experiments/STT-tensorflow
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[XLA] Constrain slice start and limits to be the same type

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Most importantly, ensures these are the same size.

PiperOrigin-RevId: 261786941
This commit is contained in:
A. Unique TensorFlower 2019-08-05 15:48:43 -07:00 committed by TensorFlower Gardener
parent 66ba95a89c
commit 3f0d7f179c
2 changed files with 29 additions and 3 deletions
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6
tensorflow/compiler/mlir/xla/ir/xla_ops.td
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@ -433,8 +433,10 @@ def XLA_CompareOp: XLA_Op<"compare",
// XLA Slice definitions. // XLA Slice definitions.
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
def XLA_SliceOp: XLA_UnaryElementwiseOp<"slice", def XLA_SliceOp: XLA_UnaryElementwiseOp<
[NoSideEffect, SameOperandsAndResultElementType]> { "slice",
[NoSideEffect, SameOperandsAndResultElementType,
AllTypesMatch<["start_indices", "limit_indices"]>]> {
let arguments = ( let arguments = (
ins XLA_Tensor:$operand, ins XLA_Tensor:$operand,
ElementsAttr:$start_indices, ElementsAttr:$start_indices,

26
tensorflow/compiler/mlir/xla/tests/ops.mlir
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@ -394,8 +394,32 @@ func @select_bad_pred_shape(%arg0: tensor<3xi1>, %arg1: tensor<2x3xi32>, %arg2:
// ----- // -----
// CHECK-LABEL: func @slice
func @slice(%arg0: tensor<3x4xi32>) -> tensor<1x4xi32> {
%0 = "xla_hlo.slice"(%arg0) {start_indices = dense<[1, 0]> : tensor<2xi64>, limit_indices = dense<[2, 4]> : tensor<2xi64>} : (tensor<3x4xi32>) -> tensor<1x4xi32>
return %0 : tensor<1x4xi32>
}
// -----
func @slice_indices_mismatch(%arg0: tensor<3x4xi32>) -> tensor<1x4xi32> {
// expected-error@+1 {{failed to verify that all of {start_indices, limit_indices} have same type}}
%0 = "xla_hlo.slice"(%arg0) {start_indices = dense<[1, 2, 3]> : tensor<3xi64>, limit_indices = dense<[2, 4]> : tensor<2xi64>} : (tensor<3x4xi32>) -> tensor<1x4xi32>
return %0 : tensor<1x4xi32>
}
// -----
func @slice_operand_result_mismatch(%arg0: tensor<3x4xi32>) -> tensor<1x4xf32> {
// expected-error@+1 {{requires the same element type for all operands and results}}
%0 = "xla_hlo.slice"(%arg0) {start_indices = dense<[1, 0]> : tensor<2xi64>, limit_indices = dense<[2, 4]> : tensor<2xi64>} : (tensor<3x4xi32>) -> tensor<1x4xf32>
return %0 : tensor<1x4xf32>
}
// -----
// CHECK-LABEL: func @transpose // CHECK-LABEL: func @transpose
func @transpose(%arg0: tensor<1x2x3x4xi32>) -> tensor<2x1x4x3xi32> { func @transpose(%arg0: tensor<1x2x3x4xi32>) -> tensor<2x1x4x3xi32> {
%0 = "xla_hlo.transpose"(%arg0) {permutation = dense<[1, 0, 3, 2]> : tensor<4xi64>} : (tensor<1x2x3x4xi32>) -> tensor<2x1x4x3xi32> %0 = "xla_hlo.transpose"(%arg0) {permutation = dense<[1, 0, 3, 2]> : tensor<4xi64>} : (tensor<1x2x3x4xi32>) -> tensor<2x1x4x3xi32>
return %0: tensor<2x1x4x3xi32> return %0: tensor<2x1x4x3xi32>
} }