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Integrate LLVM at llvm/llvm-project@52f0837778 

Updates LLVM usage to match
[52f0837778b6](https://github.com/llvm/llvm-project/commit/52f0837778b6)

PiperOrigin-RevId: 330939173
This commit is contained in:
A. Unique TensorFlower 2020-09-10 08:13:44 -07:00 committed by TensorFlow MLIR Team
parent d599485e06
commit b22f2f0eea
6 changed files with 13 additions and 13 deletions
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2
build_tools/llvm_version.txt
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@ -1,2 +1,2 @@
4964d75d7078b932ac6b17c1990adaa6eada75c1
52f0837778b6f3b742b36c22b7c608535a52097b

4
lib/Dialect/mhlo/transforms/lhlo_legalize_to_llvm.cc
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@ -45,7 +45,7 @@ struct StaticMemRefCastOpConverter
return failure();
// Create descriptor.
auto desc = MemRefDescriptor::undef(rewriter, loc, llvmTargetDescriptorTy);
Type llvmTargetElementTy = desc.getElementType();
Type llvmTargetElementTy = desc.getElementPtrType();
// Set allocated ptr.
Value allocated = sourceMemRef.allocatedPtr(rewriter, loc);
allocated =
@ -96,7 +96,7 @@ struct DynamicMemRefCastOpConverter
return failure();
// Create descriptor.
auto desc = MemRefDescriptor::undef(rewriter, loc, llvmTargetDescriptorTy);
Type llvmTargetElementTy = desc.getElementType();
Type llvmTargetElementTy = desc.getElementPtrType();
// Set allocated ptr.
Value allocated = sourceMemRef.allocatedPtr(rewriter, loc);
allocated =

4
tests/chlo_legalize_to_hlo_broadcasts.mlir
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@ -253,7 +253,7 @@ func @addScalarUnranked(%arg0: tensor<f32>, %arg1: tensor<*xf32>) -> tensor<*xf3
// to a 1D tensor.
// CHECK: %[[SHAPE_1:.*]] = shape.shape_of %[[ARG_1]] : tensor<*xf32>
// CHECK: %[[NUM_ELEMENTS:.*]] = shape.num_elements %[[SHAPE_1]] : tensor<?xindex> -> index
// CHECK: %[[SIZE_TENSOR:.*]] = tensor_from_elements(%[[NUM_ELEMENTS]]) : tensor<1xindex>
// CHECK: %[[SIZE_TENSOR:.*]] = tensor_from_elements %[[NUM_ELEMENTS]] : tensor<1xindex>
// CHECK: %[[RESHAPED:.*]] = "mhlo.dynamic_reshape"(%[[ARG_1]], %[[SIZE_TENSOR]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
// The assuming region is part of the second stage of lowering
// with ranked broadcasting logic.
@ -288,7 +288,7 @@ func @addUnrankedScalar(%arg0: tensor<*xf32>, %arg1: tensor<f32>) -> tensor<*xf3
// to a 1D tensor.
// CHECK: %[[SHAPE_0:.*]] = shape.shape_of %[[ARG_0]] : tensor<*xf32>
// CHECK: %[[NUM_ELEMENTS:.*]] = shape.num_elements %[[SHAPE_0]] : tensor<?xindex> -> index
// CHECK: %[[SIZE_TENSOR:.*]] = tensor_from_elements(%[[NUM_ELEMENTS]]) : tensor<1xindex>
// CHECK: %[[SIZE_TENSOR:.*]] = tensor_from_elements %[[NUM_ELEMENTS]] : tensor<1xindex>
// CHECK: %[[RESHAPED:.*]] = "mhlo.dynamic_reshape"(%[[ARG_0]], %[[SIZE_TENSOR]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
// The assuming region is part of the second stage of lowering
// with ranked broadcasting logic.

6
tests/hlo-legalize-to-lhlo.mlir
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@ -170,7 +170,7 @@ func @dyn_broadcast(%operand: memref<?x?xf32>) {
// BOTH-SAME: (%[[OPERAND:.*]]: memref<?x?xf32>)
%tensor_operand = tensor_load %operand : memref<?x?xf32>
%c1 = constant 1 : i64
%shape = tensor_from_elements(%c1, %c1, %c1) : tensor<3xi64>
%shape = tensor_from_elements %c1, %c1, %c1 : tensor<3xi64>
%tensor_result = "mhlo.dynamic_broadcast_in_dim"(%tensor_operand, %shape) {
broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>
} : (tensor<?x?xf32>, tensor<3xi64>) -> tensor<?x?x?xf32>
@ -416,7 +416,7 @@ func @add_dyn(%lhs: tensor<?x?xf32>, %rhs: tensor<?x?xf32>) {
// BOTH: %[[C1:.*]] = constant 1 : index
// BOTH: %[[DIM1:.*]] = dim %arg0, %[[C1]] : memref<?x?xf32>
// BOTH: %[[IC1:.*]] = index_cast %[[DIM1]] : index to i64
// BOTH: %[[SHAPE:.*]] = tensor_from_elements(%[[IC0]], %[[IC1]]) : tensor<2xi64>
// BOTH: %[[SHAPE:.*]] = tensor_from_elements %[[IC0]], %[[IC1]] : tensor<2xi64>
// BOTH: %[[C0_:.*]] = constant 0 : index
// BOTH: %[[EE0:.*]] = extract_element %[[SHAPE]][%[[C0_]]] : tensor<2xi64>
// BOTH: %[[ICS0:.*]] = index_cast %[[EE0]] : i64 to index
@ -441,7 +441,7 @@ func @tanh_dyn(%arg0: tensor<?x?xf32>) {
// BOTH: %[[C1:.*]] = constant 1 : index
// BOTH: %[[DIM1:.*]] = dim %arg0, %[[C1]] : memref<?x?xf32>
// BOTH: %[[IC1:.*]] = index_cast %[[DIM1]] : index to i64
// BOTH: %[[SHAPE:.*]] = tensor_from_elements(%[[IC0]], %[[IC1]]) : tensor<2xi64>
// BOTH: %[[SHAPE:.*]] = tensor_from_elements %[[IC0]], %[[IC1]] : tensor<2xi64>
// BOTH: %[[C0_:.*]] = constant 0 : index
// BOTH: %[[EE0:.*]] = extract_element %[[SHAPE]][%[[C0_]]] : tensor<2xi64>
// BOTH: %[[ICS0:.*]] = index_cast %[[EE0]] : i64 to index

6
tests/mhlo-transform-unranked.mlir
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@ -7,7 +7,7 @@ func @sqr_transform_result(%a: tensor<*xf32>) -> tensor<*xf32> {
// Flatten operand shape.
%shape = shape.shape_of %a : tensor<*xf32> -> tensor<?xindex>
%num_elements = shape.num_elements %shape : tensor<?xindex> -> index
%flat_shape = tensor_from_elements(%num_elements) : tensor<1xindex>
%flat_shape = tensor_from_elements %num_elements : tensor<1xindex>
%flat_a = "mhlo.dynamic_reshape"(%a, %flat_shape)
: (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
@ -29,7 +29,7 @@ func @sqr_transform_result(%a: tensor<*xf32>) -> tensor<*xf32> {
func @sqrt(%a: tensor<*xf32>) -> tensor<*xf32> {
// CHECK-NEXT: %[[SHAPE:.*]] = shape.shape_of %[[A]] : tensor<*xf32> -> tensor<?xindex>
// CHECK-NEXT: %[[NUM_ELEMENTS:.*]] = shape.num_elements %[[SHAPE]]
// CHECK-NEXT: %[[FLAT_SHAPE:.*]] = tensor_from_elements(%[[NUM_ELEMENTS]]) : tensor<1xindex>
// CHECK-NEXT: %[[FLAT_SHAPE:.*]] = tensor_from_elements %[[NUM_ELEMENTS]] : tensor<1xindex>
// CHECK-NEXT: %[[FLAT_A:.*]] = "mhlo.dynamic_reshape"(%[[A]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
// CHECK-NEXT: %[[FLAT_B:.*]] = "mhlo.sqrt"(%[[FLAT_A]]) : (tensor<?xf32>) -> tensor<?xf32>
// CHECK-NEXT: %[[B:.*]] = "mhlo.dynamic_reshape"(%[[FLAT_B]], %[[SHAPE]]) : (tensor<?xf32>, tensor<?xindex>) -> tensor<*xf32>
@ -71,7 +71,7 @@ func @add_unranked(%a : tensor<*xf32>, %b : tensor<*xf32>) -> tensor<*xf32> {
// CHECK: %[[SHAPE_B:.*]] = shape.shape_of %[[B]]
// CHECK: %[[SHAPE:.*]] = shape.any %[[SHAPE_A]], %[[SHAPE_B]]
// CHECK: %[[NUM_ELEMENTS:.*]] = shape.num_elements %[[SHAPE]]
// CHECK: %[[FLAT_SHAPE:.*]] = tensor_from_elements(%[[NUM_ELEMENTS]]) : tensor<1xindex>
// CHECK: %[[FLAT_SHAPE:.*]] = tensor_from_elements %[[NUM_ELEMENTS]] : tensor<1xindex>
// CHECK: %[[FLAT_A:.*]] = "mhlo.dynamic_reshape"(%[[A]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
// CHECK: %[[FLAT_B:.*]] = "mhlo.dynamic_reshape"(%[[B]], %[[FLAT_SHAPE]]) : (tensor<*xf32>, tensor<1xindex>) -> tensor<?xf32>
// CHECK: %[[FLAT_RESULT:.*]] = mhlo.add %[[FLAT_A]], %[[FLAT_B]] : tensor<?xf32>

4
tests/unfuse_batch_norm.mlir
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@ -109,7 +109,7 @@ func @batchNormInference_dynamic_shape(
// CHECK-DAG: %[[C3:.*]] = constant 3 : index
// CHECK-DAG: %[[EPS:.+]] = mhlo.constant dense<1.000000e-03> : tensor<f32>
// CHECK-DAG: %[[DIM:.+]] = dim %[[VARIANCE]], %[[C0]] : tensor<?xf32>
// CHECK-DAG: %[[TO_DIM_TENSOR:.+]] = tensor_from_elements(%[[DIM]]) : tensor<1xindex>
// CHECK-DAG: %[[TO_DIM_TENSOR:.+]] = tensor_from_elements %[[DIM]] : tensor<1xindex>
// CHECK-DAG: %[[EPS_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[EPS]], %[[TO_DIM_TENSOR]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<1xindex>) -> tensor<?xf32>
// CHECK-DAG: %[[VARIANCE_EPS:.+]] = mhlo.add %[[VARIANCE]], %[[EPS_BCAST]] : tensor<?xf32>
// CHECK-DAG: %[[STDDEV:.+]] = "mhlo.sqrt"(%[[VARIANCE_EPS]]) : (tensor<?xf32>) -> tensor<?xf32>
@ -117,7 +117,7 @@ func @batchNormInference_dynamic_shape(
// CHECK-DAG: %[[INPUT_DIM_1:.+]] = dim %[[X]], %[[C1]] : tensor<?x?x?x?xf32>
// CHECK-DAG: %[[INPUT_DIM_2:.+]] = dim %[[X]], %[[C2]] : tensor<?x?x?x?xf32>
// CHECK-DAG: %[[INPUT_DIM_3:.+]] = dim %[[X]], %[[C3]] : tensor<?x?x?x?xf32>
// CHECK-DAG: %[[TO_INPUT_DIM_TENSOR:.+]] = tensor_from_elements(%[[INPUT_DIM_0]], %[[INPUT_DIM_1]], %[[INPUT_DIM_2]], %[[INPUT_DIM_3]]) : tensor<4xindex>
// CHECK-DAG: %[[TO_INPUT_DIM_TENSOR:.+]] = tensor_from_elements %[[INPUT_DIM_0]], %[[INPUT_DIM_1]], %[[INPUT_DIM_2]], %[[INPUT_DIM_3]] : tensor<4xindex>
// CHECK-DAG: %[[STDDEV_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[STDDEV]], %[[TO_INPUT_DIM_TENSOR]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<?xf32>, tensor<4xindex>) -> tensor<?x?x?x?xf32>
// CHECK-DAG: %[[SCALE_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[SCALE]], %[[TO_INPUT_DIM_TENSOR]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<?xf32>, tensor<4xindex>) -> tensor<?x?x?x?xf32>
// CHECK-DAG: %[[OFFSET_BCAST:.+]] = "mhlo.dynamic_broadcast_in_dim"(%[[OFFSET]], %[[TO_INPUT_DIM_TENSOR]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<?xf32>, tensor<4xindex>) -> tensor<?x?x?x?xf32>