542 lines
34 KiB
MLIR
542 lines
34 KiB
MLIR
// RUN: mlir-hlo-opt %s --split-input-file --mhlo-rank-specialization-cluster | FileCheck %s
|
|
// RUN: mlir-hlo-opt %s --split-input-file --mhlo-rank-specialization-cluster --mhlo-rank-specialization-to-scf=max-target-rank=3 | FileCheck %s --check-prefix CHECK-SCF
|
|
|
|
// CHECK-LABEL: @add_mul
|
|
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>, %[[ARG2:.*]]: tensor<*xf32>)
|
|
func @add_mul(%arg0 : tensor<*xf32>, %arg1 : tensor<*xf32>,
|
|
%arg2 : tensor<*xf32>) -> tensor<*xf32> {
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG2]], %[[ARG0]], %[[ARG1]]) ( {
|
|
// CHECK: ^bb0(%[[ARG2_:.*]]: tensor<*xf32>, %[[ARG0_:.*]]: tensor<*xf32>, %[[ARG1_:.*]]: tensor<*xf32>):
|
|
// CHECK: %[[TMP:.*]] = chlo.broadcast_multiply %[[ARG0_]], %[[ARG1_]]
|
|
// CHECK: %[[INNER_RES:.*]] = chlo.broadcast_add %[[TMP]], %[[ARG2_]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[INNER_RES]])
|
|
// CHECK: }) : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
// CHECK: return %[[RES]]
|
|
%0 = chlo.broadcast_multiply %arg0, %arg1
|
|
: (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
%1 = chlo.broadcast_add %0, %arg2
|
|
: (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
return %1 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @add_mul
|
|
// CHECK-SCF-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>, %[[ARG2:.*]]: tensor<*xf32>)
|
|
// CHECK-SCF-DAG: %[[C1:.*]] = constant 1
|
|
// CHECK-SCF-DAG: %[[C2:.*]] = constant 2
|
|
// CHECK-SCF-DAG: %[[C3:.*]] = constant 3
|
|
// CHECK-SCF-DAG: %[[ONE_SHAPE_1:.*]] = shape.const_shape [1]
|
|
// CHECK-SCF-DAG: %[[ONE_SHAPE_2:.*]] = shape.const_shape [1, 1]
|
|
// CHECK-SCF-DAG: %[[ONE_SHAPE_3:.*]] = shape.const_shape [1, 1, 1]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG0:.*]] = shape.shape_of %[[ARG0]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG1:.*]] = shape.shape_of %[[ARG1]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG2:.*]] = shape.shape_of %[[ARG2]]
|
|
// Equal shapes case:
|
|
// CHECK-SCF-DAG: %[[EQ20:.*]] = shape.shape_eq %[[SHAPE_ARG2]], %[[SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EQ21:.*]] = shape.shape_eq %[[SHAPE_ARG2]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[SHAPES_EQ:.*]] = and %[[EQ20]], %[[EQ21]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_EQ_SHAPES:.*]] = scf.if %[[SHAPES_EQ]]
|
|
// CHECK-SCF-DAG: %[[S20:.*]] = shape.any %[[SHAPE_ARG2]], %[[SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[S201:.*]] = shape.any %[[S20]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[N:.*]] = shape.num_elements %[[S201]]
|
|
// CHECK-SCF-DAG: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF-DAG: %[[FLAT_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[FLAT_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[FLAT_ARG2:.*]] = "mhlo.dynamic_reshape"(%[[ARG2]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[TMP:.*]] = chlo.broadcast_multiply %[[FLAT_ARG0]], %[[FLAT_ARG1]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_add %[[TMP]], %[[FLAT_ARG2]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Find maximum reduced rank.
|
|
// CHECK-SCF-DAG: %[[REDUCED_SHAPES:.*]]:3 = chlo.minimum_broadcast_shapes %[[SHAPE_ARG2]], %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_RANK0:.*]] = shape.rank %[[REDUCED_SHAPES]]#1
|
|
// CHECK-SCF-DAG: %[[REDUCED_RANK1:.*]] = shape.rank %[[REDUCED_SHAPES]]#2
|
|
// CHECK-SCF-DAG: %[[REDUCED_RANK2:.*]] = shape.rank %[[REDUCED_SHAPES]]#0
|
|
// CHECK-SCF-DAG: %[[R2_GT_R0:.*]] = cmpi sgt, %[[REDUCED_RANK2]], %[[REDUCED_RANK0]]
|
|
// CHECK-SCF-DAG: %[[R20:.*]] = select %[[R2_GT_R0]], %[[REDUCED_RANK2]], %[[REDUCED_RANK0]]
|
|
// CHECK-SCF-DAG: %[[R20_GT_R1:.*]] = cmpi sgt, %[[R20]], %[[REDUCED_RANK1]]
|
|
// CHECK-SCF-DAG: %[[MAX_RED_RANK:.*]] = select %[[R20_GT_R1]], %[[R20]], %[[REDUCED_RANK1]]
|
|
// Generic case 1:
|
|
// CHECK-SCF: %[[MAX_RED_RANK_LE_1:.*]] = cmpi ule, %[[MAX_RED_RANK]], %[[C1]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_1:.*]] = scf.if %[[MAX_RED_RANK_LE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#1, %[[ONE_SHAPE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#2, %[[ONE_SHAPE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG2:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#0, %[[ONE_SHAPE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0_:.*]] = tensor.cast %[[EXT_SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1_:.*]] = tensor.cast %[[EXT_SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG2_:.*]] = tensor.cast %[[EXT_SHAPE_ARG2]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[EXT_SHAPE_ARG0_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[EXT_SHAPE_ARG1_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG2:.*]] = "mhlo.dynamic_reshape"(%[[ARG2]], %[[EXT_SHAPE_ARG2_]])
|
|
// CHECK-SCF-DAG: %[[TMP:.*]] = chlo.broadcast_multiply %[[REDUCED_ARG0]], %[[REDUCED_ARG1]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_add %[[TMP]], %[[REDUCED_ARG2]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Generic case 2:
|
|
// CHECK-SCF: %[[MAX_RED_RANK_LE_2:.*]] = cmpi ule, %[[MAX_RED_RANK]], %[[C2]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_2:.*]] = scf.if %[[MAX_RED_RANK_LE_2]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#1, %[[ONE_SHAPE_2]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#2, %[[ONE_SHAPE_2]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG2:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#0, %[[ONE_SHAPE_2]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0_:.*]] = tensor.cast %[[EXT_SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1_:.*]] = tensor.cast %[[EXT_SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG2_:.*]] = tensor.cast %[[EXT_SHAPE_ARG2]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[EXT_SHAPE_ARG0_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[EXT_SHAPE_ARG1_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG2:.*]] = "mhlo.dynamic_reshape"(%[[ARG2]], %[[EXT_SHAPE_ARG2_]])
|
|
// CHECK-SCF-DAG: %[[TMP:.*]] = chlo.broadcast_multiply %[[REDUCED_ARG0]], %[[REDUCED_ARG1]] : (tensor<?x?xf32>, tensor<?x?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_add %[[TMP]], %[[REDUCED_ARG2]] : (tensor<?x?xf32>, tensor<?x?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Generic case 3:
|
|
// CHECK-SCF: %[[MAX_RED_RANK_LE_3:.*]] = cmpi ule, %[[MAX_RED_RANK]], %[[C3]]
|
|
// CHECK-SCF: assert %[[MAX_RED_RANK_LE_3]], "Input for dynamic binary or n-ary op lowering was of a rank greater than 3"
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#1, %[[ONE_SHAPE_3]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#2, %[[ONE_SHAPE_3]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG2:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#0, %[[ONE_SHAPE_3]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0_:.*]] = tensor.cast %[[EXT_SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1_:.*]] = tensor.cast %[[EXT_SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG2_:.*]] = tensor.cast %[[EXT_SHAPE_ARG2]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[EXT_SHAPE_ARG0_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[EXT_SHAPE_ARG1_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG2:.*]] = "mhlo.dynamic_reshape"(%[[ARG2]], %[[EXT_SHAPE_ARG2_]])
|
|
// CHECK-SCF-DAG: %[[TMP:.*]] = chlo.broadcast_multiply %[[REDUCED_ARG0]], %[[REDUCED_ARG1]] : (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_add %[[TMP]], %[[REDUCED_ARG2]] : (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: scf.yield %[[UNSHAPED_RES_2]]
|
|
// CHECK-SCF: scf.yield %[[UNSHAPED_RES_1]]
|
|
// Reshape the result.
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG0:.*]] = shape.shape_of %[[ARG0]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG1:.*]] = shape.shape_of %[[ARG1]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG2:.*]] = shape.shape_of %[[ARG2]]
|
|
// CHECK-SCF-DAG: %[[RES_SHAPE:.*]] = shape.broadcast %[[SHAPE_ARG2]], %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[RES:.*]] = "mhlo.dynamic_reshape"(%[[UNSHAPED_RES_EQ_SHAPES]], %[[RES_SHAPE]])
|
|
// CHECK-SCF: return %[[RES]]
|
|
|
|
// -----
|
|
|
|
// Unary MHLO operation.
|
|
// CHECK-LABEL: @sqrt
|
|
// CHECK-SAME: (%[[ARG:.*]]: tensor<*xf32>)
|
|
func @sqrt(%arg : tensor<*xf32>) -> tensor<*xf32> {
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG]])
|
|
// CHECK: ^bb0(%[[ARG_:.*]]: tensor<*xf32>):
|
|
// CHECK: %[[TMP0:.*]] = "mhlo.sqrt"(%[[ARG_]])
|
|
// CHECK: %[[TMP1:.*]] = "mhlo.sqrt"(%[[TMP0]])
|
|
// CHECK: %[[TMP2:.*]] = "mhlo.sqrt"(%[[TMP1]])
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP2]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = "mhlo.sqrt"(%arg) : (tensor<*xf32>) -> tensor<*xf32>
|
|
%1 = "mhlo.sqrt"(%0) : (tensor<*xf32>) -> tensor<*xf32>
|
|
%2 = "mhlo.sqrt"(%1) : (tensor<*xf32>) -> tensor<*xf32>
|
|
return %2 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @sqrt
|
|
// CHECK-SCF-SAME: (%[[ARG:.*]]: tensor<*xf32>)
|
|
// CHECK-SCF: %[[SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[N:.*]] = shape.num_elements %[[SHAPE]]
|
|
// CHECK-SCF: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF: %[[FLAT_ARG:.*]] = "mhlo.dynamic_reshape"(%[[ARG]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
|
|
// CHECK-SCF: %[[TMP0:.*]] = "mhlo.sqrt"(%[[FLAT_ARG]]) : (tensor<?xf32>)
|
|
// CHECK-SCF: %[[TMP1:.*]] = "mhlo.sqrt"(%[[TMP0]]) : (tensor<?xf32>)
|
|
// CHECK-SCF: %[[UNSHAPED_RES:.*]] = "mhlo.sqrt"(%[[TMP1]]) : (tensor<?xf32>)
|
|
// CHECK-SCF: %[[RES_SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[RES:.*]] = "mhlo.dynamic_reshape"(%[[UNSHAPED_RES]], %[[RES_SHAPE]]) : (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
|
|
// CHECK-SCF: return %[[RES]]
|
|
|
|
// -----
|
|
|
|
// Don't cluster ranked operations.
|
|
// CHECK-LABEL: @sqrt_ranked
|
|
// CHECK-SAME: (%[[ARG:.*]]: tensor<3x?xf32>)
|
|
func @sqrt_ranked(%arg: tensor<3x?xf32>) -> tensor<3x?xf32> {
|
|
// CHECK-NOT: rank_specialization_cluster
|
|
%0 = "mhlo.sqrt"(%arg) : (tensor<3x?xf32>) -> tensor<3x?xf32>
|
|
%1 = "mhlo.sqrt"(%0) : (tensor<3x?xf32>) -> tensor<3x?xf32>
|
|
%2 = "mhlo.sqrt"(%1) : (tensor<3x?xf32>) -> tensor<3x?xf32>
|
|
return %2 : tensor<3x?xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @sqrt_ranked
|
|
// CHECK-SCF-NOT: dynamic_reshape
|
|
// CHECK-SCF: return
|
|
|
|
// -----
|
|
|
|
// Operation with mixed ranked and unranked operands.
|
|
// CHECK-LABEL: @select_mixed
|
|
// CHECK-SAME: (%[[PRED:.*]]: tensor<*xi1>, %[[ARG1:.*]]: tensor<*xf32>, %[[ARG2:.*]]: tensor<2xf32>)
|
|
func @select_mixed(%pred: tensor<*xi1>, %arg1: tensor<*xf32>,
|
|
%arg2: tensor<2xf32>) -> tensor<*xf32> {
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[PRED]], %[[ARG1]], %[[ARG2]])
|
|
// CHECK: ^bb0(%[[PRED_:.*]]: tensor<*xi1>, %[[ARG1_:.*]]: tensor<*xf32>, %[[ARG2_:.*]]: tensor<2xf32>)
|
|
// CHECK: %[[TMP:.*]] = chlo.broadcast_select %[[PRED_]], %[[ARG1_]], %[[ARG2_]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = "chlo.broadcast_select"(%pred, %arg1, %arg2)
|
|
: (tensor<*xi1>, tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
|
|
return %0 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @select_mixed
|
|
// CHECK-SCF: chlo.broadcast_select %{{.*}}, %{{.*}}, %{{.*}} : (tensor<?xi1>, tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF: return
|
|
|
|
// -----
|
|
|
|
// Unary CHLO operation.
|
|
// CHECK-LABEL: @tan
|
|
// CHECK-SAME: (%[[ARG:.*]]: tensor<*xf32>)
|
|
func @tan(%arg : tensor<*xf32>) -> tensor<*xf32> {
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG]]) ( {
|
|
// CHECK: ^bb0(%[[ARG_:.*]]: tensor<*xf32>)
|
|
// CHECK: %[[TMP0:.*]] = chlo.tan %[[ARG_]]
|
|
// CHECK: %[[TMP1:.*]] = chlo.tan %[[TMP0]]
|
|
// CHECK: %[[TMP2:.*]] = chlo.tan %[[TMP1]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP2]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = chlo.tan %arg : tensor<*xf32> -> tensor<*xf32>
|
|
%1 = chlo.tan %0 : tensor<*xf32> -> tensor<*xf32>
|
|
%2 = chlo.tan %1 : tensor<*xf32> -> tensor<*xf32>
|
|
return %2 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @tan
|
|
// CHECK-SCF-SAME: (%[[ARG:.*]]: tensor<*xf32>)
|
|
// CHECK-SCF: %[[SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[N:.*]] = shape.num_elements %[[SHAPE]]
|
|
// CHECK-SCF: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF: %[[FLAT_ARG:.*]] = "mhlo.dynamic_reshape"(%[[ARG]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
|
|
// CHECK-SCF: %[[TMP0:.*]] = chlo.tan %[[FLAT_ARG]] : tensor<?xf32>
|
|
// CHECK-SCF: %[[TMP1:.*]] = chlo.tan %[[TMP0]] : tensor<?xf32>
|
|
// CHECK-SCF: %[[UNSHAPED_RES:.*]] = chlo.tan %[[TMP1]] : tensor<?xf32>
|
|
// CHECK-SCF: %[[RES_SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[RES:.*]] = "mhlo.dynamic_reshape"(%[[UNSHAPED_RES]], %[[RES_SHAPE]]) : (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
|
|
// CHECK-SCF: return %[[RES]]
|
|
|
|
// -----
|
|
|
|
// Composition of unary/binary CHLO and unary MHLO ops.
|
|
// CHECK-LABEL: @mixed
|
|
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>, %[[ARG2:.*]]: tensor<*xf32>)
|
|
func @mixed(%arg0 : tensor<*xf32>, %arg1 : tensor<*xf32>, %arg2 : tensor<*xf32>)
|
|
-> tensor<*xf32> {
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG2]], %[[ARG1]], %[[ARG0]])
|
|
// CHECK: ^bb0(%[[ARG2_:.*]]: tensor<*xf32>, %[[ARG1_:.*]]: tensor<*xf32>, %[[ARG0_:.*]]: tensor<*xf32>)
|
|
// CHECK: %[[TMP0:.*]] = chlo.tan %[[ARG0_]]
|
|
// CHECK: %[[TMP1:.*]] = "mhlo.sqrt"(%[[ARG1_]])
|
|
// CHECK: %[[TMP2:.*]] = chlo.broadcast_multiply %[[TMP0]], %[[TMP1]]
|
|
// CHECK: %[[TMP3:.*]] = chlo.broadcast_add %[[TMP2]], %[[ARG2_]]
|
|
// CHECK: %[[TMP4:.*]] = "mhlo.sqrt"(%[[TMP3]])
|
|
// CHECK: %[[TMP5:.*]] = chlo.tan %[[TMP4]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP5]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = chlo.tan %arg0 : tensor<*xf32> -> tensor<*xf32>
|
|
%1 = "mhlo.sqrt"(%arg1) : (tensor<*xf32>) -> tensor<*xf32>
|
|
%2 = chlo.broadcast_multiply %0, %1
|
|
: (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
%3 = chlo.broadcast_add %2, %arg2
|
|
: (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
%4 = "mhlo.sqrt"(%3) : (tensor<*xf32>) -> tensor<*xf32>
|
|
%5 = chlo.tan %4 : tensor<*xf32> -> tensor<*xf32>
|
|
return %5 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @mixed
|
|
// CHECK-SCF-DAG: %[[TMP0:.*]] = chlo.tan %{{.*}} : tensor<?xf32>
|
|
// CHECK-SCF-DAG: %[[TMP1:.*]] = "mhlo.sqrt"(%{{.*}}) : (tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[TMP2:.*]] = chlo.broadcast_multiply %[[TMP0]], %[[TMP1]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[TMP3:.*]] = chlo.broadcast_add %[[TMP2]], %{{.*}} : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[TMP4:.*]] = "mhlo.sqrt"(%[[TMP3]]) : (tensor<?xf32>)
|
|
// CHECK-SCF: chlo.tan %[[TMP4]] : tensor<?xf32>
|
|
|
|
// -----
|
|
|
|
// Constant cluster operand.
|
|
// CHECK-LABEL: @relu
|
|
// CHECK-SAME: (%[[ARG:.*]]: tensor<*xf32>)
|
|
func @relu(%arg : tensor<*xf32>) -> tensor<*xf32> {
|
|
// CHECK: %[[C0:.*]] = mhlo.constant dense<0.000000e+00>
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG]], %[[C0]])
|
|
// CHECK: ^bb0(%[[ARG_:.*]]: tensor<*xf32>, %[[C0_:.*]]: tensor<f32>):
|
|
// CHECK: %[[TMP:.*]] = chlo.broadcast_maximum %[[ARG_]], %[[C0_]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
|
|
%1 = chlo.broadcast_maximum %0, %arg
|
|
: (tensor<f32>, tensor<*xf32>) -> tensor<*xf32>
|
|
return %1 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @relu
|
|
// CHECK-SCF-SAME: (%[[ARG:.*]]: tensor<*xf32>)
|
|
// CHECK-SCF: %[[C0:.*]] = mhlo.constant dense<0.000000e+00>
|
|
// CHECK-SCF: %[[SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[N:.*]] = shape.num_elements %[[SHAPE]]
|
|
// CHECK-SCF: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF: %[[FLAT_ARG:.*]] = "mhlo.dynamic_reshape"(%[[ARG]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
|
|
// CHECK-SCF: %[[UNSHAPED_RES:.*]] = chlo.broadcast_maximum %[[FLAT_ARG]], %[[C0]] : (tensor<?xf32>, tensor<f32>)
|
|
// CHECK-SCF: %[[RES_SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[RES:.*]] = "mhlo.dynamic_reshape"(%[[UNSHAPED_RES]], %[[RES_SHAPE]]) : (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
|
|
// CHECK-SCF: return %[[RES]]
|
|
|
|
// -----
|
|
|
|
// Cluster with binary non-broadcasting operation.
|
|
// CHECK-LABEL: @angle
|
|
// CHECK-SAME: (%[[ARG:.*]]: tensor<*xcomplex<f32>>)
|
|
func @angle(%arg : tensor<*xcomplex<f32>>) -> tensor<*xf32> {
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG]])
|
|
// CHECK: ^bb0(%[[ARG_:.*]]: tensor<*xcomplex<f32>>):
|
|
// CHECK: %[[IMAG:.*]] = "mhlo.imag"(%[[ARG_]])
|
|
// CHECK: %[[REAL:.*]] = "mhlo.real"(%[[ARG_]])
|
|
// CHECK: %[[TMP:.*]] = mhlo.atan2 %[[IMAG]], %[[REAL]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = "mhlo.imag"(%arg) : (tensor<*xcomplex<f32>>) -> tensor<*xf32>
|
|
%1 = "mhlo.real"(%arg) : (tensor<*xcomplex<f32>>) -> tensor<*xf32>
|
|
%2 = mhlo.atan2 %0, %1 : tensor<*xf32>
|
|
return %2 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @angle
|
|
// CHECK-SCF-SAME: (%[[ARG:.*]]: tensor<*xcomplex<f32>>)
|
|
// CHECK-SCF: %[[SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[N:.*]] = shape.num_elements %[[SHAPE]]
|
|
// CHECK-SCF: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF: %[[FLAT_ARG:.*]] = "mhlo.dynamic_reshape"(%[[ARG]], %[[FLAT_SHAPE]]) : (tensor<*xcomplex<f32>>, tensor<1xindex>) -> tensor<?xcomplex<f32>>
|
|
// CHECK-SCF: %[[IMAG:.*]] = "mhlo.imag"(%[[FLAT_ARG]]) : (tensor<?xcomplex<f32>>)
|
|
// CHECK-SCF: %[[REAL:.*]] = "mhlo.real"(%[[FLAT_ARG]]) : (tensor<?xcomplex<f32>>)
|
|
// CHECK-SCF: %[[UNSHAPED_RES:.*]] = mhlo.atan2 %[[IMAG]], %[[REAL]] : tensor<?xf32>
|
|
// CHECK-SCF: %[[RES_SHAPE:.*]] = shape.shape_of %[[ARG]]
|
|
// CHECK-SCF: %[[RES:.*]] = "mhlo.dynamic_reshape"(%[[UNSHAPED_RES]], %[[RES_SHAPE]]) : (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
|
|
// CHECK-SCF: return %[[RES]]
|
|
|
|
// -----
|
|
|
|
// Scalar cluster operand.
|
|
// CHECK-LABEL: @xlogy
|
|
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>)
|
|
func @xlogy(%arg0 : tensor<*xf32>, %arg1 : tensor<*xf32>) -> tensor<*xf32> {
|
|
// CHECK: %[[C0:.*]] = mhlo.constant dense<0.000000e+00>
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[C0]], %[[ARG0]], %[[ARG1]])
|
|
// CHECK: ^bb0(%[[C0_:.*]]: tensor<f32>, %[[ARG0_:.*]]: tensor<*xf32>, %[[ARG1_:.*]]: tensor<*xf32>):
|
|
// CHECK: %[[TMP0:.*]] = chlo.broadcast_compare %[[ARG0_]], %[[C0_]] {comparison_direction = "EQ"}
|
|
// CHECK: %[[TMP1:.*]] = "mhlo.log"(%[[ARG1_]])
|
|
// CHECK: %[[TMP2:.*]] = chlo.broadcast_multiply %[[ARG0_]], %[[TMP1]]
|
|
// CHECK: %[[TMP3:.*]] = chlo.broadcast_select %[[TMP0]], %[[C0_]], %[[TMP2]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP3]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
|
|
%1 = tensor.cast %0 : tensor<f32> to tensor<f32>
|
|
%2 = chlo.broadcast_compare %arg0, %1 {comparison_direction = "EQ"}
|
|
: (tensor<*xf32>, tensor<f32>) -> tensor<*xi1>
|
|
%3 = "mhlo.log"(%arg1) : (tensor<*xf32>) -> tensor<*xf32>
|
|
%4 = chlo.broadcast_multiply %arg0, %3
|
|
: (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
%5 = chlo.broadcast_select %2, %1, %4
|
|
: (tensor<*xi1>, tensor<f32>, tensor<*xf32>) -> tensor<*xf32>
|
|
return %5 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF: @xlogy
|
|
// CHECK-SCF-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>)
|
|
// CHECK-SCF-DAG: %[[C1:.*]] = constant 1
|
|
// CHECK-SCF-DAG: %[[ONE_SHAPE_1:.*]] = shape.const_shape [1]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG0:.*]] = shape.shape_of %[[ARG0]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG1:.*]] = shape.shape_of %[[ARG1]]
|
|
// CHECK-SCF-DAG: %[[ZERO:.*]] = mhlo.constant dense<0.00{{.*}}>
|
|
// Lhs scalar case:
|
|
// CHECK-SCF-DAG: %[[LHS_N:.*]] = shape.num_elements %[[SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[LHS_SCALAR:.*]] = cmpi eq, %[[LHS_N]], %[[C1]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES:.*]] = scf.if %[[LHS_SCALAR]]
|
|
// CHECK-SCF-DAG: %[[N:.*]] = shape.num_elements %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF-DAG: %[[FLAT_NON_SCALAR:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[SCALAR:.*]] = "mhlo.reshape"(%[[ARG0]])
|
|
// CHECK-SCF-DAG: %[[PRED:.*]] = chlo.broadcast_compare %[[SCALAR]], %[[ZERO]] {comparison_direction = "EQ"} : (tensor<f32>, tensor<f32>)
|
|
// CHECK-SCF-DAG: %[[TMP0:.*]] = "mhlo.log"(%[[FLAT_NON_SCALAR]]) : (tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[TMP1:.*]] = chlo.broadcast_multiply %[[SCALAR]], %[[TMP0]] : (tensor<f32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_select %[[PRED]], %[[ZERO]], %[[TMP1]] : (tensor<i1>, tensor<f32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Rhs scalar case:
|
|
// CHECK-SCF-DAG: %[[RHS_N:.*]] = shape.num_elements %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[RHS_SCALAR:.*]] = cmpi eq, %[[RHS_N]], %[[C1]]
|
|
// CHECK-SCF: %{{.*}} = scf.if %[[RHS_SCALAR]]
|
|
// CHECK-SCF-DAG: %[[N:.*]] = shape.num_elements %[[SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF-DAG: %[[FLAT_NON_SCALAR:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[SCALAR:.*]] = "mhlo.reshape"(%[[ARG1]])
|
|
// CHECK-SCF-DAG: %[[PRED:.*]] = chlo.broadcast_compare %[[FLAT_NON_SCALAR]], %[[ZERO]] {comparison_direction = "EQ"} : (tensor<?xf32>, tensor<f32>)
|
|
// CHECK-SCF-DAG: %[[TMP0:.*]] = "mhlo.log"(%[[SCALAR]]) : (tensor<f32>)
|
|
// CHECK-SCF-DAG: %[[TMP1:.*]] = chlo.broadcast_multiply %[[FLAT_NON_SCALAR]], %[[TMP0]] : (tensor<?xf32>, tensor<f32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_select %[[PRED]], %[[ZERO]], %[[TMP1]] : (tensor<?xi1>, tensor<f32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Equal shapes case:
|
|
// CHECK-SCF-DAG: %[[SHAPES_EQ:.*]] = shape.shape_eq %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF: %{{.*}} = scf.if %[[SHAPES_EQ]]
|
|
// CHECK-SCF-DAG: %[[SHAPE:.*]] = shape.any %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[N:.*]] = shape.num_elements %[[SHAPE]]
|
|
// CHECK-SCF-DAG: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF-DAG: %[[FLAT_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[FLAT_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[PRED:.*]] = chlo.broadcast_compare %[[FLAT_ARG0]], %[[ZERO]] {comparison_direction = "EQ"} : (tensor<?xf32>, tensor<f32>)
|
|
// CHECK-SCF-DAG: %[[TMP0:.*]] = "mhlo.log"(%[[FLAT_ARG1]]) : (tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[TMP1:.*]] = chlo.broadcast_multiply %[[FLAT_ARG0]], %[[TMP0]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_select %[[PRED]], %[[ZERO]], %[[TMP1]] : (tensor<?xi1>, tensor<f32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Find maximum reduced rank.
|
|
// CHECK-SCF-DAG: %[[REDUCED_SHAPES:.*]]:2 = chlo.minimum_broadcast_shapes %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_RANK0:.*]] = shape.rank %[[REDUCED_SHAPES]]#0
|
|
// CHECK-SCF-DAG: %[[REDUCED_RANK1:.*]] = shape.rank %[[REDUCED_SHAPES]]#1
|
|
// CHECK-SCF-DAG: %[[R0_GT_R1:.*]] = cmpi sgt, %[[REDUCED_RANK0]], %[[REDUCED_RANK1]]
|
|
// CHECK-SCF-DAG: %[[MAX_RED_RANK:.*]] = select %[[R0_GT_R1]], %[[REDUCED_RANK0]], %[[REDUCED_RANK1]]
|
|
// Generic case 1:
|
|
// CHECK-SCF: %[[MAX_RED_RANK_LE_1:.*]] = cmpi ule, %[[MAX_RED_RANK]], %[[C1]]
|
|
// CHECK-SCF: %{{.*}} = scf.if %[[MAX_RED_RANK_LE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#0, %[[ONE_SHAPE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#1, %[[ONE_SHAPE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0_:.*]] = tensor.cast %[[EXT_SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1_:.*]] = tensor.cast %[[EXT_SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[EXT_SHAPE_ARG0_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[EXT_SHAPE_ARG1_]])
|
|
// CHECK-SCF-DAG: %[[PRED:.*]] = chlo.broadcast_compare %[[REDUCED_ARG0]], %[[ZERO]] {comparison_direction = "EQ"} : (tensor<?xf32>, tensor<f32>)
|
|
// CHECK-SCF-DAG: %[[TMP0:.*]] = "mhlo.log"(%[[REDUCED_ARG1]]) : (tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[TMP1:.*]] = chlo.broadcast_multiply %[[REDUCED_ARG0]], %[[TMP0]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_select %[[PRED]], %[[ZERO]], %[[TMP1]] : (tensor<?xi1>, tensor<f32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// ...
|
|
// Reshape the result.
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG0:.*]] = shape.shape_of %[[ARG0]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG1:.*]] = shape.shape_of %[[ARG1]]
|
|
// CHECK-SCF-DAG: %[[RES_SHAPE:.*]] = shape.broadcast %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[RES:.*]] = "mhlo.dynamic_reshape"(%[[UNSHAPED_RES]], %[[RES_SHAPE]])
|
|
// CHECK-SCF: return %[[RES]]
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @mul
|
|
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>)
|
|
func @mul(%arg0 : tensor<*xf32>, %arg1 : tensor<*xf32>) -> tensor<*xf32> {
|
|
// CHECK: %[[RES:.*]] = "chlo.rank_specialization_cluster"(%[[ARG0]], %[[ARG1]])
|
|
// CHECK: ^bb0(%[[ARG0_:.*]]: tensor<*xf32>, %[[ARG1_:.*]]: tensor<*xf32>):
|
|
// CHECK: %[[TMP:.*]] = chlo.broadcast_multiply %[[ARG0_]], %[[ARG1_]]
|
|
// CHECK: "chlo.rank_specialization_cluster_yield"(%[[TMP]])
|
|
// CHECK: return %[[RES]]
|
|
%0 = chlo.broadcast_multiply %arg0, %arg1 : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
return %0 : tensor<*xf32>
|
|
}
|
|
|
|
// CHECK-SCF-LABEL: @mul
|
|
// CHECK-SCF-SAME: (%[[ARG0:.*]]: tensor<*xf32>, %[[ARG1:.*]]: tensor<*xf32>)
|
|
// CHECK-SCF-DAG: %[[C1:.*]] = constant 1
|
|
// CHECK-SCF-DAG: %[[C2:.*]] = constant 2
|
|
// CHECK-SCF-DAG: %[[C3:.*]] = constant 3
|
|
// CHECK-SCF-DAG: %[[ONE_SHAPE_1:.*]] = shape.const_shape [1]
|
|
// CHECK-SCF-DAG: %[[ONE_SHAPE_2:.*]] = shape.const_shape [1, 1]
|
|
// CHECK-SCF-DAG: %[[ONE_SHAPE_3:.*]] = shape.const_shape [1, 1, 1]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG0:.*]] = shape.shape_of %[[ARG0]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG1:.*]] = shape.shape_of %[[ARG1]]
|
|
// Lhs scalar case:
|
|
// CHECK-SCF-DAG: %[[LHS_N:.*]] = shape.num_elements %[[SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[LHS_SCALAR:.*]] = cmpi eq, %[[LHS_N]], %[[C1]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_LHS_SCALAR:.*]] = scf.if %[[LHS_SCALAR]]
|
|
// CHECK-SCF-DAG: %[[N:.*]] = shape.num_elements %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF-DAG: %[[FLAT_NON_SCALAR:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[SCALAR:.*]] = "mhlo.reshape"(%[[ARG0]])
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_multiply %[[SCALAR]], %[[FLAT_NON_SCALAR]] : (tensor<f32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Rhs scalar case:
|
|
// CHECK-SCF-DAG: %[[RHS_N:.*]] = shape.num_elements %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[RHS_SCALAR:.*]] = cmpi eq, %[[RHS_N]], %[[C1]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_RHS_SCALAR:.*]] = scf.if %[[RHS_SCALAR]]
|
|
// CHECK-SCF-DAG: %[[N:.*]] = shape.num_elements %[[SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF-DAG: %[[FLAT_NON_SCALAR:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[SCALAR:.*]] = "mhlo.reshape"(%[[ARG1]])
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_multiply %[[FLAT_NON_SCALAR]], %[[SCALAR]] : (tensor<?xf32>, tensor<f32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Equal shapes case:
|
|
// CHECK-SCF-DAG: %[[SHAPES_EQ:.*]] = shape.shape_eq %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_EQ_SHAPES:.*]] = scf.if %[[SHAPES_EQ]]
|
|
// CHECK-SCF-DAG: %[[SHAPE:.*]] = shape.any %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[N:.*]] = shape.num_elements %[[SHAPE]]
|
|
// CHECK-SCF-DAG: %[[FLAT_SHAPE:.*]] = tensor.from_elements %[[N]]
|
|
// CHECK-SCF-DAG: %[[FLAT_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[FLAT_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[FLAT_SHAPE]])
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_multiply %[[FLAT_ARG0]], %[[FLAT_ARG1]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Find maximum reduced rank.
|
|
// CHECK-SCF-DAG: %[[REDUCED_SHAPES:.*]]:2 = chlo.minimum_broadcast_shapes %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_RANK0:.*]] = shape.rank %[[REDUCED_SHAPES]]#0
|
|
// CHECK-SCF-DAG: %[[REDUCED_RANK1:.*]] = shape.rank %[[REDUCED_SHAPES]]#1
|
|
// CHECK-SCF-DAG: %[[R0_GT_R1:.*]] = cmpi sgt, %[[REDUCED_RANK0]], %[[REDUCED_RANK1]]
|
|
// CHECK-SCF-DAG: %[[MAX_RED_RANK:.*]] = select %[[R0_GT_R1]], %[[REDUCED_RANK0]], %[[REDUCED_RANK1]]
|
|
// Generic case 1:
|
|
// CHECK-SCF: %[[MAX_RED_RANK_LE_1:.*]] = cmpi ule, %[[MAX_RED_RANK]], %[[C1]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_1:.*]] = scf.if %[[MAX_RED_RANK_LE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#0, %[[ONE_SHAPE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#1, %[[ONE_SHAPE_1]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0_:.*]] = tensor.cast %[[EXT_SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1_:.*]] = tensor.cast %[[EXT_SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[EXT_SHAPE_ARG0_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[EXT_SHAPE_ARG1_]])
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_multiply %[[REDUCED_ARG0]], %[[REDUCED_ARG1]] : (tensor<?xf32>, tensor<?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Generic case 2:
|
|
// CHECK-SCF: %[[MAX_RED_RANK_LE_2:.*]] = cmpi ule, %[[MAX_RED_RANK]], %[[C2]]
|
|
// CHECK-SCF: %[[UNSHAPED_RES_2:.*]] = scf.if %[[MAX_RED_RANK_LE_2]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#0, %[[ONE_SHAPE_2]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#1, %[[ONE_SHAPE_2]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0_:.*]] = tensor.cast %[[EXT_SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1_:.*]] = tensor.cast %[[EXT_SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[EXT_SHAPE_ARG0_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[EXT_SHAPE_ARG1_]])
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_multiply %[[REDUCED_ARG0]], %[[REDUCED_ARG1]] : (tensor<?x?xf32>, tensor<?x?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: else
|
|
// Generic case 3:
|
|
// CHECK-SCF: %[[MAX_RED_RANK_LE_3:.*]] = cmpi ule, %[[MAX_RED_RANK]], %[[C3]]
|
|
// CHECK-SCF: assert %[[MAX_RED_RANK_LE_3]], "Input for dynamic binary or n-ary op lowering was of a rank greater than 3"
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#0, %[[ONE_SHAPE_3]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1:.*]] = shape.broadcast %[[REDUCED_SHAPES]]#1, %[[ONE_SHAPE_3]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG0_:.*]] = tensor.cast %[[EXT_SHAPE_ARG0]]
|
|
// CHECK-SCF-DAG: %[[EXT_SHAPE_ARG1_:.*]] = tensor.cast %[[EXT_SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG0:.*]] = "mhlo.dynamic_reshape"(%[[ARG0]], %[[EXT_SHAPE_ARG0_]])
|
|
// CHECK-SCF-DAG: %[[REDUCED_ARG1:.*]] = "mhlo.dynamic_reshape"(%[[ARG1]], %[[EXT_SHAPE_ARG1_]])
|
|
// CHECK-SCF-DAG: %[[INNER_RES:.*]] = chlo.broadcast_multiply %[[REDUCED_ARG0]], %[[REDUCED_ARG1]] : (tensor<?x?x?xf32>, tensor<?x?x?xf32>)
|
|
// CHECK-SCF-DAG: %[[INNER_RES_:.*]] = tensor.cast %[[INNER_RES]]
|
|
// CHECK-SCF: scf.yield %[[INNER_RES_]]
|
|
// CHECK-SCF: scf.yield %[[UNSHAPED_RES_2]]
|
|
// CHECK-SCF: scf.yield %[[UNSHAPED_RES_1]]
|
|
// CHECK-SCF: scf.yield %[[UNSHAPED_RES_EQ_SHAPES]]
|
|
// CHECK-SCF: scf.yield %[[UNSHAPED_RES_RHS_SCALAR]]
|
|
// Reshape the result.
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG0:.*]] = shape.shape_of %[[ARG0]]
|
|
// CHECK-SCF-DAG: %[[SHAPE_ARG1:.*]] = shape.shape_of %[[ARG1]]
|
|
// CHECK-SCF-DAG: %[[RES_SHAPE:.*]] = shape.broadcast %[[SHAPE_ARG0]], %[[SHAPE_ARG1]]
|
|
// CHECK-SCF-DAG: %[[RES:.*]] = "mhlo.dynamic_reshape"(%[[UNSHAPED_RES_LHS_SCALAR]], %[[RES_SHAPE]])
|
|
// CHECK-SCF: return %[[RES]]
|