88 lines
3.7 KiB
MLIR
88 lines
3.7 KiB
MLIR
// RUN: mlir-hlo-opt %s --split-input-file --mhlo-rank-specialization-cluster | FileCheck %s
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// CHECK-LABEL: @add_mul
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// CHECK-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>, %[[ARG2:.*]]: tensor<*xf32>)
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func @add_mul(%arg0 : tensor<*xf32>, %arg1 : tensor<*xf32>,
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%arg2 : tensor<*xf32>) -> tensor<*xf32> {
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// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG2]], %[[ARG0]], %[[ARG1]]) ( {
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// CHECK: ^bb0(%[[ARG2_:.*]]: tensor<*xf32>, %[[ARG0_:.*]]: tensor<*xf32>, %[[ARG1_:.*]]: tensor<*xf32>):
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// CHECK: %[[TMP:.*]] = chlo.broadcast_multiply %[[ARG0_]], %[[ARG1_]]
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// CHECK: %[[INNER_RES:.*]] = chlo.broadcast_add %[[TMP]], %[[ARG2_]]
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// CHECK: "chlo.rank_specialization_cluster_yield"(%[[INNER_RES]])
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// CHECK: }) : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
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// CHECK: return %[[RES]]
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%0 = chlo.broadcast_multiply %arg0, %arg1
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: (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
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%1 = chlo.broadcast_add %0, %arg2
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: (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
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return %1 : tensor<*xf32>
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}
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// -----
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// Unary MHLO operation.
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// CHECK-LABEL: @sqrt
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// CHECK-SAME: (%[[ARG:.*]]: tensor<*xf32>)
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func @sqrt(%arg : tensor<*xf32>) -> tensor<*xf32> {
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// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG]])
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// CHECK: ^bb0(%[[ARG_:.*]]: tensor<*xf32>):
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// CHECK: %[[TMP0:.*]] = "mhlo.sqrt"(%[[ARG_]])
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// CHECK: %[[TMP1:.*]] = "mhlo.sqrt"(%[[TMP0]])
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// CHECK: %[[TMP2:.*]] = "mhlo.sqrt"(%[[TMP1]])
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// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP2]])
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// CHECK: return %[[RES]]
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%0 = "mhlo.sqrt"(%arg) : (tensor<*xf32>) -> tensor<*xf32>
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%1 = "mhlo.sqrt"(%0) : (tensor<*xf32>) -> tensor<*xf32>
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%2 = "mhlo.sqrt"(%1) : (tensor<*xf32>) -> tensor<*xf32>
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return %2 : tensor<*xf32>
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}
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// -----
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// Don't cluster single ranked operation.
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// CHECK-LABEL: @sqrt_ranked
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// CHECK-SAME: (%[[ARG:.*]]: tensor<3x?xf32>)
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func @sqrt_ranked(%arg: tensor<3x?xf32>) -> tensor<3x?xf32> {
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// CHECK-NOT: rank_specialization_cluster
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%0 = "mhlo.sqrt"(%arg) : (tensor<3x?xf32>) -> tensor<3x?xf32>
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%1 = "mhlo.sqrt"(%0) : (tensor<3x?xf32>) -> tensor<3x?xf32>
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%2 = "mhlo.sqrt"(%1) : (tensor<3x?xf32>) -> tensor<3x?xf32>
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return %2 : tensor<3x?xf32>
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}
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// -----
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// Ternary operation.
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// CHECK-LABEL: @select_mixed
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// CHECK-SAME: (%[[PRED:.*]]: tensor<*xi1>, %[[ARG1:.*]]: tensor<*xf32>, %[[ARG2:.*]]: tensor<2xf32>)
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func @select_mixed(%pred: tensor<*xi1>, %arg1: tensor<*xf32>,
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%arg2: tensor<2xf32>) -> tensor<*xf32> {
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// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[PRED]], %[[ARG1]], %[[ARG2]])
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// CHECK: ^bb0(%[[PRED_:.*]]: tensor<*xi1>, %[[ARG1_:.*]]: tensor<*xf32>, %[[ARG2_:.*]]: tensor<2xf32>)
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// CHECK: %[[TMP:.*]] = chlo.broadcast_select %[[PRED_]], %[[ARG1_]], %[[ARG2_]]
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// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP]])
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// CHECK: return %[[RES]]
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%0 = "chlo.broadcast_select"(%pred, %arg1, %arg2)
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: (tensor<*xi1>, tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
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return %0 : tensor<*xf32>
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}
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// -----
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// Unary CHLO operation.
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// CHECK-LABEL: @tan
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// CHECK-SAME: (%[[ARG:.*]]: tensor<*xf32>) -> tensor<*xf32>
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func @tan(%arg : tensor<*xf32>) -> tensor<*xf32> {
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// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG]]) ( {
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// CHECK: ^bb0(%[[ARG_:.*]]: tensor<*xf32>)
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// CHECK: %[[TMP0:.*]] = chlo.tan %[[ARG_]]
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// CHECK: %[[TMP1:.*]] = chlo.tan %[[TMP0]]
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// CHECK: %[[TMP2:.*]] = chlo.tan %[[TMP1]]
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// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP2]])
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// CHECK: return %[[RES]]
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%0 = chlo.tan %arg : tensor<*xf32> -> tensor<*xf32>
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%1 = chlo.tan %0 : tensor<*xf32> -> tensor<*xf32>
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%2 = chlo.tan %1 : tensor<*xf32> -> tensor<*xf32>
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return %2 : tensor<*xf32>
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}
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