bump LLVM to 765206 and stablehlo to 3c6a06

Signed-off-by: Yan Xu <[email protected]>

docs/BuildOnLinuxOSX.md

@@ -15,7 +15,7 @@ Firstly, install MLIR (as a part of LLVM-Project):
 ``` bash
 git clone -n https://github.com/llvm/llvm-project.git
 # Check out a specific branch that is known to work with ONNX-MLIR.
-cd llvm-project && git checkout 20ed5b1f45871612570d3bd447121ac43e083c6a && cd ..
+cd llvm-project && git checkout 765206e050453018e861637a08a4520f29238074 && cd ..
 ```
 
 [same-as-file]: <> (utils/build-mlir.sh)

docs/BuildOnWindows.md

@@ -52,7 +52,7 @@ Install MLIR (as a part of LLVM-Project):
 ```shell
 git clone -n https://github.com/llvm/llvm-project.git
 # Check out a specific branch that is known to work with ONNX-MLIR.
-cd llvm-project && git checkout 20ed5b1f45871612570d3bd447121ac43e083c6a && cd ..
+cd llvm-project && git checkout 765206e050453018e861637a08a4520f29238074 && cd ..
 ```
 
 [same-as-file]: <> (utils/build-mlir.cmd)

src/Conversion/ONNXToStablehlo/Tensor/GatherElements.cpp

@@ -133,6 +133,8 @@ struct ONNXGatherElementsOpLoweringToStablehlo : public ConversionPattern {
         stablehlo::GatherDimensionNumbersAttr::get(rewriter.getContext(),
             /*offsetDims=*/{},
             /*collapsedSliceDims=*/collapsedDims,
+            /*operandBatchingDims=*/{},
+            /*startIndicesBatchingDims=*/{},
             /*startIndexMap=*/startIndexMap,
             /*indexVecDim=*/rank);
     SmallVector<int64_t> sliceSizes(inputType.getRank(), 1);

src/Conversion/ONNXToStablehlo/Tensor/ScatterND.cpp

@@ -60,6 +60,8 @@ struct ONNXScatterNDOpLoweringToStablehlo
             llvm::to_vector<4>(llvm::seq<int64_t>(partialIdxDim, dataRank)),
             /*insertedWindowDims*/
             llvm::to_vector<4>(llvm::seq<int64_t>(0, partialIdxDim)),
+            /*inputBatchingDims=*/{},
+            /*scatterIndicesBatchingDims=*/{},
             /*scatterDimsToOperandDims*/
             llvm::to_vector<4>(llvm::seq<int64_t>(0, partialIdxDim)),
             /*indexVectorDim=*/indicesRank - 1);

src/Conversion/ONNXToTOSA/DialectBuilder.cpp

@@ -187,7 +187,7 @@ Value TosaBuilder::intdiv(mlir::Value &lhs, mlir::Value &rhs) {
   Type rhsElementType = rhs.getType().cast<ShapedType>().getElementType();
   assert((lhsElementType.isSignlessInteger(32) &&
              rhsElementType.isSignlessInteger(32)) &&
-         "Tosa DivOp needs 32-bit signless integer inputs");
+         "Tosa IntDivOp needs 32-bit signless integer inputs");
 
   if (needsRankBroadcast({lhs, rhs})) {
     llvm::SmallVector<Value, 4> valueVec = equalizeRanks({lhs, rhs});
@@ -199,7 +199,7 @@ Value TosaBuilder::intdiv(mlir::Value &lhs, mlir::Value &rhs) {
   Type newValueType = RankedTensorType::get(
       llvm::SmallVector<int64_t, 4>(lhsType.getRank(), ShapedType::kDynamic),
       lhsElementType);
-  return tosa::CreateOpAndInfer<mlir::tosa::DivOp>(
+  return tosa::CreateOpAndInfer<mlir::tosa::IntDivOp>(
       rewriter(), loc(), newValueType, lhs, rhs);
 }
 

src/Conversion/ONNXToTOSA/Math/Elementwise.cpp

@@ -143,7 +143,7 @@ class ONNXDivOpLoweringToTOSA : public OpConversionPattern<ONNXDivOp> {
     TosaBuilder tosaBuilder(rewriter, op->getLoc());
 
     if (resultElementType.isSignlessInteger(32)) {
-      // tosa::DivOp takes 32-but signless integers as inputs
+      // tosa::IntDivOp takes 32-but signless integers as inputs
       Value divOp = tosaBuilder.intdiv(lhs, rhs);
       rewriter.replaceOp(op, {divOp});
       return success();

test/mlir/conversion/krnl_to_affine/krnl_to_affine_with_canonicalize.mlir

@@ -82,7 +82,7 @@ func.func @krnl_get_linear_offset_index_1(%arg0: memref<?x128x256xf32, #map>, %a
   %c10 = arith.constant 10: index
   %0 = memref.alloc(%arg1) : memref<?x128x256xf32, #map>
   %1 = krnl.get_linear_offset_index %arg0 at [%arg2, %c5, %c10] : memref<?x128x256xf32, #map>
-  return %1: index 
+  return %1: index
 
 // CHECK-LABEL:  func.func @krnl_get_linear_offset_index
 // CHECK:           [[VAR_0_:%.+]] = krnl.get_linear_offset_index {{.*}} at {{.*}} : memref<?x128x256xf32, #map>
@@ -127,7 +127,7 @@ func.func @prefetch(%arg0: memref<256x512xf32>) -> memref<256x512xf32> attribute
 // CHECK:           affine.for [[I_0_:%.+]] = 0 to 256 {
 // CHECK:             affine.for [[I_1_:%.+]] = 0 to 512 {
 // CHECK:               [[LOAD_PARAM_0_MEM_:%.+]] = affine.load [[PARAM_0_]]{{.}}[[I_0_]], [[I_1_]]{{.}} : memref<256x512xf32>
-// CHECK:               affine.prefetch [[PARAM_0_]]{{.}}[[I_0_]], [[I_1_]] + 64], write, locality<0>, data : memref<256x512xf32>
+// CHECK:               affine.prefetch [[PARAM_0_]]{{.}}[[I_0_]], [[I_1_]] + 64], read, locality<3>, data : memref<256x512xf32>
 // CHECK:               [[LOAD_PARAM_0_MEM_1_:%.+]] = affine.load [[PARAM_0_]]{{.}}[[I_0_]], [[I_1_]]{{.}} : memref<256x512xf32>
 // CHECK:               [[VAR_2_:%.+]] = arith.addf [[LOAD_PARAM_0_MEM_]], [[LOAD_PARAM_0_MEM_1_]] : f32
 // CHECK:               affine.store [[VAR_2_]], [[RES_]]{{.}}[[I_0_]], [[I_1_]]{{.}} : memref<256x512xf32>

test/mlir/conversion/krnl_to_llvm/constants_to_file/litte_endian/constants.mlir

@@ -1,8 +1,8 @@
 // RUN: onnx-mlir-opt --convert-krnl-to-llvm="store-constants-to-file constants-to-file-single-threshold=0.03 constants-to-file-total-threshold=0.00000006" --canonicalize %s -split-input-file | FileCheck %s && rm model.constants.bin
 
-// Thresholds for this files: 
-//  -constants-to-file-single-threshold=0.03: 30 bytes for a single constants 
-//  -constants-to-file-total-threshold=0.00000006: 60 bytes for all constants 
+// Thresholds for this files:
+//  -constants-to-file-single-threshold=0.03: 30 bytes for a single constants
+//  -constants-to-file-total-threshold=0.00000006: 60 bytes for all constants
 
 // Donot save to file if a constant is a return value.
 module {
@@ -11,7 +11,7 @@ func.func @test_constants_to_file_return_value() -> memref<10xi64> {
   return %0 : memref<10xi64>
 
   // CHECK-LABEL: module
-  // CHECK: llvm.mlir.global internal constant @constant(dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]> : tensor<10xi64>) {addr_space = 0 : i32, alignment = 4096 : i64} : !llvm.array<10 x i64> 
+  // CHECK: llvm.mlir.global internal constant @constant(dense<[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]> : tensor<10xi64>) {addr_space = 0 : i32, alignment = 4096 : i64} : !llvm.array<10 x i64>
 }
 }
 
@@ -60,17 +60,16 @@ func.func @test_constants_to_file() -> memref<10xi64> {
 
 // CHECK:         llvm.func @omLoadConstantsFromFile() {
 // CHECK-DAG:       [[VAR_0_9_:%.+]] = llvm.mlir.constant(4096 : i64) : i64
-// CHECK-DAG:       [[VAR_1_3_:%.+]] = llvm.mlir.constant(4176 : i64) : i64
+// CHECK-DAG:       [[VAR_1_3_:%.+]] = llvm.mlir.addressof @om_external_constant_data_constant_0 : !llvm.ptr
 // CHECK-DAG:       [[VAR_2_3_:%.+]] = llvm.mlir.constant(0 : i64) : i64
-// CHECK-DAG:       [[VAR_2_4_:%.+]] = llvm.mlir.constant(1 : i64) : i64
-// CHECK-DAG:       [[VAR_3_3_:%.+]] = llvm.mlir.addressof @om_external_constant_filename : !llvm.ptr
-// CHECK-NOT: separator of consecutive DAGs
-// CHECK-DAG:       [[VAR_5_3_:%.+]] = llvm.mlir.addressof @om_external_constant_packedConst : !llvm.ptr
-// CHECK:           llvm.call @omMMapBinaryFile([[VAR_5_3_]], [[VAR_3_3_]], [[VAR_1_3_]], [[VAR_2_4_]]) : (!llvm.ptr, !llvm.ptr, i64, i64) -> ()
-// CHECK:           [[VAR_7_3_:%.+]] = llvm.mlir.addressof @om_external_constant_data_constant_1 : !llvm.ptr
-// CHECK:           llvm.call @omGetExternalConstantAddr([[VAR_7_3_]], [[VAR_5_3_]], [[VAR_2_3_]]) : (!llvm.ptr, !llvm.ptr, i64) -> ()
-// CHECK:           [[VAR_9_3_:%.+]] = llvm.mlir.addressof @om_external_constant_data_constant_0 : !llvm.ptr
-// CHECK:           llvm.call @omGetExternalConstantAddr([[VAR_9_3_]], [[VAR_5_3_]], [[VAR_0_9_]]) : (!llvm.ptr, !llvm.ptr, i64) -> ()
+// CHECK-DAG:       [[VAR_3_3_:%.+]] = llvm.mlir.addressof @om_external_constant_data_constant_1 : !llvm.ptr
+// CHECK-DAG:       [[VAR_4_3_:%.+]] = llvm.mlir.constant(4176 : i64) : i64
+// CHECK-DAG:       [[VAR_5_3_:%.+]] = llvm.mlir.constant(1 : i64) : i64
+// CHECK-DAG:       [[VAR_6_3_:%.+]] = llvm.mlir.addressof @om_external_constant_packedConst : !llvm.ptr
+// CHECK-DAG:       [[VAR_7_3_:%.+]] = llvm.mlir.addressof @om_external_constant_filename : !llvm.ptr
+// CHECK:           llvm.call @omMMapBinaryFile([[VAR_6_3_]], [[VAR_7_3_]], [[VAR_4_3_]], [[VAR_5_3_]]) : (!llvm.ptr, !llvm.ptr, i64, i64) -> ()
+// CHECK:           llvm.call @omGetExternalConstantAddr([[VAR_3_3_]], [[VAR_6_3_]], [[VAR_2_3_]]) : (!llvm.ptr, !llvm.ptr, i64) -> ()
+// CHECK:           llvm.call @omGetExternalConstantAddr([[VAR_1_3_]], [[VAR_6_3_]], [[VAR_0_9_]]) : (!llvm.ptr, !llvm.ptr, i64) -> ()
 // CHECK:           llvm.return
 // CHECK:         }