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// RUN: mlir-opt -linalg-bufferize -canonicalize -cse -split-input-file %s | FileCheck %s
#map0 = affine_map<(d0) -> (d0)>
// In-depth checking of a basic case, this is testing
// - bufferization.to_memref / bufferization.to_tensor materializations are
// properly inserted
// - payload is correctly carried over
// - affine maps are correctly carried over
// Later tests will not check all these details.
// CHECK: #map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: func @basic(
// CHECK-SAME: %[[TENSOR:.*]]: tensor<4xf32>) -> tensor<4xf32> {
// CHECK-DAG: %[[MEMREF:.*]] = bufferization.to_memref %[[TENSOR]] : memref<4xf32>
// CHECK-DAG: %[[RESULT_MEMREF:.*]] = memref.alloc() {{.*}} : memref<4xf32>
// CHECK: linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]}
// CHECK-SAME: ins(%[[MEMREF]] : memref<4xf32>)
// CHECK-SAME: outs(%[[RESULT_MEMREF]] : memref<4xf32>) {
// CHECK: ^bb0(%[[RESULT1:.*]]: f32, %[[UNUSED:.*]]: f32):
// CHECK: %[[DIM1:.*]] = math.exp %[[RESULT1]] : f32
// CHECK: linalg.yield %[[DIM1]] : f32
// CHECK: }
// CHECK: %[[RESULT:.*]] = bufferization.to_tensor %[[RESULT_MEMREF]] : memref<4xf32>
// CHECK: return %[[RESULT]] : tensor<4xf32>
func.func @basic(%arg0: tensor<4xf32>) -> tensor<4xf32> {
%0 = linalg.generic {
indexing_maps = [#map0, #map0],
iterator_types = ["parallel"]
} ins(%arg0 : tensor<4xf32>)
outs(%arg0 : tensor<4xf32>) {
^bb0(%gen_arg1: f32, %out: f32):
%tmp1 = math.exp %gen_arg1 : f32
linalg.yield %tmp1 : f32
} -> tensor<4xf32>
return %0 : tensor<4xf32>
}
// -----
#map0 = affine_map<(d0) -> (d0)>
// Same as above but with tensor.empty op.
// CHECK: #map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: func @empty_tensor(
// CHECK-SAME: %[[IN:.*]]: tensor<?xf32>, %[[SIZE:.*]]: index)
// CHECK-DAG: %[[MEMREF:.*]] = bufferization.to_memref %[[IN]] : memref<?xf32>
// CHECK-DAG: %[[OUT_BUF:.*]] = memref.alloc(%[[SIZE]]) {{.*}} : memref<?xf32>
// CHECK: linalg.generic
// CHECK-SAME: ins(%[[MEMREF]] : memref<?xf32>)
// CHECK-SAME: outs(%[[OUT_BUF]] : memref<?xf32>) {
func.func @empty_tensor(%in : tensor<?xf32>, %size: index) -> tensor<?xf32> {
%init = tensor.empty(%size) : tensor<?xf32>
%0 = linalg.generic {
indexing_maps = [#map0, #map0],
iterator_types = ["parallel"]
} ins(%in : tensor<?xf32>)
outs(%init : tensor<?xf32>) {
^bb0(%gen_arg1: f32, %out: f32):
%tmp1 = math.exp %gen_arg1 : f32
linalg.yield %tmp1 : f32
} -> tensor<?xf32>
return %0 : tensor<?xf32>
}
// -----
#map0 = affine_map<(d0) -> (d0)>
// CHECK-LABEL: func @multiple_results
// CHECK: %[[RESULT0:.*]] = memref.alloc() {{.*}} : memref<4xf32>
// CHECK: %[[RESULT1:.*]] = memref.alloc() {{.*}} : memref<4xf32>
// CHECK: linalg.generic
// CHECK-SAME: ins(%{{.*}} : memref<4xf32>)
// CHECK-SAME: outs(%[[RESULT0]], %[[RESULT1]] : memref<4xf32>, memref<4xf32>)
// CHECK-NEXT: ^bb0(%{{.*}}: f32, %{{.*}}: f32, %{{.*}}: f32):
func.func @multiple_results(%arg0: tensor<4xf32>) -> (tensor<4xf32>, tensor<4xf32>) {
%0, %1 = linalg.generic {
indexing_maps = [#map0, #map0, #map0],
iterator_types = ["parallel"]
} ins(%arg0 : tensor<4xf32>)
outs (%arg0, %arg0 : tensor<4xf32>, tensor<4xf32>) {
^bb0(%gen_arg1: f32, %out1: f32, %out2: f32):
%tmp1 = math.exp %gen_arg1 : f32
linalg.yield %tmp1, %tmp1 : f32, f32
} -> (tensor<4xf32>, tensor<4xf32>)
return %0, %1 : tensor<4xf32>, tensor<4xf32>
}
// -----
#map_2d = affine_map<(d0, d1) -> (d0, d1)>
// Check that the allocs properly consider the different shapes of the output
// operands. The permuted indexing maps translate to different output shapes.
// CHECK-LABEL: func @dynamic_results(
// CHECK-SAME: %[[ARG:.*]]: tensor<?x?xf32>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[DIM0:.*]] = tensor.dim %[[ARG]], %[[C0]] : tensor<?x?xf32>
// CHECK-DAG: %[[DIM1:.*]] = tensor.dim %[[ARG]], %[[C1]] : tensor<?x?xf32>
// CHECK-DAG: %[[RESULT0:.*]] = memref.alloc(%[[DIM0]], %[[DIM1]]) {{.*}} : memref<?x?xf32>
// CHECK-DAG: %[[RESULT1:.*]] = memref.alloc(%[[DIM0]], %[[DIM1]]) {{.*}} : memref<?x?xf32>
// CHECK-DAG: %[[MEMREF_ARG:.*]] = bufferization.to_memref %[[ARG]] : memref<?x?xf32>
// CHECK: linalg.generic
// CHECK-SAME: ins(%[[MEMREF_ARG]] : memref<?x?xf32>)
// CHECK-SAME: outs(%[[RESULT0]], %[[RESULT1]] : memref<?x?xf32>, memref<?x?xf32>)
func.func @dynamic_results(%arg0: tensor<?x?xf32>)
-> (tensor<?x?xf32>, tensor<?x?xf32>) {
%0, %1 = linalg.generic {
indexing_maps = [#map_2d, #map_2d, #map_2d],
iterator_types = ["parallel", "parallel"]
} ins(%arg0 : tensor<?x?xf32>)
outs (%arg0, %arg0 : tensor<?x?xf32>, tensor<?x?xf32>) {
^bb0(%gen_arg1: f32, %out1: f32, %out2: f32):
%tmp1 = math.exp %gen_arg1 : f32
linalg.yield %tmp1, %tmp1 : f32, f32
} -> (tensor<?x?xf32>, tensor<?x?xf32>)
return %0, %1 : tensor<?x?xf32>, tensor<?x?xf32>
}
// -----
#accesses = [
affine_map<(i, j, k) -> (j, i, k)>,
affine_map<(i, j, k) -> (i, j)>
]
#trait = {
indexing_maps = #accesses,
iterator_types = ["parallel", "parallel", "reduction"]
}
// Check the bufferization of init tensors.
// CHECK-LABEL: func @generic_with_init_tensor(
// CHECK-SAME: %[[ARG0_TENSOR:.*]]: tensor<2x3x4xvector<3x4xi4>>,
// CHECK-SAME: %[[ARG1_TENSOR:.*]]: tensor<3x2xf32>) -> tensor<3x2xf32> {
// CHECK-DAG: %[[INIT_BUFFER:.*]] = memref.alloc() {{.*}} : memref<3x2xf32>
// CHECK-DAG: %[[ARG0_MEMREF:.*]] = bufferization.to_memref %[[ARG0_TENSOR]] : memref<2x3x4xvector<3x4xi4>>
// CHECK-DAG: %[[ARG1_MEMREF:.*]] = bufferization.to_memref %[[ARG1_TENSOR]] : memref<3x2xf32>
// CHECK: memref.copy %[[ARG1_MEMREF]], %[[INIT_BUFFER]] : memref<3x2xf32> to memref<3x2xf32>
// CHECK: linalg.generic
// CHECK-SAME: ins(%[[ARG0_MEMREF]] : memref<2x3x4xvector<3x4xi4>>)
// CHECK-SAME: outs(%[[INIT_BUFFER]] : memref<3x2xf32>) {
func.func @generic_with_init_tensor(%arg0: tensor<2x3x4xvector<3x4xi4>>,
%arg1: tensor<3x2xf32>) -> (tensor<3x2xf32>) {
%0 = linalg.generic #trait
ins(%arg0 : tensor<2x3x4xvector<3x4xi4>>)
outs(%arg1 : tensor<3x2xf32>) {
^bb(%v0: vector<3x4xi4>, %v1: f32) :
linalg.yield %v1 : f32
} -> tensor<3x2xf32>
return %0 : tensor<3x2xf32>
}
// -----
// CHECK-LABEL: func @bufferize_fill(
// CHECK-SAME: %[[IN:.*]]: tensor<?xf32>
func.func @bufferize_fill(%arg0: tensor<?xf32>) -> tensor<?xf32> {
%c0 = arith.constant 0.0 : f32
// CHECK: %[[ALLOC:.*]] = memref.alloc
// CHECK: linalg.fill ins(%cst : f32) outs(%[[ALLOC]] : memref<?xf32>)
// CHECK: %[[TENSOR:.*]] = bufferization.to_tensor %[[ALLOC]] : memref<?xf32>
// CHECK: return %[[TENSOR]]
%0 = linalg.fill ins(%c0 : f32) outs(%arg0 : tensor<?xf32>) -> tensor<?xf32>
return %0 : tensor<?xf32>
}
// -----
// CHECK-LABEL: func @bufferize_dot
func.func @bufferize_dot(%in: tensor<4xf32>, %out: tensor<f32>) -> tensor<f32> {
%dot = linalg.dot ins(%in, %in : tensor<4xf32>, tensor<4xf32>)
outs(%out : tensor<f32>) -> tensor<f32>
return %dot : tensor<f32>
// CHECK: %[[ALLOC:.*]] = memref.alloc
// TODO: The copy is not necessary.
// CHECK: memref.copy {{.*}}, %[[ALLOC]]
// CHECK: linalg.dot ins(%{{.*}}, %{{.*}} : memref<4xf32>, memref<4xf32>)
// CHECK-SAME: outs(%[[ALLOC:.*]] : memref<f32>)
// CHECK: %[[OUT_TENSOR:.*]] = bufferization.to_tensor %[[ALLOC]] : memref<f32>
// CHECK: return %[[OUT_TENSOR]]
}
// -----
// This is a regression test. The linalg-bufferize pass should ignore all func
// dialect ops.
// CHECK-LABEL: func private @csum(tensor<6xi64>) -> tensor<6xi64>
func.func private @csum(%arg0: tensor<6xi64>) -> tensor<6xi64>
// CHECK: func public @main(%[[arg0:.*]]: tensor<2x3xi1>)
// CHECK: %[[collapse:.*]] = tensor.collapse_shape %[[arg0]]
// CHECK: %[[collapse_m:.*]] = bufferization.to_memref %[[collapse]]
// CHECK: %[[alloc:.*]] = memref.alloc()
// CHECK: linalg.generic {{.*}} ins(%[[collapse_m]] : memref<6xi1>) outs(%[[alloc]] : memref<6xi64>)
// CHECK: %[[generic_t:.*]] = bufferization.to_tensor %[[alloc]]
// CHECK: %[[call:.*]] = call @csum(%[[generic_t]])
// CHECK: return %[[call]]
func.func public @main(%arg0: tensor<2x3xi1>) -> tensor<6xi64> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<2x3xi1> into tensor<6xi1>
%1 = tensor.empty() : tensor<6xi64>
%2 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%0 : tensor<6xi1>) outs(%1 : tensor<6xi64>) {
^bb0(%arg1: i1, %arg2: i64):
%4 = arith.extui %arg1 : i1 to i64
linalg.yield %4 : i64
} -> tensor<6xi64>
%3 = func.call @csum(%2) : (tensor<6xi64>) -> tensor<6xi64>
return %3 : tensor<6xi64>
}
|
