65 lines
3.0 KiB
MLIR
65 lines
3.0 KiB
MLIR
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// RUN: mlir-hlo-opt %s -pass-pipeline='func(mhlo-test-optimize)' | FileCheck %s
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// CHECK-LABEL: @gather_is_slice_no_rank
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func @gather_is_slice_no_rank(%arg0: tensor<2x1x2xi32>, %arg1: tensor<i64>) -> tensor<1x2xi32> {
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// CHECK: [[CST:%.+]] = mhlo.constant dense<0> : tensor<i64>
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// CHECK: [[SLICE:%.+]] = "mhlo.dynamic-slice"(%arg0, %arg1, [[CST]], [[CST]]) {slice_sizes = dense<[1, 1, 2]> : tensor<3xi64>}
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// CHECK: [[RESHAPE:%.+]] = "mhlo.reshape"([[SLICE]])
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%res = "mhlo.gather"(%arg0, %arg1) {
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dimension_numbers = {
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collapsed_slice_dims = dense<0> : tensor<1xi64>,
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index_vector_dim = 0 : i64,
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offset_dims = dense<[0, 1]> : tensor<2xi64>,
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start_index_map = dense<0> : tensor<1xi64>
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},
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slice_sizes = dense<[1, 1, 2]> : tensor<3xi64>
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} : (tensor<2x1x2xi32>, tensor<i64>) -> tensor<1x2xi32>
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// CHECK: return [[RESHAPE]]
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return %res : tensor<1x2xi32>
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}
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// CHECK-LABEL: @gather_is_slice
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func @gather_is_slice(%arg0: tensor<2x1x2xi32>, %arg1: tensor<1xi64>) -> tensor<1x2xi32> {
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// CHECK: [[CST:%.+]] = mhlo.constant dense<0> : tensor<i64>
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// CHECK: [[RESHAPE:%.+]] = "mhlo.reshape"(%arg1)
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// CHECK: [[SLICE:%.+]] = "mhlo.dynamic-slice"(%arg0, [[RESHAPE]], [[CST]], [[CST]]) {slice_sizes = dense<[1, 1, 2]> : tensor<3xi64>}
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// CHECK: [[RES:%.+]] = "mhlo.reshape"([[SLICE]])
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%res = "mhlo.gather"(%arg0, %arg1) {
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dimension_numbers = {
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collapsed_slice_dims = dense<0> : tensor<1xi64>,
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index_vector_dim = 0 : i64,
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offset_dims = dense<[0, 1]> : tensor<2xi64>,
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start_index_map = dense<0> : tensor<1xi64>
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},
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slice_sizes = dense<[1, 1, 2]> : tensor<3xi64>
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} : (tensor<2x1x2xi32>, tensor<1xi64>) -> tensor<1x2xi32>
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// CHECK: return [[RES]]
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return %res : tensor<1x2xi32>
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}
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// CHECK-LABEL: @gather_is_slice_multiple_start_indices
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func @gather_is_slice_multiple_start_indices(%arg0: tensor<2x1x2xi32>, %arg1: tensor<2xi64>) -> tensor<1x2xi32> {
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// CHECK-DAG: [[CST:%.+]] = mhlo.constant dense<0>
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// CHECK-DAG: [[SLICE1:%.+]] = "mhlo.slice"(%arg1) {limit_indices = dense<1> : tensor<1xi64>, start_indices = dense<0> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
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// CHECK-DAG: [[RESHAPE1:%.+]] = "mhlo.reshape"([[SLICE1]])
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// CHECK-DAG: [[SLICE2:%.+]] = "mhlo.slice"(%arg1) {limit_indices = dense<2> : tensor<1xi64>, start_indices = dense<1> : tensor<1xi64>, strides = dense<1> : tensor<1xi64>}
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// CHECK-DAG: [[RESHAPE2:%.+]] = "mhlo.reshape"([[SLICE2]])
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// CHECK-DAG: [[DSLICE:%.+]] = "mhlo.dynamic-slice"(%arg0, [[RESHAPE1]], [[RESHAPE2]], [[CST]]) {slice_sizes = dense<[1, 1, 2]> : tensor<3xi64>}
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// CHECK-DAG: [[RES:%.+]] = "mhlo.reshape"([[DSLICE]])
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%res = "mhlo.gather"(%arg0, %arg1) {
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dimension_numbers = {
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collapsed_slice_dims = dense<0> : tensor<1xi64>,
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index_vector_dim = 0 : i64,
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offset_dims = dense<[0, 1]> : tensor<2xi64>,
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start_index_map = dense<0> : tensor<1xi64>
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},
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slice_sizes = dense<[1, 1, 2]> : tensor<3xi64>
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} : (tensor<2x1x2xi32>, tensor<2xi64>) -> tensor<1x2xi32>
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// CHECK: return [[RES]]
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return %res : tensor<1x2xi32>
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}
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