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TheRock/compiler/amd-llvm/mlir/test/Dialect/Linalg/elementwise/generalize-named-ops.mlir
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Flatten subprojects into a single base monorepo
2026-09-02 16:34:47 -07:00

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// RUN: mlir-opt %s -linalg-generalize-named-ops -split-input-file | FileCheck %s
// CHECK: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
//
// CHECK: @unary_exp(%[[A:.+]]: tensor<8x16x32xf32>, %[[B:.+]]: tensor<8x16x32xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[IDENTITY]], #[[IDENTITY]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[A]]
// CHECK-SAME: outs(%[[B]]
//
// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32)
// CHECK: %[[EXP:.+]] = math.exp %[[A_ARG]] : f32
// CHECK: linalg.yield %[[EXP]] : f32
//
func.func @unary_exp(%A : tensor<8x16x32xf32>, %B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
%r = linalg.elementwise
kind=#linalg.elementwise_kind<exp>
ins(%A : tensor<8x16x32xf32>)
outs(%B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
return %r : tensor<8x16x32xf32>
}
// -----
// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[PROJECTION:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
//
// CHECK: @unary_transpose_broadcast_tanh(%[[A:.+]]: tensor<32x16xf32>, %[[B:.+]]: tensor<8x16x32xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[PROJECTION]], #[[IDENTITY]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[A]]
// CHECK-SAME: outs(%[[B]]
//
// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32)
// CHECK: %[[TANH:.+]] = math.tanh %[[A_ARG]] : f32
// CHECK: linalg.yield %[[TANH]] : f32
//
func.func @unary_transpose_broadcast_tanh(%A : tensor<32x16xf32>, %B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
%r = linalg.elementwise
kind=#linalg.elementwise_kind<tanh>
indexing_maps = [affine_map<(d0, d1, d2) -> (d2, d1)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>]
ins(%A : tensor<32x16xf32>)
outs(%B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
return %r : tensor<8x16x32xf32>
}
// -----
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
//
// CHECK: @binary_div_on_memrefs(%[[A:.+]]: memref<16x8xf32>, %[[B:.+]]: memref<16x8xf32>, %[[C:.+]]: memref<16x8xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel"]
// CHECK-SAME: ins(%[[A]], %[[B]]
// CHECK-SAME: outs(%[[C]]
//
// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32)
// CHECK: %[[DIV:.+]] = arith.divf %[[A_ARG]], %[[B_ARG]] : f32
// CHECK: linalg.yield %[[DIV]] : f32
//
func.func @binary_div_on_memrefs(%A : memref<16x8xf32>, %B: memref<16x8xf32>, %C: memref<16x8xf32>) {
linalg.elementwise
kind=#linalg.elementwise_kind<div>
ins(%A, %B: memref<16x8xf32>, memref<16x8xf32>)
outs(%C: memref<16x8xf32>)
return
}
// -----
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
//
// CHECK: @binary_mul_on_tensors(%[[A:.+]]: tensor<16x8xf32>, %[[B:.+]]: tensor<16x8xf32>, %[[C:.+]]: tensor<16x8xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel"]
// CHECK-SAME: ins(%[[A]], %[[B]]
// CHECK-SAME: outs(%[[C]]
//
// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32)
// CHECK: %[[MUL:.+]] = arith.mulf %[[A_ARG]], %[[B_ARG]] : f32
// CHECK: linalg.yield %[[MUL]] : f32
//
func.func @binary_mul_on_tensors(%A : tensor<16x8xf32>, %B: tensor<16x8xf32>, %C: tensor<16x8xf32>) -> tensor<16x8xf32> {
%r = linalg.elementwise
kind=#linalg.elementwise_kind<mul>
ins(%A, %B: tensor<16x8xf32>, tensor<16x8xf32>)
outs(%C: tensor<16x8xf32>) -> tensor<16x8xf32>
return %r : tensor<16x8xf32>
}
// -----
// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[TRANSPOSE:.+]] = affine_map<(d0, d1) -> (d1, d0)>
//
// CHECK: @binary_transpose_a(%[[A:.+]]: tensor<8x16xf32>, %[[B:.+]]: tensor<16x8xf32>, %[[C:.+]]: tensor<16x8xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[TRANSPOSE]], #[[IDENTITY]], #[[IDENTITY]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel"]
// CHECK-SAME: ins(%[[A]], %[[B]]
// CHECK-SAME: outs(%[[C]]
//
// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32)
// CHECK: %[[SUB:.+]] = arith.subf %[[A_ARG]], %[[B_ARG]] : f32
// CHECK: linalg.yield %[[SUB]] : f32
//
func.func @binary_transpose_a(%A : tensor<8x16xf32>, %B: tensor<16x8xf32>, %C: tensor<16x8xf32>) -> tensor<16x8xf32> {
%r = linalg.elementwise
kind=#linalg.elementwise_kind<sub>
indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>]
ins(%A, %B: tensor<8x16xf32>, tensor<16x8xf32>)
outs(%C: tensor<16x8xf32>) -> tensor<16x8xf32>
return %r : tensor<16x8xf32>
}
// -----
// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[TRANSPOSE:.+]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK-DAG: #[[BROADCAST:.+]] = affine_map<(d0, d1) -> (d0)>
//
// CHECK: @binary_transpose_a_broadcast_b(%[[A:.+]]: tensor<8x16xf32>, %[[B:.+]]: tensor<16xf32>, %[[C:.+]]: tensor<16x8xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[TRANSPOSE]], #[[BROADCAST]], #[[IDENTITY]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel"]
// CHECK-SAME: ins(%[[A]], %[[B]]
// CHECK-SAME: outs(%[[C]]
//
// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32)
// CHECK: %[[ADD:.+]] = arith.addf %[[A_ARG]], %[[B_ARG]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
//
func.func @binary_transpose_a_broadcast_b(%A : tensor<8x16xf32>, %B: tensor<16xf32>, %C: tensor<16x8xf32>) -> tensor<16x8xf32> {
%r = linalg.elementwise
kind=#linalg.elementwise_kind<add>
indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>,
affine_map<(d0, d1) -> (d0)>,
affine_map<(d0, d1) -> (d0, d1)>]
ins(%A, %B: tensor<8x16xf32>, tensor<16xf32>)
outs(%C: tensor<16x8xf32>) -> tensor<16x8xf32>
return %r : tensor<16x8xf32>
}
// -----
// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[PROJECTION:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
//
// CHECK: @ternary(%[[A:.+]]: tensor<32x16xi1>, %[[B:.+]]: tensor<8x16x32xf32>, %[[C:.+]]: tensor<8x16x32xf32>, %[[D:.+]]: tensor<8x16x32xf32>)
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[PROJECTION]], #[[IDENTITY]], #[[IDENTITY]], #[[IDENTITY]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
//
// CHECK-SAME: ins(%[[A]], %[[B]], %[[C]]
// CHECK-SAME: outs(%[[D]]
//
// CHECK: ^{{.*}}(%[[A_ARG:.+]]: i1, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32, %[[D_ARG:.+]]: f32)
// CHECK: %[[SELECTED:.+]] = arith.select %[[A_ARG]], %[[B_ARG]], %[[C_ARG]] : f32
// CHECK: linalg.yield %[[SELECTED]] : f32
//
func.func @ternary(%A : tensor<32x16xi1>, %B: tensor<8x16x32xf32>, %C : tensor<8x16x32xf32>, %D : tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
%r = linalg.elementwise
kind=#linalg.elementwise_kind<select>
indexing_maps = [affine_map<(d0, d1, d2) -> (d2, d1)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>]
ins(%A, %B, %C : tensor<32x16xi1>, tensor<8x16x32xf32>, tensor<8x16x32xf32>)
outs(%D: tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
return %r : tensor<8x16x32xf32>
}
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