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// RUN: mlir-opt -split-input-file -linalg-fold-reshape-ops-by-linearization %s | FileCheck %s
#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
func @generic_op_reshape_producer_fusion(%arg0 : tensor<?x?x?xi32>)
-> tensor<?x?x4x?xi32> {
%0 = linalg.tensor_expand_shape %arg0 [[0], [1, 2], [3]] :
tensor<?x?x?xi32> into tensor<?x?x4x?xi32>
%1 = linalg.generic {
indexing_maps = [#map0, #map0],
iterator_types = ["parallel", "parallel", "parallel", "parallel"] }
ins(%0 : tensor<?x?x4x?xi32>)
outs(%0 : tensor<?x?x4x?xi32>) {
^bb0(%arg6: i32, %arg7 : i32): // no predecessors
%idx = linalg.index 0 : index
%2 = index_cast %idx : index to i32
%3 = addi %arg6, %2 : i32
linalg.yield %3 : i32
} -> tensor<?x?x4x?xi32>
return %1 : tensor<?x?x4x?xi32>
}
// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1 * 4 + d2, d3)>
// CHECK-DAG: #[[MAP4:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK: func @generic_op_reshape_producer_fusion
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?xi32>
// CHECK: %[[T0:.+]] = linalg.tensor_expand_shape %[[ARG0]]
// CHECK-SAME: [0], [1, 2], [3]
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP3]], #[[MAP4]]]
// CHECK-SAME: ins(%[[ARG0]] : tensor<?x?x?xi32>)
// CHECK-SAME: outs(%[[T0]] : tensor<?x?x4x?xi32>)
// CHECK: %[[IDX:.+]] = linalg.index 0 : index
// CHECK-NEXT: %[[IDX_CASTED:.+]] = index_cast %[[IDX]] : index to i32
// -----
#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
func @generic_op_reshape_consumer_fusion(%arg0 : tensor<?x?x4x5xi32>)
-> tensor<?x?xi32> {
%0 = linalg.generic {
indexing_maps = [#map0, #map0],
iterator_types = ["parallel", "parallel", "parallel", "parallel"] }
ins(%arg0 : tensor<?x?x4x5xi32>) outs(%arg0 : tensor<?x?x4x5xi32>) {
^bb0(%arg6: i32, %arg7: i32): // no predecessors
%idx = linalg.index 0 : index
%2 = index_cast %idx : index to i32
%3 = addi %arg6, %2 : i32
linalg.yield %3 : i32
} -> tensor<?x?x4x5xi32>
%1 = linalg.tensor_collapse_shape %0 [[0], [1, 2, 3]] :
tensor<?x?x4x5xi32> into tensor<?x?xi32>
return %1 : tensor<?x?xi32>
}
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1 * 20 + d2 * 5 + d3)>
// CHECK: func @generic_op_reshape_consumer_fusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x4x5xi32>
// CHECK: %[[T0:.+]] = linalg.tensor_collapse_shape %[[ARG0]]
// CHECK-SAME: [0], [1, 2, 3]
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP3]]]
// CHECK-SAME: outs(%[[T0]] : tensor<?x?xi32>)
// CHECK: %[[IDX:.+]] = linalg.index 0 : index
// CHECK-NEXT: %[[IDX_CASTED:.+]] = index_cast %[[IDX]] : index to i32
// CHECK-NOT: linalg.tensor_collapse_shape
// -----
#map2 = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
#map3 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
func @generic_op_021_permultation_reshape_producer_fusion(%arg0 : tensor<3x35xf32>) -> tensor<3x7x5xf32> {
%0 = linalg.tensor_expand_shape %arg0 [[0], [1, 2]]
: tensor<3x35xf32> into tensor<3x5x7xf32>
%1 = linalg.init_tensor [3, 7, 5] : tensor<3x7x5xf32>
%2 = linalg.generic
{indexing_maps = [#map2, #map3],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0 : tensor<3x5x7xf32>) outs(%1 : tensor<3x7x5xf32>) {
^bb0(%arg2: f32, %arg3 : f32): // no predecessors
linalg.yield %arg2 : f32
} -> tensor<3x7x5xf32>
return %2 : tensor<3x7x5xf32>
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0, d1 + d2 * 7)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generic_op_021_permultation_reshape_producer_fusion
// CHECK-NOT: linalg.tensor_expand_shape
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// -----
#map2 = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
#map3 = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
func @generic_op_120_permutation_reshape_producer_fusion(%arg0 : tensor<3x35xf32>) -> tensor<5x7x3xf32> {
%0 = linalg.tensor_expand_shape %arg0 [[0], [1, 2]]
: tensor<3x35xf32> into tensor<3x5x7xf32>
%1 = linalg.init_tensor [5, 7, 3] : tensor<5x7x3xf32>
%2 = linalg.generic
{indexing_maps = [#map2, #map3],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0 : tensor<3x5x7xf32>) outs(%1 : tensor<5x7x3xf32>) {
^bb0(%arg2: f32, %arg3: f32): // no predecessors
linalg.yield %arg2 : f32
} -> tensor<5x7x3xf32>
return %2 : tensor<5x7x3xf32>
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d1, d0 * 7 + d2)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
// CHECK: func @generic_op_120_permutation_reshape_producer_fusion
// CHECK-NOT: linalg.tensor_expand_shape
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// -----
#map0 = affine_map<(d0, d1, d2) -> (d0)>
#map1 = affine_map<(d0, d1, d2) -> (d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
#map3 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
func @generic_op_102_permultation_reshape_producer_fusion(%arg0 : tensor<3x35xf32>) -> tensor<5x3x7xf32> {
%0 = linalg.tensor_expand_shape %arg0 [[0], [1, 2]]
: tensor<3x35xf32> into tensor<3x5x7xf32>
%1 = linalg.init_tensor [5, 3, 7] : tensor<5x3x7xf32>
%2 = linalg.generic
{indexing_maps = [#map2, #map3],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0 : tensor<3x5x7xf32>) outs(%1 : tensor<5x3x7xf32>) {
^bb0(%arg2: f32, %arg3: f32): // no predecessors
linalg.yield %arg2 : f32
} -> tensor<5x3x7xf32>
return %2 : tensor<5x3x7xf32>
}
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d1, d0 * 7 + d2)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK: func @generic_op_102_permultation_reshape_producer_fusion
// CHECK-NOT: linalg.tensor_expand_shape
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// -----
#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0)>
#map3 = affine_map<(d0, d1, d2) -> (d1, d2)>
func @generic_op_102_permultation_reshape_consumer_fusion(%arg0 : tensor<3x5x7xf32>) -> tensor<5x21xf32> {
%0 = linalg.init_tensor [5, 3, 7] : tensor<5x3x7xf32>
%1 = linalg.generic
{indexing_maps = [#map0, #map1],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0 : tensor<3x5x7xf32>) outs(%0 : tensor<5x3x7xf32>) {
^bb0(%arg2: f32, %arg3 : f32): // no predecessors
linalg.yield %arg2 : f32
} -> tensor<5x3x7xf32>
%2 = linalg.tensor_collapse_shape %1 [[0], [1, 2]]
: tensor<5x3x7xf32> into tensor<5x21xf32>
return %2 : tensor<5x21xf32>
}
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0, d1, d2) -> (d1, d0 * 7 + d2)>
// CHECK: func @generic_op_102_permultation_reshape_consumer_fusion
// CHECK-SAME: %[[ARG0:.+]]: tensor<3x5x7xf32>
// CHECK: %[[T0:.+]] = linalg.init_tensor [5, 3, 7]
// CHECK: %[[T1:.+]] = linalg.tensor_collapse_shape %[[T0]]
// CHECK-SAME: [0], [1, 2]
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP2]], #[[MAP3]]]
// CHECK-SAME: ins(%[[ARG0]] : tensor<3x5x7xf32>)
// CHECK-SAME: outs(%[[T1]] : tensor<5x21xf32>)
// -----
#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
func @generic_op_reshape_consumer_nofusion(%arg0 : tensor<?x?x?x5xf32>,
%arg1 : tensor<?x?x?x5xf32>) ->
tensor<?x?xf32>
{
%0 = linalg.generic {
indexing_maps = [#map0, #map0, #map0],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
ins(%arg0, %arg1 : tensor<?x?x?x5xf32>, tensor<?x?x?x5xf32>)
outs(%arg0 : tensor<?x?x?x5xf32>) {
^bb0(%arg3: f32, %arg4: f32, %arg5: f32): // no predecessors
%1 = mulf %arg3, %arg4 : f32
linalg.yield %1 : f32
} -> tensor<?x?x?x5xf32>
%1 = linalg.tensor_collapse_shape %0 [[0], [1, 2, 3]] :
tensor<?x?x?x5xf32> into tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
// CHECK-LABEL: func @generic_op_reshape_consumer_nofusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x?x5xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?x?x5xf32>
// CHECK: %[[NOFUSE:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]]
// CHECK: %[[RESULT:.+]] = linalg.tensor_collapse_shape %[[NOFUSE]]
// CHECK: return %[[RESULT]]
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