derek.holloway
7502bf64a4
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91 lines
3.9 KiB
MLIR
91 lines
3.9 KiB
MLIR
// RUN: mlir-opt %s -split-input-file | FileCheck %s
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//
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// Note - the functions are named @{unary|binary}_{identity|transpose|broadcast|transpose_a|...}_{exp|mul|div|..}
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// CHECK: @unary_identity_exp(%[[A:.+]]: tensor<8x16x32xf32>, %[[B:.+]]: tensor<8x16x32xf32>)
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// CHECK: %{{.*}} = linalg.elementwise kind=#linalg.elementwise_kind<exp>
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// CHECK-SAME ins(%[[A:.+]] : tensor<8x16x32xf32>) outs(%[[B:.+]] : tensor<8x16x32xf32>)
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//
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func.func @unary_identity_exp(%A : tensor<8x16x32xf32>, %B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
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%r = linalg.elementwise
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kind=#linalg.elementwise_kind<exp>
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ins(%A : tensor<8x16x32xf32>)
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outs(%B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
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return %r : tensor<8x16x32xf32>
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}
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// -----
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// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-DAG: #[[PROJECTION:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
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//
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// CHECK: @unary_projection_tanh(%[[A:.+]]: tensor<?x16xf32>,
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// CHECK-SAME: %[[B:.+]]: tensor<8x16x?xf32>) -> tensor<8x16x?xf32> {
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// CHECK: {{.*}} = linalg.elementwise kind=#linalg.elementwise_kind<tanh>
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// CHECK-SAME: indexing_maps = [#[[PROJECTION]], #[[IDENTITY]]]
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// CHECK-SAME: ins(%[[A]] : tensor<?x16xf32>) outs(%[[B]] : tensor<8x16x?xf32>) -> tensor<8x16x?xf32>
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//
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func.func @unary_projection_tanh(%A: tensor<?x16xf32>,
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%B: tensor<8x16x?xf32>) -> tensor<8x16x?xf32> {
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%r = linalg.elementwise
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kind=#linalg.elementwise_kind<tanh>
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indexing_maps = [affine_map<(d0, d1, d2) -> (d2, d1)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>]
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ins(%A : tensor<?x16xf32>)
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outs(%B: tensor<8x16x?xf32>) -> tensor<8x16x?xf32>
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return %r : tensor<8x16x?xf32>
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}
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// -----
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// CHECK: @binary_identity_div(%[[A:.+]]: tensor<16x8xf32>, %[[B:.+]]: tensor<16x8xf32>,
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// CHECK-SAME: %[[C:.+]]: tensor<16x8xf32>) -> tensor<16x8xf32> {
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// CHECK: {{.*}} = linalg.elementwise
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// CHECK-SAME: kind=#linalg.elementwise_kind<div>
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// CHECK-SAME: ins(%[[A]], %[[B]] : tensor<16x8xf32>, tensor<16x8xf32>)
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// CHECK-SAME: outs(%[[C]] : tensor<16x8xf32>) -> tensor<16x8xf32>
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//
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func.func @binary_identity_div(%A: tensor<16x8xf32>, %B: tensor<16x8xf32>,
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%C: tensor<16x8xf32>) -> tensor<16x8xf32> {
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%r = linalg.elementwise
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kind=#linalg.elementwise_kind<div>
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ins(%A, %B: tensor<16x8xf32>, tensor<16x8xf32>)
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outs(%C: tensor<16x8xf32>) -> tensor<16x8xf32>
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return %r : tensor<16x8xf32>
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}
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// -----
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// CHECK: @binary_identity_mul_5Di(%[[A]]: tensor<1x2x3x4x5xi32>,
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// CHECK-SAME: %[[B:.+]]: tensor<1x2x3x4x5xi32>,
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// CHECK-SAME: %[[C:.+]]: tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32> {
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// CHECK: {{.*}} = linalg.elementwise
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// CHECK-SAME: kind=#linalg.elementwise_kind<mul>
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// CHECK-SAME: ins(%[[A]], %[[B]] : tensor<1x2x3x4x5xi32>, tensor<1x2x3x4x5xi32>)
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// CHECK-SAME: outs(%[[C]] : tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32>
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//
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func.func @binary_identity_mul_5Di(%A: tensor<1x2x3x4x5xi32>, %B: tensor<1x2x3x4x5xi32>,
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%C: tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32> {
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%r = linalg.elementwise
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kind=#linalg.elementwise_kind<mul>
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ins(%A, %B: tensor<1x2x3x4x5xi32>, tensor<1x2x3x4x5xi32>)
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outs(%C: tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32>
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return %r : tensor<1x2x3x4x5xi32>
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}
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// -----
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// CHECK: @redundant_maps
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// CHECK-NOT: indexing_maps
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//
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#map = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
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func.func @redundant_maps(%A: tensor<1x2x3x4x5xi32>, %B: tensor<1x2x3x4x5xi32>,
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%C: tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32> {
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%r = linalg.elementwise
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kind=#linalg.elementwise_kind<mul>
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indexing_maps = [#map, #map, #map]
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ins(%A, %B: tensor<1x2x3x4x5xi32>, tensor<1x2x3x4x5xi32>)
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outs(%C: tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32>
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return %r : tensor<1x2x3x4x5xi32>
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}
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