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Integrate LLVM at https://github.com/llvm/llvm-project/commit/f233b92f92a6 

PiperOrigin-RevId: 321454533
This commit is contained in:
A. Unique TensorFlower 2020-07-15 22:48:16 +00:00 committed by Mehdi Amini
parent 98a1e3b108
commit c8bb0ff54d
2 changed files with 4 additions and 4 deletions
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2
tests/chlo_infer_shape_type_methods.mlir
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@ -8,7 +8,7 @@
func @broadcast_add(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<1xindex> { func @broadcast_add(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<1xindex> {
// CHECK-DAG: %[[ARG0_S:.+]] = shape.shape_of %[[ARG0]] // CHECK-DAG: %[[ARG0_S:.+]] = shape.shape_of %[[ARG0]]
// CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]] // CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]]
// CHECK-DAG: %[[BCAST_S:.+]] = "shape.broadcast"(%[[ARG0_S]], %[[ARG1_S]]) // CHECK-DAG: %[[BCAST_S:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]]
// CHECK: %[[EXTENTS:.+]] = shape.to_extent_tensor %[[BCAST_S]] // CHECK: %[[EXTENTS:.+]] = shape.to_extent_tensor %[[BCAST_S]]
// CHECK: return %[[EXTENTS]] // CHECK: return %[[EXTENTS]]
%0 = chlo.broadcast_add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32> %0 = chlo.broadcast_add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>

6
tests/chlo_legalize_to_hlo_broadcasts.mlir
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@ -18,7 +18,7 @@ func @dynamicBroadcast(%arg0: tensor<?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?
// CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]] // CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]]
// CHECK-NEXT: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[ARG0_S]], %[[ARG1_S]] // CHECK-NEXT: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[ARG0_S]], %[[ARG1_S]]
// CHECK-NEXT: %[[FINAL_RESULT:.+]] = shape.assuming %[[WITNESS]] // CHECK-NEXT: %[[FINAL_RESULT:.+]] = shape.assuming %[[WITNESS]]
// CHECK-DAG: %[[RESULT_S:.+]] = "shape.broadcast"(%[[ARG0_S]], %[[ARG1_S]]) // CHECK-DAG: %[[RESULT_S:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]]
// CHECK: %[[RESULT_EXTENTS:.+]] = shape.to_extent_tensor %[[RESULT_S]] // CHECK: %[[RESULT_EXTENTS:.+]] = shape.to_extent_tensor %[[RESULT_S]]
// CHECK-DAG: %[[ARG0_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} // CHECK-DAG: %[[ARG0_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<1> : tensor<1xi64>}
// CHECK-DAG: %[[ARG1_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} // CHECK-DAG: %[[ARG1_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}
@ -39,7 +39,7 @@ func @dynamicBroadcastComplex(%arg0: tensor<?xf32>, %arg1: tensor<?x?xf32>) -> t
// CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]] // CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]]
// CHECK-NEXT: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[ARG0_S]], %[[ARG1_S]] // CHECK-NEXT: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[ARG0_S]], %[[ARG1_S]]
// CHECK-NEXT: %[[FINAL_RESULT:.+]] = shape.assuming %[[WITNESS]] // CHECK-NEXT: %[[FINAL_RESULT:.+]] = shape.assuming %[[WITNESS]]
// CHECK-NEXT: %[[RESULT_S:.+]] = "shape.broadcast"(%[[ARG0_S]], %[[ARG1_S]]) // CHECK-NEXT: %[[RESULT_S:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]]
// CHECK-NEXT: %[[RESULT_EXTENTS:.+]] = shape.to_extent_tensor %[[RESULT_S]] // CHECK-NEXT: %[[RESULT_EXTENTS:.+]] = shape.to_extent_tensor %[[RESULT_S]]
// CHECK-DAG: %[[ARG0_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<?xf32>, tensor<2xindex>) -> tensor<?x?xf32> // CHECK-DAG: %[[ARG0_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<?xf32>, tensor<2xindex>) -> tensor<?x?xf32>
// CHECK-DAG: %[[ARG1_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<?x?xf32>, tensor<2xindex>) -> tensor<?x?xf32> // CHECK-DAG: %[[ARG1_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<?x?xf32>, tensor<2xindex>) -> tensor<?x?xf32>
@ -60,7 +60,7 @@ func @dynamicBroadcastCompare(%arg0: tensor<?xf32>, %arg1: tensor<?x?xf32>) -> t
// CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]] // CHECK-DAG: %[[ARG1_S:.+]] = shape.shape_of %[[ARG1]]
// CHECK: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[ARG0_S]], %[[ARG1_S]] // CHECK: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[ARG0_S]], %[[ARG1_S]]
// CHECK: %[[FINAL_RESULT:.+]] = shape.assuming %[[WITNESS]] // CHECK: %[[FINAL_RESULT:.+]] = shape.assuming %[[WITNESS]]
// CHECK: %[[RESULT_S:.+]] = "shape.broadcast"(%[[ARG0_S]], %[[ARG1_S]]) // CHECK: %[[RESULT_S:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]]
// CHECK: %[[RESULT_EXTENTS:.+]] = shape.to_extent_tensor %[[RESULT_S]] // CHECK: %[[RESULT_EXTENTS:.+]] = shape.to_extent_tensor %[[RESULT_S]]
// CHECK-DAG: %[[ARG0_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<?xf32>, tensor<2xindex>) -> tensor<?x?xf32> // CHECK-DAG: %[[ARG0_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<?xf32>, tensor<2xindex>) -> tensor<?x?xf32>
// CHECK-DAG: %[[ARG1_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<?x?xf32>, tensor<2xindex>) -> tensor<?x?xf32> // CHECK-DAG: %[[ARG1_B:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %[[RESULT_EXTENTS]]) {broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<?x?xf32>, tensor<2xindex>) -> tensor<?x?xf32>