// RUN: mlir-opt %s | mlir-opt | FileCheck %s
// RUN: mlir-opt %s --mlir-print-op-generic | mlir-opt | FileCheck %s
// TODO: Re-enable LLVM lowering test.
//
// Test that we can lower all the way to LLVM without crashing, don't check results here.
// DISABLED: mlir-opt %s -o=/dev/null 2>&1
func.func @views(%arg0: index) {
%c0 = arith.constant 0 : index
%0 = arith.muli %arg0, %arg0 : index
%1 = memref.alloc (%0) : memref<?xi8>
%3 = memref.view %1[%c0][%arg0, %arg0] : memref<?xi8> to memref<?x?xf32>
%4 = memref.view %1[%c0][%arg0, %arg0] : memref<?xi8> to memref<?x?xvector<4x4xf32>>
memref.dealloc %1 : memref<?xi8>
return
}
// CHECK-LABEL: func @views
// CHECK: arith.muli %{{.*}}, %{{.*}} : index
// CHECK-NEXT: memref.alloc(%{{.*}}) : memref<?xi8>
// CHECK-NEXT: memref.view %{{.*}}[%{{.*}}][%{{.*}}] :
// CHECK-SAME: memref<?xi8> to memref<?x?xf32>
// CHECK-NEXT: memref.view %{{.*}}[%{{.*}}][%{{.*}}] :
// CHECK-SAME: memref<?xi8> to memref<?x?xvector<4x4xf32>>
// CHECK-NEXT: memref.dealloc %{{.*}} : memref<?xi8>
// -----
func.func @ops(%arg0: memref<?x?xf32, strided<[?, 1], offset: ?>>,
%arg1: memref<?xf32, strided<[1], offset: ?>>,
%arg2: memref<?xf32, strided<[1], offset: ?>>,
%arg3: memref<f32>) {
linalg.matmul ins(%arg0, %arg0 : memref<?x?xf32, strided<[?, 1], offset: ?>>,
memref<?x?xf32, strided<[?, 1], offset: ?>>)
outs(%arg0 : memref<?x?xf32, strided<[?, 1], offset: ?>>)
linalg.matvec ins(%arg0, %arg1: memref<?x?xf32, strided<[?, 1], offset: ?>>,
memref<?xf32, strided<[1], offset: ?>>)
outs(%arg2: memref<?xf32, strided<[1], offset: ?>>)
linalg.dot ins(%arg1, %arg2: memref<?xf32, strided<[1], offset: ?>>,
memref<?xf32, strided<[1], offset: ?>>)
outs(%arg3: memref<f32>)
return
}
// CHECK-LABEL: func @ops(%
// CHECK: linalg.matmul
// CHECK-SAME: ins(%{{.*}}, %{{.*}} : memref<?x?xf32, strided<[?, 1], offset: ?>>,
// CHECK-SAME: memref<?x?xf32, strided<[?, 1], offset: ?>>)
// CHECK-SAME: outs(%{{.*}} : memref<?x?xf32, strided<[?, 1], offset: ?>>)
// CHECK: linalg.matvec
// CHECK-SAME: ins(%{{.*}}, %{{.*}}: memref<?x?xf32, strided<[?, 1], offset: ?>>,
// CHECK-SAME: memref<?xf32, strided<[1], offset: ?>>)
// CHECK-SAME: outs(%{{.*}}: memref<?xf32, strided<[1], offset: ?>>)
// CHECK: linalg.dot
// CHECK-SAME: ins(%{{.*}}, %{{.*}}: memref<?xf32, strided<[1], offset: ?>>,
// CHECK-SAME: memref<?xf32, strided<[1], offset: ?>>)
// CHECK-SAME: outs(%{{.*}}: memref<f32>)
// -----
func.func @fill_view(%arg0: memref<?xf32, strided<[1], offset: ?>>, %arg1: f32) {
linalg.fill ins(%arg1 : f32) outs(%arg0 : memref<?xf32, strided<[1], offset: ?>>)
return
}
// CHECK-LABEL: func @fill_view(
// CHECK: %{{.*}}: memref<?xf32, strided<[1], offset: ?>>, %{{.*}}: f32) {
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%{{.*}} : memref<?xf32, strided<[1], offset: ?>>)
// -----
func.func @memref_transpose(%arg0: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>) {
%0 = memref.transpose %arg0 (i, j, k) -> (k, j, i) : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>> to memref<?x?x?xf32, strided<[1, ?, ?], offset: ?>>
return
}
// CHECK-LABEL: func @memref_transpose
// CHECK: memref.transpose %{{.*}} ([[i:.*]], [[j:.*]], [[k:.*]]) -> ([[k]], [[j]], [[i]]) :
// CHECK-SAME: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>> to memref<?x?x?xf32, strided<[1, ?, ?], offset: ?>>
// -----
func.func @fill_view3(%arg0: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>, %arg1: f32) {
linalg.fill ins(%arg1 : f32) outs(%arg0 : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>)
return
}
// CHECK-LABEL: func @fill_view3(
// CHECK: %{{.*}}: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>, %{{.*}}: f32) {
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%{{.*}} : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>)
// -----
#accesses_0 = [
affine_map<(i, j, k) -> (j, i)>,
affine_map<(i, j, k) -> ()>,
affine_map<(i, j, k) -> (i, k, i + j)>
]
#trait_0 = {
indexing_maps = #accesses_0,
iterator_types = ["parallel", "parallel", "parallel"],
library_call = "some_external_function_name_1"
}
func.func @generic(%arg0: memref<?x?xvector<3x4xi4>, strided<[?, 1], offset: ?>>,
%arg1: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>) {
%cst = arith.constant 0.0 : f32
linalg.generic #trait_0
ins(%arg0, %cst : memref<?x?xvector<3x4xi4>, strided<[?, 1], offset: ?>>, f32)
outs(%arg1 : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>)
attrs = {foo = 1} {
^bb(%0: vector<3x4xi4>, %1: f32, %2: f32) :
linalg.yield %1 : f32
}
return
}
// CHECK-LABEL: func @generic
// CHECK: linalg.generic {
// CHECK-SAME: indexing_maps = [#{{[0-9a-z]*}}, #{{[0-9a-z]*}}, #{{[0-9a-z]*}}],
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"],
// CHECK-SAME: library_call = "some_external_function_name_1"}
// CHECK-SAME: ins({{.*}}, {{.*}} : memref<?x?xvector<3x4xi4>, strided<[?, 1], offset: ?>>, f32)
// CHECK-SAME: outs({{.*}} : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>)
// CHECK-SAME: {foo = 1 : i64}
// -----
#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
func.func @generic_without_inputs(%arg0 : memref<?x?x?xf32>) {
linalg.generic {indexing_maps = [#map0],
iterator_types = ["parallel", "parallel", "parallel"]}
outs(%arg0 : memref<?x?x?xf32>) {
^bb0(%arg3: f32):
%cst = arith.constant 0.000000e+00 : f32
linalg.yield %cst : f32
}
return
}
// CHECK-LABEL: func @generic_without_inputs
// CHECK: linalg.generic
// CHECK-NOT: ins
// -----
#accesses_1 = [
affine_map<(i, j, k) -> (j, i)>,
affine_map<(i, j, k) -> (i, k, i + j)>,
affine_map<(i, j, k) -> (i, k, i + j)>
]
#trait_1 = {
indexing_maps = #accesses_1,
iterator_types = ["parallel", "parallel", "parallel"],
library_call = "some_external_function_name_1"
}
func.func @generic_with_tensor_input_and_output(
%arg0: tensor<?x?xvector<3x4xi4>>, %arg1: tensor<?x?x?xf32>)
-> (tensor<?x?x?xf32>) {
%0 = linalg.generic #trait_1
ins(%arg0, %arg1 : tensor<?x?xvector<3x4xi4>>, tensor<?x?x?xf32>)
outs(%arg1 : tensor<?x?x?xf32>)
attrs = {foo = 1} {
^bb(%0: vector<3x4xi4>, %1: f32, %2: f32) :
%f0 = arith.constant 0.0 : f32
linalg.yield %f0 : f32
} -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
// CHECK-LABEL: func @generic_with_tensor_input_and_output
// CHECK: linalg.generic {
// CHECK-SAME: indexing_maps = [#{{.*}}, #{{.*}}], iterator_types = ["parallel", "parallel", "parallel"],
// CHECK-SAME: library_call = "some_external_function_name_1"}
// CHECK-SAME: ins({{.*}} : tensor<?x?xvector<3x4xi4>>, tensor<?x?x?xf32>)
// CHECK-SAME: outs({{.*}} : tensor<?x?x?xf32>)
// CHECK-SAME: {foo = 1 : i64}
// CHECK: -> tensor<?x?x?xf32>
// CHECK: return {{.*}} : tensor<?x?x?xf32>
// -----
func.func @generic_with_multiple_tensor_outputs(
%arg0: tensor<?xi32>, %arg1: tensor<?xi32>, %arg2: i32)
-> (tensor<i32>, tensor<i32>) {
%c0 = arith.constant 0 : index
%0 = tensor.empty() : tensor<i32>
%1 = linalg.fill ins(%arg2 : i32) outs(%0 : tensor<i32>) -> tensor<i32>
%2 = tensor.empty() : tensor<i32>
%3 = linalg.fill ins(%arg2 : i32) outs(%2 : tensor<i32>) -> tensor<i32>
%4:2 = linalg.generic {
indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>, affine_map<(d0) -> ()>, affine_map<(d0) -> ()>],
iterator_types = ["reduction"]}
ins(%arg0, %arg1 : tensor<?xi32>, tensor<?xi32>)
outs(%1, %3 : tensor<i32>, tensor<i32>) {
^bb0(%arg3: i32, %arg4: i32, %arg5: i32, %arg6: i32):
%5 = arith.cmpi sge, %arg3, %arg5 : i32
%6 = arith.select %5, %arg3, %arg5 : i32
%7 = arith.cmpi eq, %arg3, %arg5 : i32
%8 = arith.cmpi slt, %arg4, %arg6 : i32
%9 = arith.select %8, %arg4, %arg6 : i32
%10 = arith.select %5, %arg4, %arg6 : i32
%11 = arith.select %7, %9, %10 : i32
linalg.yield %6, %11 : i32, i32
} -> (tensor<i32>, tensor<i32>)
return %4#0, %4#1 : tensor<i32>, tensor<i32>
}
// CHECK-LABEL: func @generic_with_multiple_tensor_outputs
// CHECK: %{{.*}} = linalg.generic {
// CHECK-SAME: ins({{.*}} : tensor<?xi32>, tensor<?xi32>)
// CHECK-SAME: outs({{.*}} : tensor<i32>, tensor<i32>)
// CHECK: } -> (tensor<i32>, tensor<i32>)
// -----
#broadcast_access = [
affine_map<(i, j) -> ()>,
affine_map<(i, j) -> (i, j)>
]
#trait_broadcast = {
indexing_maps = #broadcast_access,
iterator_types = ["parallel", "parallel"],
library_call = "some_broadcast_external_fn"
}
func.func @generic_op_zero_rank(%arg0: tensor<f32>, %arg1 : tensor<3x4xf32>) -> (tensor<3x4xf32>)
{
%0 = linalg.generic #trait_broadcast
ins(%arg0 : tensor<f32>)
outs(%arg1 : tensor<3x4xf32>) {
^bb(%a: f32, %b: f32) :
linalg.yield %a : f32
} -> tensor<3x4xf32>
return %0 : tensor<3x4xf32>
}
// -----
#accesses_3 = [
affine_map<(i, j, k) -> (j, i)>,
affine_map<(i, j, k) -> (i, k, i + j)>
]
#trait_3 = {
indexing_maps = #accesses_3,
iterator_types = ["parallel", "parallel", "parallel"],
library_call = "some_external_function_name_2"
}
func.func @generic_region(%arg0: memref<?x?xvector<3x4xi4>, strided<[?, 1], offset: ?>>,
%arg1: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>) {
linalg.generic #trait_3
ins(%arg0 : memref<?x?xvector<3x4xi4>, strided<[?, 1], offset: ?>>)
outs(%arg1 : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>)
attrs = {foo = 1} {
^bb(%a: vector<3x4xi4>, %b: f32) :
%0 = linalg.index 0 : index
%1 = linalg.index 1 : index
%2 = linalg.index 2 : index
linalg.yield %b : f32
}
return
}
// CHECK-LABEL: func @generic_region
// CHECK: linalg.generic {
// CHECK-SAME: indexing_maps = [#{{[0-9a-z]*}}, #{{[0-9a-z]*}}],
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"],
// CHECK-SAME: library_call = "some_external_function_name_2"
// CHECK-SAME: ins({{.*}} : memref<?x?xvector<3x4xi4>, strided<[?, 1], offset: ?>>)
// CHECK-SAME: outs({{.*}} : memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>)
// CHECK-SAME: attrs = {foo = 1 : i64} {
// CHECK: ^{{.*}}(%{{.*}}: vector<3x4xi4>, %{{.*}}: f32):
// CHECK: %{{.*}} = linalg.index 0 : index
// CHECK: %{{.*}} = linalg.index 1 : index
// CHECK: %{{.*}} = linalg.index 2 : index
// CHECK: linalg.yield %{{.*}} : f32
// -----
func.func @named_ops(%a3: memref<?x?x?xf32>, %b3: memref<?x?x?xf32>, %c3: memref<?x?x?xf32>,
%ta3: tensor<?x?x?xf32>, %tb3: tensor<?x?x?xf32>, %tc3: tensor<?x?x?xf32>)
-> (tensor<?x?x?xf32>)
{
linalg.batch_matmul ins(%a3, %b3: memref<?x?x?xf32>, memref<?x?x?xf32>)
outs(%c3: memref<?x?x?xf32>)
%res1 = linalg.batch_matmul
ins(%ta3, %tb3: tensor<?x?x?xf32>, tensor<?x?x?xf32>)
outs(%tc3: tensor<?x?x?xf32>)
-> tensor<?x?x?xf32>
return %res1 : tensor<?x?x?xf32>
}
// CHECK-LABEL: func @named_ops
// CHECK: linalg.batch_matmul
// CHECK: linalg.batch_matmul
// -----
func.func @fill_tensor(%arg0 : index, %arg1 : index, %arg2 : f32) -> tensor<?x?xf32> {
%0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf32>
%1 = linalg.fill ins(%arg2 : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
// CHECK: %{{.+}} = linalg.fill ins(%{{.+}} : f32) outs(%{{.+}} : tensor<?x?xf32>) -> tensor<?x?xf32>
// -----
func.func @mixed_parallel_reduced_results(%arg0 : tensor<?x?x?xf32>,
%arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?x?xf32>, %arg3 : tensor<?x?xf32>) ->
(tensor<?x?x?xf32>, tensor<?x?xf32>) {
%0:2 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>],
iterator_types = ["parallel", "parallel", "reduction"]}
ins(%arg0, %arg1 : tensor<?x?x?xf32>, tensor<?x?xf32>)
outs(%arg2, %arg3 : tensor<?x?x?xf32>, tensor<?x?xf32>) {
^bb0(%b0 : f32, %b1 : f32, %b2 : f32, %b3 : f32):
%1 = arith.mulf %b0, %b1 : f32
%2 = arith.addf %1, %b3 : f32
linalg.yield %1, %2 : f32, f32
} -> (tensor<?x?x?xf32>, tensor<?x?xf32>)
return %0#0, %0#1 : tensor<?x?x?xf32>, tensor<?x?xf32>
}
// CHECK-LABEL: func @mixed_parallel_reduced_results
// CHECK: linalg.generic
// -----
func.func @map_no_inputs(%init: tensor<64xf32>) -> tensor<64xf32> {
%add = linalg.map
outs(%init:tensor<64xf32>)
() {
%0 = arith.constant 0.0: f32
linalg.yield %0: f32
}
func.return %add : tensor<64xf32>
}
// CHECK-LABEL: func @map_no_inputs
// CHECK: linalg.map outs
// CHECK-NEXT: () {
// CHECK-NEXT: arith.constant
// CHECK-NEXT: linalg.yield
// CHECK-NEXT: }
// -----
func.func @map_binary(%lhs: tensor<64xf32>, %rhs: tensor<64xf32>,
%init: tensor<64xf32>) -> tensor<64xf32> {
%add = linalg.map
ins(%lhs, %rhs: tensor<64xf32>, tensor<64xf32>)
outs(%init:tensor<64xf32>)
(%lhs_elem: f32, %rhs_elem: f32) {
%0 = arith.addf %lhs_elem, %rhs_elem: f32
linalg.yield %0: f32
}
func.return %add : tensor<64xf32>
}
// CHECK-LABEL: func @map_binary
// CHECK: linalg.map { arith.addf } ins
// CHECK-SAME: outs
// -----
func.func @map_binary_memref(%lhs: memref<64xf32>, %rhs: memref<64xf32>,
%init: memref<64xf32>) {
linalg.map
ins(%lhs, %rhs: memref<64xf32>, memref<64xf32>)
outs(%init:memref<64xf32>)
(%lhs_elem: f32, %rhs_elem: f32) {
%0 = arith.addf %lhs_elem, %rhs_elem: f32
linalg.yield %0: f32
}
func.return
}
// CHECK-LABEL: func @map_binary_memref
// CHECK: linalg.map
// -----
func.func @map_unary(%input: tensor<64xf32>, %init: tensor<64xf32>) -> tensor<64xf32> {
%abs = linalg.map
ins(%input:tensor<64xf32>)
outs(%init:tensor<64xf32>)
(%input_elem: f32) {
%0 = math.absf %input_elem: f32
linalg.yield %0: f32
}
func.return %abs : tensor<64xf32>
}
// CHECK-LABEL: func @map_unary
// CHECK: linalg.map
// -----
func.func @map_unary_memref(%input: memref<64xf32>, %init: memref<64xf32>) {
linalg.map
ins(%input:memref<64xf32>)
outs(%init:memref<64xf32>)
(%input_elem: f32) {
%0 = math.absf %input_elem: f32
linalg.yield %0: f32
}
func.return
}
// CHECK-LABEL: func @map_unary_memref
// CHECK: linalg.map
// -----
func.func @reduce(%input: tensor<16x32x64xf32>,
%init: tensor<16x64xf32>) -> tensor<16x64xf32> {
%reduce = linalg.reduce
ins(%input:tensor<16x32x64xf32>)
outs(%init:tensor<16x64xf32>)
dimensions = [1]
(%in: f32, %out: f32) {
%0 = arith.addf %out, %in: f32
linalg.yield %0: f32
}
func.return %reduce : tensor<16x64xf32>
}
// CHECK-LABEL: func @reduce
// CHECK: linalg.reduce { arith.addf } ins
// CHECK-SAME: outs
// CHECK-SAME: dimensions = [1]
// -----
func.func @reduce_memref(%input: memref<16x32x64xf32>,
%init: memref<16x64xf32>) {
linalg.reduce
ins(%input:memref<16x32x64xf32>)
outs(%init:memref<16x64xf32>)
dimensions = [1]
(%in: f32, %out: f32) {
%0 = arith.addf %out, %in: f32
linalg.yield %0: f32
}
func.return
}
// CHECK-LABEL: func @reduce
// CHECK: linalg.reduce { arith.addf } ins
// CHECK-SAME: outs
// CHECK-SAME: dimensions = [1]
// -----
func.func @variadic_reduce(%input1: tensor<16x32x64xf32>,
%init1: tensor<16x64xf32>, %input2: tensor<16x32x64xi64>,
%init2: tensor<16x64xi64>) -> (tensor<16x64xf32>, tensor<16x64xi64>) {
%reduce, %reduce2 = linalg.reduce
ins(%input1, %input2 : tensor<16x32x64xf32>, tensor<16x32x64xi64>)
outs(%init1, %init2 : tensor<16x64xf32>, tensor<16x64xi64>)
dimensions = [1]
(%in1: f32, %in2: i64, %out1: f32, %out2: i64) {
%0 = arith.addf %in1, %out1: f32
%1 = arith.addi %in2, %out2: i64
linalg.yield %0, %1: f32, i64
}
func.return %reduce, %reduce2 : tensor<16x64xf32>, tensor<16x64xi64>
}
// CHECK-LABEL: func @variadic_reduce
// CHECK: linalg.reduce
// CHECK-NOT: { arith.addf
// -----
func.func @variadic_reduce_memref(%input1: memref<16x32x64xf32>,
%init1: memref<16x64xf32>, %input2: memref<16x32x64xi64>,
%init2: memref<16x64xi64>) {
linalg.reduce
ins(%input1, %input2 : memref<16x32x64xf32>, memref<16x32x64xi64>)
outs(%init1, %init2 : memref<16x64xf32>, memref<16x64xi64>)
dimensions = [1]
(%in1: f32, %in2: i64, %out1: f32, %out2: i64) {
%0 = arith.addf %in1, %out1: f32
%1 = arith.addi %in2, %out2: i64
linalg.yield %0, %1: f32, i64
}
func.return
}
// CHECK-LABEL: func @variadic_reduce_memref
// CHECK: linalg.reduce
// CHECK-NOT: { arith.addf
// -----
func.func @transpose(%input: tensor<16x32x64xf32>,
%init: tensor<32x64x16xf32>) -> tensor<32x64x16xf32> {
%transpose = linalg.transpose
ins(%input:tensor<16x32x64xf32>)
outs(%init:tensor<32x64x16xf32>)
permutation = [1, 2, 0]
func.return %transpose : tensor<32x64x16xf32>
}
// CHECK-LABEL: func @transpose
// CHECK: linalg.transpose ins
// CHECK-SAME: outs
// CHECK-SAME: permutation
// -----
func.func @transpose_memref(%input: memref<16x32x64xf32>,
%init: memref<32x64x16xf32>) {
linalg.transpose
ins(%input:memref<16x32x64xf32>)
outs(%init:memref<32x64x16xf32>)
permutation = [1, 2, 0]
func.return
}
// CHECK-LABEL: func @transpose_memref
// -----
func.func @broadcast_static_sizes(%input: tensor<8x32xf32>,
%init: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
%bcast = linalg.broadcast
ins(%input:tensor<8x32xf32>)
outs(%init:tensor<8x16x32xf32>)
dimensions = [1]
func.return %bcast : tensor<8x16x32xf32>
}
// CHECK-LABEL: func @broadcast_static_sizes
// CHECK: linalg.broadcast ins
// CHECK-SAME: outs
// CHECK-SAME: dimensions
// -----
func.func @broadcast_with_dynamic_sizes(
%input: tensor<8x?xf32>, %init: tensor<8x16x?xf32>)
-> tensor<8x16x?xf32> {
%bcast = linalg.broadcast
ins(%input:tensor<8x?xf32>)
outs(%init:tensor<8x16x?xf32>)
dimensions = [1]
func.return %bcast : tensor<8x16x?xf32>
}
// CHECK-LABEL: func @broadcast_with_dynamic_sizes
// CHECK: linalg.broadcast ins
// CHECK-SAME: outs
// CHECK-SAME: dimensions
// -----
func.func @broadcast_memref(%input: memref<8x32xf32>,
%init: memref<8x16x32xf32>) {
linalg.broadcast
ins(%input:memref<8x32xf32>)
outs(%init:memref<8x16x32xf32>)
dimensions = [1]
func.return
}
// CHECK-LABEL: func @broadcast_memref
// CHECK: linalg.broadcast ins
// CHECK-SAME: outs
// CHECK-SAME: dimensions
// -----
func.func @map_arith_with_attr(%lhs: tensor<64xf32>, %rhs: tensor<64xf32>,
%init: tensor<64xf32>) -> tensor<64xf32> {
%add = linalg.map
ins(%lhs, %rhs: tensor<64xf32>, tensor<64xf32>)
outs(%init:tensor<64xf32>)
(%lhs_elem: f32, %rhs_elem: f32) {
%0 = arith.addf %lhs_elem, %rhs_elem fastmath<fast> : f32
linalg.yield %0: f32
}
func.return %add : tensor<64xf32>
}
// CHECK-LABEL: func @map_arith_with_attr
// CHECK-NEXT: %[[MAPPED:.*]] = linalg.map
// CHECK-SAME: { arith.addf {fastmath = #arith.fastmath<fast>} }
// CHECK-SAME: ins
// CHECK-SAME: outs
// CHECK-NEXT: return %[[MAPPED]] : tensor<64xf32>
// -----
func.func @reduce_arith_with_attr(%input: tensor<16x32x64xf32>,
%init: tensor<16x64xf32>) -> tensor<16x64xf32> {
%reduce = linalg.reduce
ins(%input:tensor<16x32x64xf32>)
outs(%init:tensor<16x64xf32>)
dimensions = [1]
(%in: f32, %out: f32) {
%0 = arith.addf %out, %in fastmath<fast> : f32
linalg.yield %0: f32
}
func.return %reduce : tensor<16x64xf32>
}
// CHECK-LABEL: func @reduce_arith_with_attr
// CHECK-NEXT: %[[REDUCED:.*]] = linalg.reduce
// CHECK-SAME: { arith.addf {fastmath = #arith.fastmath<fast>} }
// CHECK-SAME: ins
// CHECK-SAME: outs
// CHECK-SAME: dimensions = [1]
// CHECK-NEXT: return %[[REDUCED]] : tensor<16x64xf32>
// -----
func.func @softmax(%arg0: tensor<2x16x32xf32>) -> tensor<2x16x32xf32> {
%0 = tensor.empty() : tensor<2x16x32xf32>
%1 = linalg.softmax dimension(2) ins(%arg0 : tensor<2x16x32xf32>) outs(%0: tensor<2x16x32xf32>) -> tensor<2x16x32xf32>
return %1 : tensor<2x16x32xf32>
}
// CHECK: func.func @softmax(%[[ARG0:[a-zA-Z0-9_]+]]: tensor<2x16x32xf32>) -> tensor<2x16x32xf32> {
// CHECK: %[[D0:.+]] = tensor.empty() : tensor<2x16x32xf32>
// CHECK: %[[D1:.+]] = linalg.softmax dimension(2) ins(%[[ARG0]] : tensor<2x16x32xf32>) outs(%[[D0]] :
// CHECK-SAME: tensor<2x16x32xf32>) -> tensor<2x16x32xf32>
// CHECK: return %[[D1]] : tensor<2x16x32xf32>
// CHECK: }
// -----
func.func @winograd(%arg0: tensor<2x6x6x5xf32>, %arg1: tensor<2x3x3x5xf32>, %arg2: tensor<1xf32>, %arg3: tensor<2x4x4x2xf32>) -> tensor<2x4x4x2xf32> {
%0 = tensor.empty() : tensor<6x6x5x2xf32>
%1 = linalg.winograd_filter_transform m(4) r(3) ins(%arg1 : tensor<2x3x3x5xf32>) outs(%0 : tensor<6x6x5x2xf32>) -> tensor<6x6x5x2xf32>
%2 = tensor.empty() : tensor<6x6x1x1x2x5xf32>
%3 = linalg.winograd_input_transform m(4) r(3) ins(%arg0 : tensor<2x6x6x5xf32>) outs(%2 : tensor<6x6x1x1x2x5xf32>) -> tensor<6x6x1x1x2x5xf32>
%collapsed = tensor.collapse_shape %1 [[0, 1], [2], [3]] : tensor<6x6x5x2xf32> into tensor<36x5x2xf32>
%collapsed_0 = tensor.collapse_shape %3 [[0, 1], [2, 3, 4], [5]] : tensor<6x6x1x1x2x5xf32> into tensor<36x2x5xf32>
%4 = tensor.empty() : tensor<36x2x2xf32>
%5 = linalg.batch_matmul ins(%collapsed_0, %collapsed : tensor<36x2x5xf32>, tensor<36x5x2xf32>) outs(%4 : tensor<36x2x2xf32>) -> tensor<36x2x2xf32>
%expanded = tensor.expand_shape %5 [[0, 1], [2, 3, 4], [5]] output_shape [6, 6, 1, 1, 2, 2] : tensor<36x2x2xf32> into tensor<6x6x1x1x2x2xf32>
%6 = linalg.winograd_output_transform m(4) r(3) ins(%expanded : tensor<6x6x1x1x2x2xf32>) outs(%arg3 : tensor<2x4x4x2xf32>) -> tensor<2x4x4x2xf32>
return %6 : tensor<2x4x4x2xf32>
}
// CHECK-LABEL: func @winograd
// CHECK: linalg.winograd_filter_transform m(4) r(3)
// CHECK: linalg.winograd_input_transform m(4) r(3)
// CHECK: linalg.winograd_output_transform m(4) r(3)
// -----
func.func @winograd_filter_dyn(%arg0: tensor<?x3x3x?xf32>, %arg1: tensor<6x6x?x?xf32>) -> tensor<6x6x?x?xf32> {
%0 = linalg.winograd_filter_transform m(4) r(3) ins(%arg0 : tensor<?x3x3x?xf32>) outs(%arg1 : tensor<6x6x?x?xf32>) -> tensor<6x6x?x?xf32>
return %0 : tensor<6x6x?x?xf32>
}
// CHECK-LABEL: func @winograd_filter_dyn
// CHECK: linalg.winograd_filter_transform m(4) r(3) ins(%arg0 : tensor<?x3x3x?xf32>) outs(%arg1 : tensor<6x6x?x?xf32>) -> tensor<6x6x?x?xf32>
// -----
func.func @winograd_input_dyn(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<6x6x?x?x?x?xf32>) -> tensor<6x6x?x?x?x?xf32> {
%0 = linalg.winograd_input_transform m(4) r(3) ins(%arg0 : tensor<?x?x?x?xf32>) outs(%arg1 : tensor<6x6x?x?x?x?xf32>) -> tensor<6x6x?x?x?x?xf32>
return %0 : tensor<6x6x?x?x?x?xf32>
}
// CHECK-LABEL: func @winograd_input_dyn
// CHECK: linalg.winograd_input_transform m(4) r(3) ins(%arg0 : tensor<?x?x?x?xf32>) outs(%arg1 : tensor<6x6x?x?x?x?xf32>) -> tensor<6x6x?x?x?x?xf32>
// -----
func.func @winograd_output_dyn(%arg0: tensor<6x6x?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
%0 = linalg.winograd_output_transform m(4) r(3) ins(%arg0 : tensor<6x6x?x?x?x?xf32>) outs(%arg1 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %0 : tensor<?x?x?x?xf32>
}
// CHECK-LABEL: func @winograd_output_dyn
// CHECK: linalg.winograd_output_transform m(4) r(3) ins(%arg0 : tensor<6x6x?x?x?x?xf32>) outs(%arg1 : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
Codebase Browser
llvm