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Integrate LLVM at llvm/llvm-project@5c7b43aa82 

Updates LLVM usage to match
[5c7b43aa8298](https://github.com/llvm/llvm-project/commit/5c7b43aa8298)

PiperOrigin-RevId: 373028739
This commit is contained in:
A. Unique TensorFlower 2021-05-10 15:45:29 -07:00 committed by TensorFlow MLIR Team
parent 2a4c63d949
commit 2af1796194
3 changed files with 7 additions and 11 deletions
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4
WORKSPACE
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@ -15,9 +15,9 @@
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
LLVM_COMMIT = "9ba661f91276dd8cc728f9b2e82905b78c0119b4" LLVM_COMMIT = "5c7b43aa8298a389b906d72c792941a0ce57782e"
LLVM_SHA256 = "f89c033b0e8e6d4e6ff5ce3883aadc82a502b063a830cd685672cec4bea3dfb1" LLVM_SHA256 = "e34534a864e2bedaff6811effb757d2eed3a50c9c1e540515ed1568addf1815d"
LLVM_BAZEL_TAG = "llvm-project-{commit}".format(commit = LLVM_COMMIT) LLVM_BAZEL_TAG = "llvm-project-{commit}".format(commit = LLVM_COMMIT)

2
build_tools/llvm_version.txt
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@ -1,2 +1,2 @@
9ba661f91276dd8cc728f9b2e82905b78c0119b4 5c7b43aa8298a389b906d72c792941a0ce57782e

12
tests/chlo_legalize_to_hlo_broadcasts.mlir
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@ -18,8 +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_EXTENTS:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]]
// CHECK: %[[RESULT_EXTENTS:.+]] = tensor.cast %[[RESULT_S]] : tensor<?xindex> to tensor<2xindex>
// 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>}
// CHECK-NEXT: %[[RESULT:.+]] = mhlo.add %[[ARG0_B]], %[[ARG1_B]] // CHECK-NEXT: %[[RESULT:.+]] = mhlo.add %[[ARG0_B]], %[[ARG1_B]]
@ -39,8 +38,7 @@ 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_EXTENTS:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]]
// CHECK-NEXT: %[[RESULT_EXTENTS:.+]] = tensor.cast %[[RESULT_S]] : tensor<?xindex> to tensor<2xindex>
// 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>
// CHECK-NEXT: %[[RESULT:.+]] = "mhlo.complex"(%[[ARG0_B]], %[[ARG1_B]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xcomplex<f32>> // CHECK-NEXT: %[[RESULT:.+]] = "mhlo.complex"(%[[ARG0_B]], %[[ARG1_B]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xcomplex<f32>>
@ -60,8 +58,7 @@ 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_EXTENTS:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]]
// CHECK: %[[RESULT_EXTENTS:.+]] = tensor.cast %[[RESULT_S]] : tensor<?xindex> to tensor<2xindex>
// 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>
// CHECK: %[[RESULT:.+]] = "mhlo.compare"(%[[ARG0_B]], %[[ARG1_B]]) {comparison_direction = "EQ"} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1> // CHECK: %[[RESULT:.+]] = "mhlo.compare"(%[[ARG0_B]], %[[ARG1_B]]) {comparison_direction = "EQ"} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
@ -137,8 +134,7 @@ func @selectv2_dynamic_ranked(%arg0: tensor<1xi1>, %arg1: tensor<2x?x8xi32>, %ar
// CHECK-NEXT: %[[SHAPE1:.*]] = shape.shape_of %arg1 : tensor<2x?x8xi32> -> tensor<3xindex> // CHECK-NEXT: %[[SHAPE1:.*]] = shape.shape_of %arg1 : tensor<2x?x8xi32> -> tensor<3xindex>
// CHECK-NEXT: %[[CSTR:.*]] = shape.cstr_broadcastable %[[SHAPE1]], %[[SHAPE0]], %[[SHAPE2]] : tensor<3xindex>, tensor<1xindex>, tensor<3xindex> // CHECK-NEXT: %[[CSTR:.*]] = shape.cstr_broadcastable %[[SHAPE1]], %[[SHAPE0]], %[[SHAPE2]] : tensor<3xindex>, tensor<1xindex>, tensor<3xindex>
// CHECK-NEXT: %[[ASSUME:.*]] = shape.assuming %[[CSTR]] -> (tensor<2x?x8xi32>) { // CHECK-NEXT: %[[ASSUME:.*]] = shape.assuming %[[CSTR]] -> (tensor<2x?x8xi32>) {
// CHECK-NEXT: %[[BCST_V:.*]] = shape.broadcast %[[SHAPE1]], %[[SHAPE2]] : tensor<3xindex>, tensor<3xindex> -> tensor<?xindex> // CHECK-NEXT: %[[BCST:.*]] = shape.broadcast %[[SHAPE1]], %[[SHAPE2]] : tensor<3xindex>, tensor<3xindex> -> tensor<3xindex>
// CHECK-NEXT: %[[BCST:.*]] = tensor.cast %[[BCST_V]] : tensor<?xindex> to tensor<3xindex>
// CHECK-NEXT: %[[BCST0:.*]] = "mhlo.dynamic_broadcast_in_dim"(%arg0, %[[BCST]]) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<1xi1>, tensor<3xindex>) -> tensor<2x?x8xi1> // CHECK-NEXT: %[[BCST0:.*]] = "mhlo.dynamic_broadcast_in_dim"(%arg0, %[[BCST]]) {broadcast_dimensions = dense<2> : tensor<1xi64>} : (tensor<1xi1>, tensor<3xindex>) -> tensor<2x?x8xi1>
// CHECK-NEXT: %[[BCST1:.*]] = "mhlo.dynamic_broadcast_in_dim"(%arg1, %[[BCST]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<2x?x8xi32>, tensor<3xindex>) -> tensor<2x?x8xi32> // CHECK-NEXT: %[[BCST1:.*]] = "mhlo.dynamic_broadcast_in_dim"(%arg1, %[[BCST]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<2x?x8xi32>, tensor<3xindex>) -> tensor<2x?x8xi32>
// CHECK-NEXT: %[[BCST2:.*]] = "mhlo.dynamic_broadcast_in_dim"(%arg2, %[[BCST]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<2x8x8xi32>, tensor<3xindex>) -> tensor<2x?x8xi32> // CHECK-NEXT: %[[BCST2:.*]] = "mhlo.dynamic_broadcast_in_dim"(%arg2, %[[BCST]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<2x8x8xi32>, tensor<3xindex>) -> tensor<2x?x8xi32>