derek.holloway
7502bf64a4
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72 lines
3.9 KiB
MLIR
72 lines
3.9 KiB
MLIR
// RUN: mlir-opt %s -split-input-file --linalg-specialize-generic-ops | FileCheck %s
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#projection = affine_map<(d0, d1, d2, d3, d4) -> (d2, d3, d1)>
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#identity = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
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func.func @transpose_and_broadcast(%x : tensor<7x8x9xf32>, %y: tensor<5x9x7x8x10xf32>, %z : tensor<5x9x7x8x10xf32>) -> tensor<5x9x7x8x10xf32> {
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%res = linalg.generic
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{ indexing_maps = [#projection, #identity, #identity], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
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ins(%x, %y : tensor<7x8x9xf32>, tensor<5x9x7x8x10xf32>) outs(%z : tensor<5x9x7x8x10xf32>) {
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^bb0(%in: f32, %in_1: f32, %out: f32):
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%div = arith.divf %in, %in_1 : f32
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linalg.yield %div : f32
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} -> tensor<5x9x7x8x10xf32>
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return %res : tensor<5x9x7x8x10xf32>
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}
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// CHECK-LABEL: transpose_and_broadcast
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// CHECK-SAME: %[[X:.+]]: tensor<7x8x9xf32>, %[[Y:.+]]: tensor<5x9x7x8x10xf32>, %[[Z:.+]]: tensor<5x9x7x8x10xf32>) -> tensor<5x9x7x8x10xf32> {
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// CHECK: %[[E0:.+]] = tensor.empty() : tensor<9x7x8xf32>
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// CHECK: %[[X_trans:.+]] = linalg.transpose ins(%[[X]] : tensor<7x8x9xf32>) outs(%[[E0]] : tensor<9x7x8xf32>) permutation = [2, 0, 1]
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// CHECK: %[[E1:.+]] = tensor.empty() : tensor<5x9x7x8x10xf32>
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// CHECK: %[[X_trans_bc:.+]] = linalg.broadcast ins(%[[X_trans]] : tensor<9x7x8xf32>) outs(%[[E1]] : tensor<5x9x7x8x10xf32>) dimensions = [0, 4]
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// CHECK: {{.*}} = linalg.div ins(%[[X_trans_bc]], %[[Y]] : tensor<5x9x7x8x10xf32>, tensor<5x9x7x8x10xf32>) outs(%[[Z]] : tensor<5x9x7x8x10xf32>) -> tensor<5x9x7x8x10xf32>
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// CHECK-NOT: linalg.generic
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// -----
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#identity = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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#transposed = affine_map<(d0, d1, d2) -> (d2, d0, d1)>
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func.func @transpose_only(%x : tensor<32x2x16xf32>, %y: tensor<2x16x32xf32>, %z : tensor<2x16x32xf32>) -> tensor<2x16x32xf32> {
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%res = linalg.generic
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{ indexing_maps = [#transposed, #identity, #identity], iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%x, %y : tensor<32x2x16xf32>, tensor<2x16x32xf32>)
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outs(%z : tensor<2x16x32xf32>) {
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^bb0(%in: f32, %in_1: f32, %out: f32):
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%div = arith.divf %in, %in_1 : f32
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linalg.yield %div : f32
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} -> tensor<2x16x32xf32>
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return %res : tensor<2x16x32xf32>
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}
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// CHECK-LABEL: transpose_only
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// CHECK-SAME: %[[X:.+]]: tensor<32x2x16xf32>, %[[Y:.+]]: tensor<2x16x32xf32>, %[[Z:.+]]: tensor<2x16x32xf32>) -> tensor<2x16x32xf32> {
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// CHECK: %[[E0:.+]] = tensor.empty() : tensor<2x16x32xf32>
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// CHECK: %[[X_trans:.+]] = linalg.transpose ins(%[[X]] : tensor<32x2x16xf32>) outs(%[[E0]] : tensor<2x16x32xf32>) permutation = [1, 2, 0]
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// CHECK: {{.*}} = linalg.div ins(%[[X_trans]], %[[Y]] : tensor<2x16x32xf32>, tensor<2x16x32xf32>) outs(%[[Z]] : tensor<2x16x32xf32>) -> tensor<2x16x32xf32>
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// CHECK-NOT: linalg.generic
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// -----
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#identity = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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#broadcast = affine_map<(d0, d1, d2) -> (d0, d2)>
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func.func @broadcast_only(%x : tensor<2x16x32xf32>, %y: tensor<2x32xf32>, %z : tensor<2x16x32xf32>) -> tensor<2x16x32xf32> {
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%res = linalg.generic
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{ indexing_maps = [#identity, #broadcast, #identity], iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%x, %y : tensor<2x16x32xf32>, tensor<2x32xf32>)
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outs(%z : tensor<2x16x32xf32>) {
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^bb0(%in: f32, %in_1: f32, %out: f32):
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%div = arith.divf %in, %in_1 : f32
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linalg.yield %div : f32
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} -> tensor<2x16x32xf32>
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return %res : tensor<2x16x32xf32>
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}
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// CHECK-LABEL: broadcast_only
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// CHECK-SAME: %[[X:.+]]: tensor<2x16x32xf32>, %[[Y:.+]]: tensor<2x32xf32>, %[[Z:.+]]: tensor<2x16x32xf32>) -> tensor<2x16x32xf32> {
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// CHECK: %[[E0:.+]] = tensor.empty() : tensor<2x16x32xf32>
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// CHECK: %[[X_bc:.+]] = linalg.broadcast ins(%[[Y]] : tensor<2x32xf32>) outs(%[[E0]] : tensor<2x16x32xf32>) dimensions = [1]
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// CHECK: {{.*}} = linalg.div ins(%[[X]], %[[X_bc]] : tensor<2x16x32xf32>, tensor<2x16x32xf32>) outs(%arg2 : tensor<2x16x32xf32>) -> tensor<2x16x32xf32>
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// CHECK-NOT: linalg.generic
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