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mlir-hlo/tests/chlo_legalize_to_mhlo.mlir

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[MLIR][KernelGen] Fix unranked codegeneration in kernel generator PiperOrigin-RevId: 335847086
2020-10-07 20:39:04 +08:00
// RUN: mlir-hlo-opt --chlo-legalize-to-hlo --split-input-file %s | FileCheck %s
[MLIR] Lower `chlo.constant_like` to MHLO Lower `chlo.constant_like` to a constant and, if needed, a broadcast. PiperOrigin-RevId: 331964137
2020-09-16 17:41:02 +08:00
// Lower statically shaped `constant_like` to constant.
// CHECK-LABEL: @constant_like_static_shape
func @constant_like_static_shape(%arg : tensor<1x2xi64>) -> tensor<1x2xf32> {
// CHECK: %[[RESULT:.*]] = mhlo.constant dense<3.200000e+00> : tensor<1x2xf32>
// CHECK: return %[[RESULT]]
%result = "chlo.constant_like"(%arg) { value = 3.2 : f32 }
: (tensor<1x2xi64>) -> tensor<1x2xf32>
return %result : tensor<1x2xf32>
}
// Lower dynamically shaped `constant_like` to broadcasted constant.
// CHECK-LABEL: constant_like_dynamic_shape
// CHECK-SAME: (%[[ARG:.*]]: tensor<?x?xi64>)
func @constant_like_dynamic_shape(%arg : tensor<?x?xi64>) -> tensor<?x?xf32> {
// CHECK: %[[CONSTANT:.*]] = mhlo.constant dense<3.200000e+00> : tensor<f32>
// CHECK: %[[UNCASTED_SHAPE:.*]] = shape.shape_of %[[ARG]] : tensor<?x?xi64> -> tensor<?xindex>
Integrate LLVM at llvm/llvm-project@c3acda0798f9 Updates LLVM usage to match [c3acda0798f9](https://github.com/llvm/llvm-project/commit/c3acda0798f9) PiperOrigin-RevId: 348896724
2020-12-24 15:53:08 +08:00
// CHECK: %[[SHAPE:.*]] = tensor.cast %[[UNCASTED_SHAPE]] : tensor<?xindex> to tensor<2xindex>
[MLIR] Lower `chlo.constant_like` to MHLO Lower `chlo.constant_like` to a constant and, if needed, a broadcast. PiperOrigin-RevId: 331964137
2020-09-16 17:41:02 +08:00
// CHECK: %[[BROADCASTED_CONSTANT:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[CONSTANT]], %[[SHAPE]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<2xindex>) -> tensor<?x?xf32>
// CHECK: return %[[BROADCASTED_CONSTANT]] : tensor<?x?xf32>
%result = "chlo.constant_like"(%arg) { value = 3.2 : f32 }
: (tensor<?x?xi64>) -> tensor<?x?xf32>
return %result : tensor<?x?xf32>
}
[KERNEL_GEN] Add unranked Conj kernel. PiperOrigin-RevId: 344243271
2020-11-25 22:36:42 +08:00
// CHECK-LABEL: func @conj
func @conj(%arg0: tensor<3xcomplex<f32>>) -> tensor<3xcomplex<f32>> {
// CHECK-SAME: ([[INPUT:%.*]]: tensor
// CHECK-NEXT: [[R1:%.*]] = "mhlo.real"([[INPUT]])
// CHECK-NEXT: [[R2:%.*]] = "mhlo.imag"([[INPUT]])
// CHECK-NEXT: [[R3:%.*]] = "mhlo.negate"([[R2]])
// CHECK-NEXT: [[R4:%.*]] = "mhlo.complex"([[R1]], [[R3]])
%1 = "chlo.conj"(%arg0) : (tensor<3xcomplex<f32>>) -> tensor<3xcomplex<f32>>
return %1 : tensor<3xcomplex<f32>>
}
[MLIR][KernelGen] Add erf kernel for f32 arguments and missing lowerings PiperOrigin-RevId: 352381016
2021-01-18 19:34:19 +08:00
// CHECK-LABEL: @erf_f32
// CHECK-SAME: %[[ARG:.*]]: tensor<f32>
func @erf_f32(%arg : tensor<f32>) -> tensor<f32> {
// CHECK: %[[TMP_0:.*]] = mhlo.constant dense<-4.000000e+00>
// CHECK: %[[TMP_1:.*]] = mhlo.constant dense<4.000000e+00>
// CHECK: %[[TMP_2:.*]] = "mhlo.clamp"(%[[TMP_0]], %[[ARG]], %[[TMP_1]])
// CHECK: %[[TMP_3:.*]] = mhlo.multiply %[[TMP_2]], %[[TMP_2]]
// CHECK: %[[TMP_4:.*]] = mhlo.constant dense<0.000000e+00>
// CHECK: %[[TMP_5:.*]] = mhlo.multiply %[[TMP_4]], %[[TMP_3]]
// CHECK: %[[TMP_6:.*]] = mhlo.constant dense<-2.72614237E-10>
// CHECK: %[[TMP_7:.*]] = mhlo.add %[[TMP_5]], %[[TMP_6]]
// CHECK: %[[TMP_8:.*]] = mhlo.multiply %[[TMP_7]], %[[TMP_3]]
// CHECK: %[[TMP_9:.*]] = mhlo.constant dense<2.77068146E-8>
// CHECK: %[[TMP_10:.*]] = mhlo.add %[[TMP_8]], %[[TMP_9]]
// CHECK: %[[TMP_11:.*]] = mhlo.multiply %[[TMP_10]], %[[TMP_3]]
// CHECK: %[[TMP_12:.*]] = mhlo.constant dense<-2.10102394E-6>
// CHECK: %[[TMP_13:.*]] = mhlo.add %[[TMP_11]], %[[TMP_12]]
// CHECK: %[[TMP_14:.*]] = mhlo.multiply %[[TMP_13]], %[[TMP_3]]
// CHECK: %[[TMP_15:.*]] = mhlo.constant dense<-5.69250624E-5>
// CHECK: %[[TMP_16:.*]] = mhlo.add %[[TMP_14]], %[[TMP_15]]
// CHECK: %[[TMP_17:.*]] = mhlo.multiply %[[TMP_16]], %[[TMP_3]]
// CHECK: %[[TMP_18:.*]] = mhlo.constant dense<-7.34990637E-4>
// CHECK: %[[TMP_19:.*]] = mhlo.add %[[TMP_17]], %[[TMP_18]]
// CHECK: %[[TMP_20:.*]] = mhlo.multiply %[[TMP_19]], %[[TMP_3]]
// CHECK: %[[TMP_21:.*]] = mhlo.constant dense<-2.954600e-03>
// CHECK: %[[TMP_22:.*]] = mhlo.add %[[TMP_20]], %[[TMP_21]]
// CHECK: %[[TMP_23:.*]] = mhlo.multiply %[[TMP_22]], %[[TMP_3]]
// CHECK: %[[TMP_24:.*]] = mhlo.constant dense<-0.0160960332>
// CHECK: %[[TMP_25:.*]] = mhlo.add %[[TMP_23]], %[[TMP_24]]
// CHECK: %[[TMP_26:.*]] = mhlo.constant dense<0.000000e+00>
// CHECK: %[[TMP_27:.*]] = mhlo.multiply %[[TMP_26]], %[[TMP_3]]
// CHECK: %[[TMP_28:.*]] = mhlo.constant dense<-1.45660715E-5>
// CHECK: %[[TMP_29:.*]] = mhlo.add %[[TMP_27]], %[[TMP_28]]
// CHECK: %[[TMP_30:.*]] = mhlo.multiply %[[TMP_29]], %[[TMP_3]]
// CHECK: %[[TMP_31:.*]] = mhlo.constant dense<-2.13374049E-4>
// CHECK: %[[TMP_32:.*]] = mhlo.add %[[TMP_30]], %[[TMP_31]]
// CHECK: %[[TMP_33:.*]] = mhlo.multiply %[[TMP_32]], %[[TMP_3]]
// CHECK: %[[TMP_34:.*]] = mhlo.constant dense<-0.00168282702>
// CHECK: %[[TMP_35:.*]] = mhlo.add %[[TMP_33]], %[[TMP_34]]
// CHECK: %[[TMP_36:.*]] = mhlo.multiply %[[TMP_35]], %[[TMP_3]]
// CHECK: %[[TMP_37:.*]] = mhlo.constant dense<-0.00737332925>
// CHECK: %[[TMP_38:.*]] = mhlo.add %[[TMP_36]], %[[TMP_37]]
// CHECK: %[[TMP_39:.*]] = mhlo.multiply %[[TMP_38]], %[[TMP_3]]
// CHECK: %[[TMP_40:.*]] = mhlo.constant dense<-0.0142647391>
// CHECK: %[[TMP_41:.*]] = mhlo.add %[[TMP_39]], %[[TMP_40]]
// CHECK: %[[TMP_42:.*]] = mhlo.multiply %[[TMP_2]], %[[TMP_25]]
// CHECK: %[[RESULT:.*]] = mhlo.divide %[[TMP_42]], %[[TMP_41]]
// CHECK: return %[[RESULT]]
%1 = "chlo.erf"(%arg) : (tensor<f32>) -> tensor<f32>
return %1 : tensor<f32>
}