3efac5c68a
Add a new mlir-opt pass `--linalg-specialize-generic-ops` which lifts generic,
where possible, to linalg named ops.
Much like `-linalg-generalize-named-ops` lowers named ops to linalg.generic .
Also add patterns to recognize contractions which can be specialized from
linalg.generic to named op: `linalg.{batch_}?matmul{_transpose_(a|b)}?`
77 lines
4.4 KiB
MLIR
77 lines
4.4 KiB
MLIR
// RUN: mlir-opt --transform-interpreter --split-input-file --verify-diagnostics %s | FileCheck %s
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#map = affine_map<(d0, d1) -> (d0, d1)>
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func.func @specialize_add(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%1 = arith.addf %in, %in_0 : f32
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linalg.yield %1 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: specialize_add
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// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK-NOT: linalg.generic
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// CHECK: linalg.add ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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func.func @specialize_sub(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%1 = arith.subf %in, %in_0 : f32
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linalg.yield %1 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: specialize_sub
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// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK-NOT: linalg.generic
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// CHECK: linalg.sub ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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func.func @specialize_sub_swapped_operands(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%1 = arith.subf %in_0, %in : f32
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linalg.yield %1 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: specialize_sub
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// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK-NOT: linalg.generic
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// CHECK: linalg.sub ins(%[[ARG1]], %[[ARG0]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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func.func @specialize_mul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%1 = arith.mulf %in, %in_0 : f32
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linalg.yield %1 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: specialize_mul
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// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK-NOT: linalg.generic
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// CHECK: linalg.mul ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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func.func @specialize_div(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg2 : tensor<?x?xf32>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%1 = arith.divf %in, %in_0 : f32
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linalg.yield %1 : f32
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} -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: specialize_div
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// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK-NOT: linalg.generic
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// CHECK: linalg.div ins(%[[ARG0]], %[[ARG1]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match interface{LinalgOp} in %arg0 : (!transform.any_op) -> !transform.any_op
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%1 = transform.structured.specialize %0 : (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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