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Check LLVM PR https://github.com/llvm/llvm-project/pull/113501/ #18907

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Check LLVM PR https://github.com/llvm/llvm-project/pull/113501/ #18907

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f63c491
Pick up LLVM change https://github.com/llvm/llvm-project/pull/113501
MaheshRavishankar Oct 24, 2024
66c536a
Fixes for https://github.com/llvm/llvm-project/pull/113501
MaheshRavishankar Oct 13, 2024
b986346
Change Torch lowering to use divSI
MaheshRavishankar Jan 25, 2025
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6 changes: 3 additions & 3 deletions compiler/plugins/input/Torch/InputConversion/BindSymbolicShapes.cpp
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Expand Up @@ -281,11 +281,11 @@ class BindSymbolicShapesPass final
case AffineExprKind::Mul:
return builder.create<arith::MulIOp>(loc, lhs, rhs);
case AffineExprKind::Mod:
return builder.create<arith::RemUIOp>(loc, lhs, rhs);
return builder.create<arith::RemSIOp>(loc, lhs, rhs);
case AffineExprKind::FloorDiv:
return builder.create<arith::DivUIOp>(loc, lhs, rhs);
return builder.create<arith::DivSIOp>(loc, lhs, rhs);
case AffineExprKind::CeilDiv:
return builder.create<arith::CeilDivUIOp>(loc, lhs, rhs);
return builder.create<arith::CeilDivSIOp>(loc, lhs, rhs);
default:
break;
}
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8 changes: 4 additions & 4 deletions compiler/plugins/input/Torch/InputConversion/test/bind_symbolic_shapes.mlir
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Expand Up @@ -113,8 +113,8 @@ module @add_expr {
// CHECK-LABEL: @mod_expr
module @mod_expr {
func.func @main(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) {
// CHECK: remui
// CHECK-NOT: udiv
// CHECK: remsi
// CHECK-NOT: sdiv
%0 = torch.symbolic_int "s0" {min_val = 0, max_val = 1024} : !torch.int
%1 = torch.symbolic_int "s1" {min_val = 0, max_val = 1024} : !torch.int
torch.bind_symbolic_shape %arg0, [%0, %1], affine_map<()[s0, s1] -> (s0, s1)> : !torch.vtensor<[?,?],f32>
Expand All @@ -127,8 +127,8 @@ module @mod_expr {
// CHECK-LABEL: @floordiv_expr
module @floordiv_expr {
func.func @main(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) {
// CHECK: divui
// CHECK-NOT: udiv
// CHECK: divsi
// CHECK-NOT: sdiv
%0 = torch.symbolic_int "s0" {min_val = 0, max_val = 1024} : !torch.int
%1 = torch.symbolic_int "s1" {min_val = 0, max_val = 1024} : !torch.int
torch.bind_symbolic_shape %arg0, [%0, %1], affine_map<()[s0, s1] -> (s0, s1)> : !torch.vtensor<[?,?],f32>
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4 changes: 2 additions & 2 deletions compiler/src/iree/compiler/Codegen/Common/test/block_dynamic_dims.mlir
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Expand Up @@ -66,11 +66,11 @@ func.func @block_attention_dims() {
// CHECK-DAG: %[[C16:.+]] = arith.constant 16 : index
// CHECK-DAG: %[[M:.+]] = flow.dispatch.workload.ordinal %{{.+}}, 0 : index
// CHECK-DAG: %[[K2:.+]] = flow.dispatch.workload.ordinal %{{.+}}, 1 : index
// CHECK-DAG: %[[M_DYNAMIC:.+]] = arith.divui %[[M]], %[[C16]]
// CHECK-DAG: %[[M_DYNAMIC:.+]] = arith.divsi %[[M]], %[[C16]]
// CHECK: %[[Q_BINDING:.+]] = hal.interface.binding.subspan
// CHECK-SAME: binding(0)
// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<4x?x16x32x128xf16>>{%[[M_DYNAMIC]]}
// CHECK: %[[K2_DYNAMIC:.+]] = arith.divui %[[K2]], %[[C32]]
// CHECK: %[[K2_DYNAMIC:.+]] = arith.divsi %[[K2]], %[[C32]]
// CHECK: %[[K_BINDING:.+]] = hal.interface.binding.subspan
// CHECK-SAME: binding(1)
// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<4x?x32x32x128xf16>>{%[[K2_DYNAMIC]]}
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4 changes: 2 additions & 2 deletions compiler/src/iree/compiler/Codegen/Common/test/propagate_reshapes_by_expansion.mlir
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Expand Up @@ -57,7 +57,7 @@ func.func @fold_expand_into_loads_dynamic() -> tensor<2x?x16x32xf32> {
// CHECK-LABEL: func @fold_expand_into_loads_dynamic()
// CHECK-DAG: %[[C16:.+]] = arith.constant 16 : index
// CHECK-DAG: %[[CONST:.+]] = hal.interface.constant.load
// CHECK: %[[SHAPE:.+]] = arith.divui %[[CONST]], %[[C16]]
// CHECK: %[[SHAPE:.+]] = arith.divsi %[[CONST]], %[[C16]]
// CHECK: %[[SUBSPAN:.+]] = hal.interface.binding.subspan
// CHECK-SAME: !flow.dispatch.tensor<readonly:tensor<2x?x16x32xf32>>{%[[SHAPE]]}
// CHECK: %[[LOAD:.+]] = flow.dispatch.tensor.load %[[SUBSPAN]]
Expand All @@ -81,7 +81,7 @@ func.func @fold_collapse_into_stores_dynamic(%arg0 : tensor<2x?x32xf32>) {
// CHECK-LABEL: func @fold_collapse_into_stores_dynamic(
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
// CHECK: %[[CONST:.+]] = hal.interface.constant.load
// CHECK: %[[SHAPE:.+]] = arith.divui %[[CONST]], %[[C2]]
// CHECK: %[[SHAPE:.+]] = arith.divsi %[[CONST]], %[[C2]]
// CHECK: %[[SUBSPAN:.+]] = hal.interface.binding.subspan
// CHECK-SAME: !flow.dispatch.tensor<writeonly:tensor<2x?x32xf32>>{%[[SHAPE]]}
// CHECK: flow.dispatch.tensor.store %{{.+}}, %[[SUBSPAN]]
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20 changes: 9 additions & 11 deletions compiler/src/iree/compiler/DispatchCreation/test/attention_fuse_by_expansion.mlir
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Expand Up @@ -197,18 +197,17 @@ util.func public @attention_dynamic(%arg0: tensor<?x?x?xf16>, %arg1: tensor<?x?x
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK-DAG: %[[D2:.+]] = tensor.dim %[[ARG0]], %[[C2]]
// CHECK-DAG: %[[D4:.+]] = tensor.dim %[[ARG2]], %[[C2]]
// CHECK-DAG: %[[SPLIT0:.+]] = arith.divui %[[D0]]
// CHECK-DAG: %[[VAL:.+]] = affine.apply affine_map<()[s0] -> (s0 floordiv 2)>()[%[[D0]]]
// CHECK-DAG: %[[EMPTY:.+]] = tensor.empty(%[[VAL]], %[[D1]], %[[D4]]) : tensor<2x?x?x?xf16>
// CHECK-DAG: %[[SPLIT0:.+]] = arith.divsi %[[D0]]
// CHECK-DAG: %[[EMPTY:.+]] = tensor.empty(%[[SPLIT0]], %[[D1]], %[[D4]]) : tensor<2x?x?x?xf16>
// CHECK-DAG: %[[QUERY:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, %[[SPLIT0]], %[[D1]], %[[D2]]]
// CHECK-DAG: %[[D5:.+]] = tensor.dim %[[ARG1]], %[[C0]]
// CHECK-DAG: %[[D6:.+]] = tensor.dim %[[ARG1]], %[[C1]]
// CHECK-DAG: %[[D7:.+]] = tensor.dim %[[ARG1]], %[[C2]]
// CHECK-DAG: %[[SPLIT1:.+]] = arith.divui %[[D5]], %[[C2]]
// CHECK-DAG: %[[SPLIT1:.+]] = arith.divsi %[[D5]], %[[C2]]
// CHECK-DAG: %[[KEY:.+]] = tensor.expand_shape %[[ARG1]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, %[[SPLIT1]], %[[D6]], %[[D7]]]
// CHECK-DAG: %[[D8:.+]] = tensor.dim %[[ARG2]], %[[C0]]
// CHECK-DAG: %[[D9:.+]] = tensor.dim %[[ARG2]], %[[C1]]
// CHECK-DAG: %[[SPLIT2:.+]] = arith.divui %[[D8]], %[[C2]]
// CHECK-DAG: %[[SPLIT2:.+]] = arith.divsi %[[D8]], %[[C2]]
// CHECK-DAG: %[[CACHE:.+]] = tensor.expand_shape %[[ARG2]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, %[[SPLIT2]], %[[D9]], %[[D4]]]
// CHECK: %[[ATTENTION:.+]] = iree_linalg_ext.attention
// CHECK-SAME: indexing_maps =
Expand Down Expand Up @@ -263,23 +262,22 @@ util.func public @attention_dynamic_masked(%arg0: tensor<?x?x?xf16>, %arg1: tens
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK-DAG: %[[D2:.+]] = tensor.dim %[[ARG0]], %[[C2]]
// CHECK-DAG: %[[D4:.+]] = tensor.dim %[[ARG2]], %[[C2]]
// CHECK-DAG: %[[SPLIT0:.+]] = arith.divui %[[D0]]
// CHECK-DAG: %[[VAL:.+]] = affine.apply affine_map<()[s0] -> (s0 floordiv 2)>()[%[[D0]]]
// CHECK-DAG: %[[EMPTY:.+]] = tensor.empty(%[[VAL]], %[[D1]], %[[D4]]) : tensor<2x?x?x?xf16>
// CHECK-DAG: %[[SPLIT0:.+]] = arith.divsi %[[D0]]
// CHECK-DAG: %[[EMPTY:.+]] = tensor.empty(%[[SPLIT0]], %[[D1]], %[[D4]]) : tensor<2x?x?x?xf16>
// CHECK-DAG: %[[QUERY:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, %[[SPLIT0]], %[[D1]], %[[D2]]]
// CHECK-DAG: %[[D5:.+]] = tensor.dim %[[ARG1]], %[[C0]]
// CHECK-DAG: %[[D6:.+]] = tensor.dim %[[ARG1]], %[[C1]]
// CHECK-DAG: %[[D7:.+]] = tensor.dim %[[ARG1]], %[[C2]]
// CHECK-DAG: %[[SPLIT1:.+]] = arith.divui %[[D5]], %[[C2]]
// CHECK-DAG: %[[SPLIT1:.+]] = arith.divsi %[[D5]], %[[C2]]
// CHECK-DAG: %[[KEY:.+]] = tensor.expand_shape %[[ARG1]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, %[[SPLIT1]], %[[D6]], %[[D7]]]
// CHECK-DAG: %[[D8:.+]] = tensor.dim %[[ARG2]], %[[C0]]
// CHECK-DAG: %[[D9:.+]] = tensor.dim %[[ARG2]], %[[C1]]
// CHECK-DAG: %[[SPLIT2:.+]] = arith.divui %[[D8]], %[[C2]]
// CHECK-DAG: %[[SPLIT2:.+]] = arith.divsi %[[D8]], %[[C2]]
// CHECK-DAG: %[[CACHE:.+]] = tensor.expand_shape %[[ARG2]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, %[[SPLIT2]], %[[D9]], %[[D4]]]
// CHECK-DAG: %[[D10:.+]] = tensor.dim %[[ARG4]], %[[C0]]
// CHECK-DAG: %[[D11:.+]] = tensor.dim %[[ARG4]], %[[C1]]
// CHECK-DAG: %[[D12:.+]] = tensor.dim %[[ARG4]], %[[C2]]
// CHECK-DAG: %[[SPLIT3:.+]] = arith.divui %[[D10]], %[[C2]]
// CHECK-DAG: %[[SPLIT3:.+]] = arith.divsi %[[D10]], %[[C2]]
// CHECK-DAG: %[[MASK:.+]] = tensor.expand_shape %[[ARG4]] {{\[}}[0, 1], [2], [3]{{\]}} output_shape [2, %[[SPLIT3]], %[[D11]], %[[D12]]]
// CHECK: %[[ATTENTION:.+]] = iree_linalg_ext.attention
// CHECK-SAME: indexing_maps =
Expand Down
4 changes: 2 additions & 2 deletions compiler/src/iree/compiler/GlobalOptimization/test/linalg_quantized_conv_to_conv.mlir
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Expand Up @@ -292,12 +292,12 @@ func.func @conv2d_all_dyn(%arg0: tensor<?x?x?x?xi8>, %arg1: tensor<?x?x?x?xi8>,
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction"]}
// CHECK-SAME: ins(%arg0 : tensor<?x?x?x?xi8>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?x?x?xi32>)
// CHECK: %[[DIM00:.+]] = tensor.dim %arg0, %[[I0]]
// CHECK: %[[EXPAND:.+]] = tensor.expand_shape %[[SUMI]] {{\[\[}}0], [1], [2, 3]]

// CHECK-DAG: %[[DIM0:.+]] = tensor.dim %arg0, %[[I0]]
// CHECK-DAG: %[[DIM1:.+]] = tensor.dim %arg2, %[[I1]]
// CHECK-DAG: %[[DIM2:.+]] = tensor.dim %arg2, %[[I2]]
// CHECK: %[[EMPTY:.+]] = tensor.empty(%[[DIM0]], %[[DIM1]], %[[DIM2]])
// CHECK: %[[EMPTY:.+]] = tensor.empty(%[[DIM00]], %[[DIM1]], %[[DIM2]])
// CHECK: %[[FILL:.+]] = linalg.fill ins(%[[C0]] : i32) outs(%9 : tensor<?x?x?x1xi32>)
// CHECK-DAG: %[[DIM0:.+]] = tensor.dim %arg1, %[[I0]] : tensor<?x?x?x?xi8>
// CHECK-DAG: %[[DIM1:.+]] = tensor.dim %arg1, %[[I1]] : tensor<?x?x?x?xi8>
Expand Down
2 changes: 1 addition & 1 deletion third_party/llvm-project
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Submodule llvm-project updated 10 files
+3 −0 mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
+3 −0 mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
+1 −1 mlir/lib/Dialect/Arith/Utils/Utils.cpp
+3 −2 mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
+20 −95 mlir/lib/Dialect/Tensor/IR/TensorInferTypeOpInterfaceImpl.cpp
+4 −0 mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+8 −2 mlir/lib/Dialect/Utils/StaticValueUtils.cpp
+0 −8 mlir/lib/Interfaces/InferTypeOpInterface.cpp
+2 −5 mlir/test/Dialect/Linalg/resolve-shaped-type-result-dims.mlir
+3 −6 mlir/test/Dialect/Tensor/fold-empty-op.mlir
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