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Integrate LLVM at llvm/llvm-project@671f0e2e18 

Updates LLVM usage to match
[671f0e2e189c](https://github.com/llvm/llvm-project/commit/671f0e2e189c)

PiperOrigin-RevId: 371011125
This commit is contained in:
A. Unique TensorFlower 2021-04-28 16:36:37 -07:00 committed by TensorFlow MLIR Team
parent 9e9434a0ea
commit 4d41b11f3b
4 changed files with 9 additions and 10 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 = "bf9eef92b6cd71d262ac12ce6c4919271bd6c910" LLVM_COMMIT = "671f0e2e189c561512511331d95de382e2d6d15d"
LLVM_SHA256 = "3389f8b08828b5254986a80e38f46f0f1cfe585fb357b7876f1ccf2cacd3ed78" LLVM_SHA256 = "2628dd724f9cf6130aabff2127f99d2dc8577b10ba2fda16a9e3d4d90a84ab52"
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 @@
bf9eef92b6cd71d262ac12ce6c4919271bd6c910 671f0e2e189c561512511331d95de382e2d6d15d

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>) -> !shape.shape { func @broadcast_add(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> !shape.shape {
// 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]] : tensor<?xindex>, tensor<?xindex> -> !shape.shape // CHECK-DAG: %[[BCAST_S:.+]] = shape.broadcast %[[ARG0_S]], %[[ARG1_S]] : tensor<1xindex>, tensor<1xindex> -> !shape.shape
// CHECK: return %[[BCAST_S]] : !shape.shape // CHECK: return %[[BCAST_S]] : !shape.shape
%0 = chlo.broadcast_add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32> %0 = chlo.broadcast_add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
%1 = "mhlo_test.reify_return_type_shapes"(%0) : (tensor<?xf32>) -> !shape.shape %1 = "mhlo_test.reify_return_type_shapes"(%0) : (tensor<?xf32>) -> !shape.shape

11
tests/chlo_legalize_to_hlo_broadcasts.mlir
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@ -132,13 +132,12 @@ func @selectv2_broadcast_all(%arg0: tensor<8x1x1xi1>, %arg1: tensor<1x8x1xi32>,
// CHECK-LABEL: func @selectv2_dynamic_ranked // CHECK-LABEL: func @selectv2_dynamic_ranked
func @selectv2_dynamic_ranked(%arg0: tensor<1xi1>, %arg1: tensor<2x?x8xi32>, %arg2: tensor<2x8x8xi32>) -> tensor<2x?x8xi32> { func @selectv2_dynamic_ranked(%arg0: tensor<1xi1>, %arg1: tensor<2x?x8xi32>, %arg2: tensor<2x8x8xi32>) -> tensor<2x?x8xi32> {
// CHECK-NEXT: %[[SHAPE0:.*]] = shape.const_shape [1] : tensor<?xindex> // CHECK-NEXT: %[[SHAPE0:.*]] = shape.const_shape [1] : tensor<1xindex>
// CHECK-NEXT: %[[SHAPE2:.*]] = shape.const_shape [2, 8, 8] : tensor<?xindex> // CHECK-NEXT: %[[SHAPE2:.*]] = shape.const_shape [2, 8, 8] : tensor<3xindex>
// CHECK-NEXT: %[[PARTIALLY_FOLDED_BCAST:.*]] = constant dense<[2, 8, 8]> : tensor<3xindex> // CHECK-NEXT: %[[SHAPE1:.*]] = shape.shape_of %arg1 : tensor<2x?x8xi32> -> tensor<3xindex>
// CHECK-NEXT: %[[SHAPE1:.*]] = shape.shape_of %arg1 : tensor<2x?x8xi32> -> tensor<?xindex> // 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<?xindex>, tensor<?xindex>, tensor<?xindex>
// 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]], %[[PARTIALLY_FOLDED_BCAST]] : tensor<?xindex>, tensor<3xindex> -> tensor<?xindex> // CHECK-NEXT: %[[BCST_V:.*]] = shape.broadcast %[[SHAPE1]], %[[SHAPE2]] : tensor<3xindex>, tensor<3xindex> -> tensor<?xindex>
// CHECK-NEXT: %[[BCST:.*]] = tensor.cast %[[BCST_V]] : tensor<?xindex> to 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>