Bump LLVM to llvm/llvm-project@0cb024b (#2753)

- Add fixes for
af78e5daf0
- Add fixes for
bb6d5c2200

externals/llvm-project

@@ -1 +1 @@
-Subproject commit 6b65d79fbb4682468333cea42b62f15c2dffd8f3
+Subproject commit 0cb024b357aff294b1ba0f9d3de8f48ab684962b

lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp

@@ -33,8 +33,9 @@ Value buildRescale(PatternRewriter &rewriter, Operation *op,
       rewriter.getI32IntegerAttr(static_cast<int32_t>(input_zp)),
       rewriter.getI32IntegerAttr(static_cast<int32_t>(output_zp)),
       rewriter.getDenseI32ArrayAttr({multiplier}),
-      rewriter.getDenseI32ArrayAttr({shift}), rewriter.getBoolAttr(scale32),
-      rewriter.getBoolAttr(double_round), rewriter.getBoolAttr(false));
+      rewriter.getDenseI8ArrayAttr({static_cast<int8_t>(shift)}),
+      rewriter.getBoolAttr(scale32), rewriter.getBoolAttr(double_round),
+      rewriter.getBoolAttr(false));
 
   return rescale_op.getResult();
 }
@@ -86,8 +87,9 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,
         rewriter, op->getLoc(), output_type, conv_val,
         rewriter.getI32IntegerAttr(0), rewriter.getI32IntegerAttr(output_zp),
         rewriter.getDenseI32ArrayAttr({multiplier}),
-        rewriter.getDenseI32ArrayAttr({shift}), rewriter.getBoolAttr(scale32),
-        rewriter.getBoolAttr(true), rewriter.getBoolAttr(false));
+        rewriter.getDenseI8ArrayAttr({static_cast<int8_t>(shift)}),
+        rewriter.getBoolAttr(scale32), rewriter.getBoolAttr(true),
+        rewriter.getBoolAttr(false));
 
     return rescale_op.getResult();
 
@@ -96,7 +98,7 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,
                      .dyn_cast<mlir::quant::UniformQuantizedPerAxisType>()) {
     // Per-channel quantization
     SmallVector<int32_t> multiplier_arr;
-    SmallVector<int32_t> shift_arr;
+    SmallVector<int8_t> shift_arr;
 
     SmallVector<double> weight_scale_arr(
         weight_per_channel_qtype.getScales().begin(),
@@ -115,14 +117,14 @@ Value buildRescaleOpConvOutput(PatternRewriter &rewriter, Operation *op,
                                 scale_width);
 
       multiplier_arr.push_back(multiplier);
-      shift_arr.push_back(shift);
+      shift_arr.push_back(static_cast<int8_t>(shift));
     }
 
     auto rescale_op = CreateOpAndInfer<tosa::RescaleOp>(
         rewriter, op->getLoc(), output_type, conv_val,
         rewriter.getI32IntegerAttr(0), rewriter.getI32IntegerAttr(output_zp),
         rewriter.getDenseI32ArrayAttr(multiplier_arr),
-        rewriter.getDenseI32ArrayAttr(shift_arr), rewriter.getBoolAttr(scale32),
+        rewriter.getDenseI8ArrayAttr(shift_arr), rewriter.getBoolAttr(scale32),
         rewriter.getBoolAttr(true), rewriter.getBoolAttr(true));
 
     return rescale_op.getResult();

test/Dialect/Torch/canonicalize.mlir

@@ -1,10 +1,10 @@
 // RUN: torch-mlir-opt %s -canonicalize | FileCheck %s
 
 // CHECK-LABEL:   func.func @torch.aten.__range_length$fold() -> (!torch.int, !torch.int, !torch.int, !torch.int) {
-// CHECK:           %[[INT1:.*]] = torch.constant.int 1
-// CHECK:           %[[INT2:.*]] = torch.constant.int 2
-// CHECK:           %[[INT3:.*]] = torch.constant.int 3
-// CHECK:           %[[INTM1:.*]] = torch.constant.int -1
+// CHECK-DAG:       %[[INT1:.*]] = torch.constant.int 1
+// CHECK-DAG:       %[[INT2:.*]] = torch.constant.int 2
+// CHECK-DAG:       %[[INT3:.*]] = torch.constant.int 3
+// CHECK-DAG:       %[[INTM1:.*]] = torch.constant.int -1
 // CHECK:           %[[NEG_STEP:.*]] = torch.aten.__range_length %[[INT1]], %[[INT3]], %[[INTM1]] : !torch.int, !torch.int, !torch.int -> !torch.int
 // CHECK:           return %[[INT2]], %[[INT2]], %[[INT1]], %[[NEG_STEP]] : !torch.int, !torch.int, !torch.int, !torch.int
 func.func @torch.aten.__range_length$fold() -> (!torch.int, !torch.int, !torch.int, !torch.int) {

test/Dialect/Torch/decompose-complex-ops.mlir

@@ -84,8 +84,8 @@ func.func @torch.aten.adaptive_avg_pool2d$unit_output_size(%arg0: !torch.vtensor
 
 // CHECK-LABEL:  func.func @torch.aten.type_as$basic(
 // CHECK-SAME:                                %[[ARG_0:.*]]: !torch.tensor, %[[ARG_1:.*]]: !torch.tensor) -> !torch.tensor {
-// CHECK:          %[[FALSE:.*]] = torch.constant.bool false
-// CHECK:          %[[NONE:.*]] = torch.constant.none
+// CHECK-DAG:      %[[FALSE:.*]] = torch.constant.bool false
+// CHECK-DAG:      %[[NONE:.*]] = torch.constant.none
 // CHECK:          %[[DTYPE:.*]] = torch.prim.dtype %[[ARG_1]] : !torch.tensor -> !torch.int
 // CHECK:          %[[VAR:.*]] = torch.aten.to.dtype %[[ARG_0]], %[[DTYPE]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.tensor, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor
 // CHECK:          return %[[VAR]] : !torch.tensor

test/Dialect/Torch/simplify-shape-calculations.mlir

@@ -105,9 +105,9 @@ func.func @refine_shape_calculate_result$user_allows_type_refinement(%arg0: !tor
 // CHECK-LABEL:   func.func @fully_unroll_prim_loop$unroll(
 // CHECK-SAME:                                 %[[ARG0:.*]]: !torch.vtensor,
 // CHECK-SAME:                                 %[[ARG1:.*]]: !torch.list<int>) -> !torch.vtensor {
-// CHECK:           %[[INT1:.*]] = torch.constant.int 1
-// CHECK:           %[[INT2:.*]] = torch.constant.int 2
-// CHECK:           %[[INT0:.*]] = torch.constant.int 0
+// CHECK-DAG:       %[[INT1:.*]] = torch.constant.int 1
+// CHECK-DAG:       %[[INT2:.*]] = torch.constant.int 2
+// CHECK-DAG:       %[[INT0:.*]] = torch.constant.int 0
 // CHECK:           %[[RESULT:.*]] = torch.shape.calculate {
 // CHECK:             torch.shape.calculate.yield %[[ARG0]] : !torch.vtensor
 // CHECK:           } shapes {
@@ -375,8 +375,8 @@ func.func @abstractly_interpret_list_ops$miscompile$list_identity(%arg0: !torch.
 // missing.
 // CHECK-LABEL:   func.func @basic_integration(
 // CHECK-SAME:                %[[ARG0:.*]]: !torch.vtensor<[?,?],unk>) -> !torch.vtensor {
-// CHECK:           %[[INT0:.*]] = torch.constant.int 0
-// CHECK:           %[[INT1:.*]] = torch.constant.int 1
+// CHECK-DAG:       %[[INT0:.*]] = torch.constant.int 0
+// CHECK-DAG:       %[[INT1:.*]] = torch.constant.int 1
 // CHECK:           %[[RESULT:.*]] = torch.shape.calculate {
 // CHECK:             %[[TANH:.*]] = torch.aten.tanh %[[ARG0]] : !torch.vtensor<[?,?],unk> -> !torch.vtensor<[?,?],unk>
 // CHECK:             torch.shape.calculate.yield %[[TANH]] : !torch.vtensor<[?,?],unk>
@@ -410,8 +410,8 @@ func.func @basic_integration(%arg0: !torch.vtensor<[?,?],unk>) -> !torch.vtensor
 // CHECK-LABEL:   func.func @fold_prim_unchecked_cast_op(
 // CHECK-SAME:                                           %[[VAL_0:.*]]: !torch.vtensor,
 // CHECK-SAME:                                           %[[VAL_1:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.vtensor {
-// CHECK:           %[[VAL_2:.*]] = torch.constant.int 0
-// CHECK:           %[[VAL_3:.*]] = torch.constant.int 1
+// CHECK-DAG:       %[[VAL_2:.*]] = torch.constant.int 0
+// CHECK-DAG:       %[[VAL_3:.*]] = torch.constant.int 1
 // CHECK:           %[[VAL_4:.*]] = torch.shape.calculate {
 // CHECK:             %[[VAL_5:.*]] = torch.tensor_static_info_cast %[[VAL_0]] : !torch.vtensor to !torch.vtensor<[?,?],unk>
 // CHECK:             torch.shape.calculate.yield %[[VAL_5]] : !torch.vtensor<[?,?],unk>