-DAG: #[[MAP8:.+]] = affine_map<(d0, d1) -> (d0 + d1 * 3)>
// CHECK-DAG: #[[MAP9:.+]] = affine_map<(d0, d1, d2) -> (d0 + d1 * 7 + d2 * 42)>
// CHECK: func @reshape_as_consumer_permutation
// CHECK-SAME: %[[ARG0:.+]]: tensor<210x6x4xi32>
// CHECK-SAME: %[[ARG1:.+]]: tensor<210x4xi32>
// CHECK-DAG: %[[T1:.+]] = linalg.tensor_expand_shape %[[ARG0]]
// CHECK-SAME: [0, 1, 2], [3, 4], [5]
// CHECK-DAG: %[[T2:.+]] = linalg.tensor_expand_shape %[[ARG1]]
// CHECK-SAME: [0, 1, 2], [3]
// CHECK-DAG: %[[T0:.+]] = linalg.init_tensor [2, 3, 4, 5, 6, 7]
// CHECK: %[[T4:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP5]], #[[MAP6]], #[[MAP7]]]
// CHECK-SAME: ins(%[[T1]], %[[T2]] : tensor<5x6x7x2x3x4xi32>, tensor<5x6x7x4xi32>)
// CHECK-SAME: outs(%[[T0]] : tensor<2x3x4x5x6x7xi32>)
// CHECK: ^{{.+}}(
// CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: i32, %[[ARG9:[a-zA-Z0-9]+]]: i32,
// CHECK-SAME: %[[ARG10:[a-zA-Z0-9]+]]: i32)
// CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
// CHECK-DAG: %[[IDX1:.+]] = linalg.index 1 : index
// CHECK-DAG: %[[IDX2:.+]] = linalg.index 2 : index
// CHECK-DAG: %[[IDX3:.+]] = linalg.index 3 : index
// CHECK-DAG: %[[IDX4:.+]] = linalg.index 4 : index
// CHECK-DAG: %[[IDX5:.+]] = linalg.index 5 : index
// CHECK-DAG: %[[T5:.+]] = affine.apply #[[MAP8]](%[[IDX1]], %[[IDX0]])
// CHECK-DAG: %[[T6:.+]] = affine.apply #[[MAP9]](%[[IDX4]], %[[IDX3]], %[[IDX2]])
// CHECK-DAG: %[[T7:.+]] = addi %[[ARG8]], %[[ARG9]]
// CHECK: %[[T8:.+]] = index_cast %[[T5]]
// CHECK: %[[T9:.+]] = addi %[[T7]], %[[T8]]
// CHECK: %[[T10:.+]] = index_cast %[[T6]]
// CHECK: %[[T11:.+]] = addi %[[T9]], %[[T10]]
// CHECK: %[[T12:.+]] = index_cast %[[IDX5]]
// CHECK: %[[T13:.+]] = addi %[[T11]], %[[T12]]
// -----
func @reshape_as_producer_projected_permutation(
%arg0 : tensor<33x8x?xi32>, %shape : tensor<264x?x4xi32>) -> tensor<264x?x4xi32>
{
%0 = linalg.tensor_collapse_shape %arg0 [[0, 1], [2]]
: tensor<33x8x?xi32> into tensor<264x?xi32>
%1 = linalg.generic
{indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0 : tensor<264x?xi32>)
outs(%shape : tensor<264x?x4xi32>) {
^bb0(%arg1: i32, %s: i32): // no predecessors
%idx0 = linalg.index 0 : index
%idx1 = linalg.index 1 : index
%idx2 = linalg.index 2 : index
%2 = index_cast %idx0 : index to i32
%3 = addi %arg1, %2 : i32
%4 = index_cast %idx1 : index to i32
%5 = addi %3, %4 : i32
%6 = index_cast %idx2 : index to i32
%7 = addi %5, %6 : i32
linalg.yield %7 : i32
} -> tensor<264x?x4xi32>
return %1 : tensor<264x?x4xi32>
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0 + d1 * 8)>
// CHECK: @reshape_as_producer_projected_permutation
// CHECK-SAME: %[[ARG0:.+]]: tensor<33x8x?xi32>
// CHECK: %[[RES:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: ins(%[[ARG0]] : tensor<33x8x?xi32>)
// CHECK: ^{{.+}}(
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: i32,
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: i32)
// CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
// CHECK-DAG: %[[IDX1:.+]] = linalg.index 1 : index
// CHECK-DAG: %[[IDX2:.+]] = linalg.index 2 : index
// CHECK-DAG: %[[IDX3:.+]] = linalg.index 3 : index
// CHECK-DAG: %[[T0:.+]] = affine.apply #[[MAP2]](%[[IDX1]], %[[IDX0]])
// CHECK: %[[T1:.+]] = index_cast %[[T0]] : index to i32
// CHECK: %[[T2:.+]] = addi %[[ARG1]], %[[T1]] : i32
// CHECK: %[[T3:.+]] = index_cast %[[IDX2]] : index to i32
// CHECK: %[[T4:.+]] = addi %[[T2]], %[[T3]] : i32
// CHECK: %[[T5:.+]] = index_cast %[[IDX3]] : index to i32
// CHECK: %[[T6:.+]] = addi %[[T4]], %[[T5]] : i32
// CHECK: linalg.yield %[[T6]] : i32
// CHECK: %[[RES2:.+]] = linalg.tensor_collapse_shape %[[RES]]
// CHECK-SAME: [0, 1], [2], [3]
// CHECK-SAME: : tensor<33x8x?x4xi32> into tensor<264x?x4xi32>
// CHECK: return %[[RES2]] : tensor<264x?x4xi32>
// -----
#map0 = affine_map<(d0, d1) -> (d0, d1)>
#map1 = affine_map<(d0, d1) -> (d1, d0)>
func @generic_op_reshape_consumer_fusion_projected(%arg0 : tensor<?x?xf32>,
%arg1 : tensor<?x?xf32>) ->
tensor<?x?x4x5xf32>
{
%0 = linalg.generic {
indexing_maps = [#map0, #map0, #map1],
iterator_types = ["parallel", "parallel"]}
ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg0 : tensor<?x?xf32>) {
^bb0(%arg3: f32, %arg4: f32, %s: f32): // no predecessors
%1 = mulf %arg3, %arg4 : f32
linalg.yield %1 : f32
} -> tensor<?x?xf32>
%1 = linalg.tensor_expand_shape %0 [[0], [1, 2, 3]] :
tensor<?x?xf32> into tensor<?x?x4x5xf32>
return %1 : tensor<?x?x4x5xf32>
}
// CHECK-DAG: #[[MAP4:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-DAG: #[[MAP5:.+]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d1, d2)>
// CHECK: func @generic_op_reshape_consumer_fusion_projected
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK: %[[T0:.+]] = linalg.tensor_expand_shape %[[ARG0]]
// CHECK-SAME: [0, 1, 2], [3]
// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x5x?xf32>
// CHECK: %[[T1:.+]] = linalg.tensor_expand_shape %[[ARG1]]
// CHECK-SAME: [0, 1, 2], [3]
// CHECK-SAME: tensor<?x?xf32> into tensor<?x4x5x?xf32>
// CHECK: %[[T3:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP4]], #[[MAP4]], #[[MAP5]]]
// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[T0]], %[[T1]] : tensor<?x4x5x?xf32>, tensor<?x4x5x?xf32>)
// CHECK-SAME: outs(%{{.+}} : tensor<?x?x4x5xf32>)
// CHECK: return %[[T3]] : tensor<?x?x4x5xf32>
// -----
func @unit_dim_reshape_expansion(%arg0 : tensor<1x5xf32>) -> tensor<5x5xf32> {
%0 = linalg.tensor_collapse_shape %arg0 [[0, 1]]
: tensor<1x5xf32> into tensor<5xf32>
%1 = linalg.init_tensor [5, 5] : tensor<5x5xf32>
%2 = linalg.generic
{indexing_maps = [affine_map<(d0, d1) -> (d0)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%0 : tensor<5xf32>) outs(%1 : tensor<5x5xf32>) {
^bb0(%arg2: f32, %arg3: f32): // no predecessors
linalg.yield %arg2 : f32
} -> tensor<5x5xf32>
return %2 : tensor<5x5xf32>
}
// CHECK: func @unit_dim_reshape_expansion
// CHECK-DAG: linalg.tensor_collapse_shape
// CHECK-DAG: linalg.init_tensor
// CHECK: linalg.generic
// -----
func @unit_dim_reshape_collapse(%arg0 : tensor<5xf32>) -> tensor<5x1x5xf32> {
%0 = linalg.init_tensor [5, 5] : tensor<5x5xf32>
%1 = linalg.generic
{indexing_maps = [affine_map<(d0, d1) -> (d0)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg0 : tensor<5xf32>) outs(%0 : tensor<5x5xf32>) {
^bb0(%arg2: f32, %arg3: f32): // no predecessors
linalg.yield %arg2 : f32
} -> tensor<5x5xf32>
%2 = linalg.tensor_expand_shape %1 [[0, 1], [2]]
: tensor<5x5xf32> into tensor<5x1x5xf32>
return %2 : tensor<5x1x5xf32>
}
// CHECK: func @unit_dim_reshape_collapse
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK: linalg.tensor_expand_shape
// -----
func @unit_dim_reshape_expansion_full
(%arg0 : tensor<1x?x1x2x1x4xf32>, %arg1 : tensor<?x2x4xf32>)
-> tensor<?x2x4xf32> {
%c1 = constant 1 : index
%0 = linalg.tensor_collapse_shape %arg0 [[0, 1, 2], [3, 4], [5]]
: tensor<1x?x1x2x1x4xf32> into tensor<?x2x4xf32>
%1 = tensor.dim %arg0, %c1 : tensor<1x?x1x2x1x4xf32>
%2 = linalg.init_tensor [%1, 2, 4] : tensor<?x2x4xf32>
%3 = linalg.generic
{indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0, %arg1 : tensor<?x2x4xf32>, tensor<?x2x4xf32>)
outs(%2 : tensor<?x2x4xf32>) {
^bb0(%arg2: f32, %arg3: f32, %arg4: f32): // no predecessors
%4 = mulf %arg2, %arg3 : f32
linalg.yield %4 : f32
} -> tensor<?x2x4xf32>
return %3 : tensor<?x2x4xf32>
}
// CHECK: func @unit_dim_reshape_expansion_full
// CHECK-DAG: linalg.tensor_collapse_shape
// CHECK-DAG: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<?x2x4xf32>, tensor<?x2x4xf32>)
// FOLDUNITDIM: func @unit_dim_reshape_expansion_full
// FOLDUNITDIM-SAME: %[[ARG0:.+]]: tensor<1x?x1x2x1x4xf32>
// FOLDUNITDIM-SAME: %[[ARG1:.+]]: tensor<?x2x4xf32>
// FOLDUNITDIM-DAG: %[[RESHAPE:.+]] = linalg.tensor_expand_shape %[[ARG1]]
// FOLDUNITDIM: linalg.generic
// FOLDUNITDIM-SAME: ins(%[[ARG0]], %[[RESHAPE]] : tensor<1x?x1x2x1x4xf32>, tensor<1x?x1x2x1x4xf32>)
// FOLDUNITDIM-SAME: outs(%{{.+}} : tensor<1x?x1x2x1x4xf32>)
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