| // RUN: mlir-opt --split-input-file --convert-shape-to-std --verify-diagnostics %s | FileCheck %s |
| |
| // Lower binary ops. |
| // CHECK-LABEL: @binary_ops |
| // CHECK-SAME: (%[[LHS:.*]]: index, %[[RHS:.*]]: index) |
| func @binary_ops(%lhs : index, %rhs : index) { |
| // CHECK: arith.addi %[[LHS]], %[[RHS]] : index |
| %sum = shape.add %lhs, %rhs : index, index -> index |
| // CHECK: arith.muli %[[LHS]], %[[RHS]] : index |
| %product = shape.mul %lhs, %rhs : index, index -> index |
| return |
| } |
| |
| // ----- |
| |
| // Don't lower binary ops when they operate on `shape.size`. |
| // CHECK-LABEL: @binary_ops_on_size |
| // CHECK-SAME: (%[[LHS:.*]]: !shape.size, %[[RHS:.*]]: !shape.size) |
| func @binary_ops_on_size(%lhs : !shape.size, %rhs : !shape.size) { |
| // CHECK: shape.add %[[LHS]], %[[RHS]] : !shape.size, !shape.size -> !shape.size |
| // CHECK: shape.mul %[[LHS]], %[[RHS]] : !shape.size, !shape.size -> !shape.size |
| %sum = shape.add %lhs, %rhs : !shape.size, !shape.size -> !shape.size |
| %prod = shape.mul %lhs, %rhs : !shape.size, !shape.size -> !shape.size |
| return |
| } |
| |
| // ----- |
| |
| // Convert `rank` to `dim` of the first dimension. |
| // CHECK-LABEL: @rank |
| // CHECK-SAME: (%[[SHAPE:.*]]: tensor<?xindex>) -> index |
| func @rank(%shape : tensor<?xindex>) -> index { |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[RESULT:.*]] = tensor.dim %[[SHAPE]], %[[C0]] |
| // CHECK: return %[[RESULT]] : index |
| %rank = shape.rank %shape : tensor<?xindex> -> index |
| return %rank : index |
| } |
| |
| // ----- |
| |
| // Don't lower `get_extent` if it is of type `shape.size`. |
| // CHECK-LABEL: @get_extent |
| func @get_extent(%shape : tensor<?xindex>, %idx : !shape.size) -> !shape.size { |
| // CHECK: shape.get_extent |
| %result = shape.get_extent %shape, %idx |
| : tensor<?xindex>, !shape.size -> !shape.size |
| return %result : !shape.size |
| } |
| |
| // ----- |
| |
| // Don't lower `rank` if type is not error-free. |
| // CHECK-LABEL: @rank |
| func @rank(%shape : !shape.shape) { |
| // CHECK: shape.rank |
| %rank = shape.rank %shape : !shape.shape -> !shape.size |
| return |
| } |
| |
| // ----- |
| |
| // Express `get_extent` as `tensor.dim` when it relies directly on the outcome of a |
| // `shape_of` operation. |
| // CHECK-LABEL: @get_extent_shape_of |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<2x3xf32>, %[[IDX:.*]]: index) -> index |
| func @get_extent_shape_of(%arg : tensor<2x3xf32>, %idx : index) -> index { |
| // CHECK: %[[RESULT:.*]] = tensor.dim %[[ARG]], %[[IDX]] : tensor<2x3xf32> |
| // CHECK: return %[[RESULT]] : index |
| %shape = shape.shape_of %arg : tensor<2x3xf32> -> tensor<?xindex> |
| %result = shape.get_extent %shape, %idx : tensor<?xindex>, index -> index |
| return %result : index |
| } |
| |
| // ----- |
| |
| // Express `get_extent` as `std.tensor.extract`. |
| // CHECK-LABEL: @get_extent_from_extent_tensor |
| // CHECK-SAME: (%[[EXTENTS:.*]]: tensor<?xindex>, %[[IDX:.*]]: index) -> index |
| func @get_extent_from_extent_tensor(%extents : tensor<?xindex>, %idx : index) |
| -> index { |
| // CHECK: %[[RESULT:.*]] = tensor.extract %[[EXTENTS]][%[[IDX]]] : tensor<?xindex> |
| // CHECK: return %[[RESULT]] : index |
| %result = shape.get_extent %extents, %idx : tensor<?xindex>, index -> index |
| return %result : index |
| } |
| |
| // ----- |
| |
| // Lower `const_shape` to `tensor.from_elements`. |
| // CHECK-LABEL: @const_shape |
| // CHECK-SAME: () -> tensor<3xindex> |
| func @const_shape() -> tensor<3xindex> { |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[C2:.*]] = arith.constant 2 : index |
| // CHECK: %[[C3:.*]] = arith.constant 3 : index |
| // CHECK: %[[TENSOR3:.*]] = tensor.from_elements %[[C1]], %[[C2]], %[[C3]] |
| // CHECK: %[[RESULT:.*]] = tensor.cast %[[TENSOR3]] : tensor<3xindex> to tensor<3xindex> |
| // CHECK: return %[[RESULT]] : tensor<3xindex> |
| %shape = shape.const_shape [1, 2, 3] : tensor<3xindex> |
| return %shape : tensor<3xindex> |
| } |
| |
| // ----- |
| |
| // Lower `const_shape` in the case of rank 0. |
| // CHECK-LABEL: func @const_shape_zero_elements |
| // CHECK-SAME: () -> tensor<0xindex> |
| func @const_shape_zero_elements() -> tensor<0xindex> { |
| // CHECK: %[[TENSOR:.*]] = tensor.from_elements : tensor<0xindex> |
| // CHECK: %[[RESULT:.*]] = tensor.cast %[[TENSOR]] : tensor<0xindex> to tensor<0xindex> |
| // CHECK: return %[[RESULT]] : tensor<0xindex> |
| %shape = shape.const_shape [] : tensor<0xindex> |
| return %shape : tensor<0xindex> |
| } |
| |
| // ----- |
| |
| // Lower `any` to its first operand. |
| // CHECK-LABEL: @any_of_three |
| // CHECK-SAME: (%[[A:.*]]: tensor<?xindex>, %[[B:.*]]: tensor<?xindex>, %[[C:.*]]: tensor<?xindex>) -> tensor<?xindex> |
| func @any_of_three(%a : tensor<?xindex>, |
| %b : tensor<?xindex>, |
| %c : tensor<?xindex>) -> tensor<?xindex> { |
| // CHECK: return %[[A]] : tensor<?xindex> |
| %result = "shape.any"(%a, %b, %c) : (tensor<?xindex>, tensor<?xindex>, tensor<?xindex>) -> tensor<?xindex> |
| return %result : tensor<?xindex> |
| } |
| |
| // ----- |
| |
| // Lower `any` to its first operand. |
| // CHECK-LABEL: @any_of_one |
| // CHECK-SAME: (%[[A:.*]]: tensor<?xindex>) -> tensor<?xindex> |
| func @any_of_one(%a : tensor<?xindex>) -> tensor<?xindex> { |
| // CHECK: return %[[A]] : tensor<?xindex> |
| %result = "shape.any"(%a) : (tensor<?xindex>) -> tensor<?xindex> |
| return %result : tensor<?xindex> |
| } |
| |
| // ----- |
| |
| // Lower 'const_size` to `arith.constant` |
| // CHECK-LABEL: @const_size |
| func @const_size() -> index { |
| // CHECK: %[[RES:.*]] = arith.constant 42 : index |
| %size = shape.const_size 42 |
| %result = shape.size_to_index %size : !shape.size |
| // CHECK: return %[[RES]] |
| return %result : index |
| } |
| |
| // ----- |
| |
| // Lower `to_extent_tensor` to `tensor.cast` |
| // Fold to_extent_tensor when already on tensor. |
| // CHECK-LABEL: @to_extent_tensor |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<?xindex> |
| func @to_extent_tensor(%arg: tensor<?xindex>) -> tensor<3xindex> { |
| // CHECK-NOT: to_extent_tensor |
| // CHECK: %[[RES:.*]] = tensor.cast %[[ARG]] : tensor<?xindex> to tensor<3xindex |
| %casted = shape.to_extent_tensor %arg : tensor<?xindex> -> tensor<3xindex> |
| // CHECK: return %[[RES]] |
| return %casted : tensor<3xindex> |
| } |
| |
| // CHECK-LABEL: @shape_reduce |
| // CHECK-SAME: (%[[SHAPE:.*]]: tensor<?xindex>) -> index |
| func @shape_reduce(%shape : tensor<?xindex>) -> index { |
| %init = arith.constant 1 : index |
| %num_elements = shape.reduce(%shape, %init) : tensor<?xindex> -> index { |
| ^bb0(%index : index, %extent : index, %acc: index): |
| %new_acc = arith.muli %acc, %extent : index |
| shape.yield %new_acc : index |
| } |
| return %num_elements : index |
| } |
| // CHECK-NEXT: %[[INIT:.*]] = arith.constant 1 : index |
| // CHECK-NEXT: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK-NEXT: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK-NEXT: %[[RANK:.*]] = tensor.dim %[[SHAPE]], %[[C0]] : tensor<?xindex> |
| // CHECK-NEXT: %[[RESULT:.*]] = scf.for %[[I:.*]] = %[[C0]] to %[[RANK]] step %[[C1]] iter_args(%[[ACC:.*]] = %[[INIT]]) -> (index) |
| // CHECK-NEXT: %[[EXTENT:.*]] = tensor.extract %[[SHAPE]][%[[I]]] |
| // CHECK-NEXT: %[[NEW_ACC:.*]] = arith.muli %[[ACC]], %[[EXTENT]] : index |
| // CHECK-NEXT: scf.yield %[[NEW_ACC]] : index |
| // CHECK-NEXT: } |
| // CHECK-NEXT: return %[[RESULT]] : index |
| |
| // ----- |
| |
| // Don't lower `shape_of` for result type of `shape.shape`. |
| // CHECK-LABEL: @shape_of |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<*xf32>) |
| func @shape_of(%arg : tensor<*xf32>) { |
| // CHECK: shape.shape |
| %shape = shape.shape_of %arg : tensor<*xf32> -> !shape.shape |
| return |
| } |
| |
| // ----- |
| |
| // Lower `shape_of` for unranked tensors. |
| // CHECK-LABEL: @shape_of_unranked |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<*xf32>) |
| func @shape_of_unranked(%arg : tensor<*xf32>) { |
| // CHECK: %[[RANK:.*]] = rank %[[ARG]] : tensor<*xf32> |
| // CHECK: %[[SHAPE:.*]] = tensor.generate %[[RANK]] { |
| // CHECK: ^bb0(%[[I:.*]]: index): |
| // CHECK: %[[EXTENT:.*]] = tensor.dim %[[ARG]], %[[I]] : tensor<*xf32> |
| // CHECK: yield %[[EXTENT]] : index |
| // CHECK: } : tensor<?xindex> |
| %shape = shape.shape_of %arg : tensor<*xf32> -> tensor<?xindex> |
| return |
| } |
| |
| // ----- |
| |
| // Don't lower `shape_of` with `shape.shape` type. |
| // CHECK-LABEL: @shape_of |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<1x2x3xf32>) |
| func @shape_of_stat(%arg : tensor<1x2x3xf32>) { |
| // CHECK: shape.shape_of %[[ARG]] : tensor<1x2x3xf32> -> !shape.shape |
| %shape = shape.shape_of %arg : tensor<1x2x3xf32> -> !shape.shape |
| return |
| } |
| |
| // ----- |
| |
| // Lower `shape_of` for statically shaped tensor. |
| // CHECK-LABEL: @shape_of_stat |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<1x2x3xf32>) |
| func @shape_of_stat(%arg : tensor<1x2x3xf32>) { |
| // CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index |
| // CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index |
| // CHECK-DAG: %[[SHAPE_UNCASTED:.*]] = tensor.from_elements %[[C1]], %[[C2]], %[[C3]] : tensor<3xindex> |
| %shape = shape.shape_of %arg : tensor<1x2x3xf32> -> tensor<?xindex> |
| return |
| } |
| |
| // ----- |
| |
| // Lower `shape_of` for 0-D tensor. |
| // CHECK-LABEL: @shape_of_zero_d |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<f32>) |
| func @shape_of_zero_d(%arg : tensor<f32>) { |
| // CHECK-DAG: %[[SHAPE_UNCASTED:.*]] = tensor.from_elements : tensor<0xindex> |
| %shape = shape.shape_of %arg : tensor<f32> -> tensor<?xindex> |
| return |
| } |
| |
| // ----- |
| |
| // Lower `shape_of` for dynamically shaped tensor. |
| // CHECK-LABEL: @shape_of_dyn |
| // CHECK-SAME: (%[[ARG:.*]]: tensor<1x5x?xf32>) |
| func @shape_of_dyn(%arg : tensor<1x5x?xf32>) { |
| // CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C5:.*]] = arith.constant 5 : index |
| // CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index |
| // CHECK-DAG: %[[DYN_DIM:.*]] = tensor.dim %[[ARG]], %[[C2]] : tensor<1x5x?xf32> |
| // CHECK-DAG: %[[SHAPE_UNCASTED:.*]] = tensor.from_elements %[[C1]], %[[C5]], %[[DYN_DIM]] : tensor<3xindex> |
| %shape = shape.shape_of %arg : tensor<1x5x?xf32> -> tensor<?xindex> |
| return |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @shape_eq |
| // CHECK-SAME: (%[[A:.*]]: tensor<?xindex>, %[[B:.*]]: tensor<?xindex>) -> i1 |
| func @shape_eq(%a : tensor<?xindex>, %b : tensor<?xindex>) -> i1 { |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[RANK_A:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?xindex> |
| // CHECK: %[[RANK_B:.*]] = tensor.dim %[[B]], %[[C0]] : tensor<?xindex> |
| // CHECK: %[[RANK_EQ:.*]] = arith.cmpi eq, %[[RANK_A]], %[[RANK_B]] |
| // CHECK: %[[SHAPE_EQ:.*]] = scf.if %[[RANK_EQ]] -> (i1) { |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[INIT:.*]] = arith.constant true |
| // CHECK: %[[SHAPE_EQ_INNER:.*]] = scf.for %[[I:.*]] = %[[C0]] to %[[RANK_A]] step %[[C1]] iter_args(%[[CONJ:.*]] = %[[INIT]]) -> (i1) { |
| // CHECK: %[[EXTENT_A:.*]] = tensor.extract %[[A]][%[[I]]] : tensor<?xindex> |
| // CHECK: %[[EXTENT_B:.*]] = tensor.extract %[[B]][%[[I]]] : tensor<?xindex> |
| // CHECK: %[[EXTENT_EQ:.*]] = arith.cmpi eq, %[[EXTENT_A]], %[[EXTENT_B]] |
| // CHECK: %[[CONJ_NEXT:.*]] = arith.andi %[[CONJ]], %[[EXTENT_EQ]] |
| // CHECK: scf.yield %[[CONJ_NEXT]] : i1 |
| // CHECK: } |
| // CHECK: scf.yield %[[SHAPE_EQ_INNER]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHAPE_EQ_INNER:.*]] = arith.constant false |
| // CHECK: scf.yield %[[SHAPE_EQ_INNER]] : i1 |
| // CHECK: } |
| // CHECK: return %[[SHAPE_EQ]] : i1 |
| %result = shape.shape_eq %a, %b : tensor<?xindex>, tensor<?xindex> |
| return %result : i1 |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: @shape_eq |
| // CHECK-SAME: (%[[A:.*]]: tensor<?xindex>, %[[B:.*]]: tensor<?xindex>, %[[C:.*]]: tensor<?xindex>) -> i1 |
| func @shape_eq(%a : tensor<?xindex>, %b : tensor<?xindex>, %c : tensor<?xindex>) -> i1 { |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[RANK_A:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?xindex> |
| // CHECK: %[[RANK_B:.*]] = tensor.dim %[[B]], %[[C0]] : tensor<?xindex> |
| // CHECK: %[[RANK_EQ:.*]] = arith.cmpi eq, %[[RANK_A]], %[[RANK_B]] |
| // CHECK: %[[SHAPE_EQ:.*]] = scf.if %[[RANK_EQ]] -> (i1) { |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[INIT:.*]] = arith.constant true |
| // CHECK: %[[SHAPE_EQ_INNER:.*]] = scf.for %[[I:.*]] = %[[C0]] to %[[RANK_A]] step %[[C1]] iter_args(%[[CONJ:.*]] = %[[INIT]]) -> (i1) { |
| // CHECK: %[[EXTENT_A:.*]] = tensor.extract %[[A]][%[[I]]] : tensor<?xindex> |
| // CHECK: %[[EXTENT_B:.*]] = tensor.extract %[[B]][%[[I]]] : tensor<?xindex> |
| // CHECK: %[[EXTENT_EQ:.*]] = arith.cmpi eq, %[[EXTENT_A]], %[[EXTENT_B]] |
| // CHECK: %[[CONJ_NEXT:.*]] = arith.andi %[[CONJ]], %[[EXTENT_EQ]] |
| // CHECK: scf.yield %[[CONJ_NEXT]] : i1 |
| // CHECK: } |
| // CHECK: scf.yield %[[SHAPE_EQ_INNER]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHAPE_EQ_INNER:.*]] = arith.constant false |
| // CHECK: scf.yield %[[SHAPE_EQ_INNER]] : i1 |
| // CHECK: } |
| // CHECK: %[[RANK_C:.*]] = tensor.dim %[[C]], %[[C0]] : tensor<?xindex> |
| // CHECK: %[[RANK_EQ:.*]] = arith.cmpi eq, %[[RANK_A]], %[[RANK_C]] |
| // CHECK: %[[SHAPE_EQ2:.*]] = scf.if %[[RANK_EQ]] -> (i1) { |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[INIT:.*]] = arith.constant true |
| // CHECK: %[[SHAPE_EQ_INNER:.*]] = scf.for %[[I:.*]] = %[[C0]] to %[[RANK_A]] step %[[C1]] iter_args(%[[CONJ:.*]] = %[[INIT]]) -> (i1) { |
| // CHECK: %[[EXTENT_A:.*]] = tensor.extract %[[A]][%[[I]]] : tensor<?xindex> |
| // CHECK: %[[EXTENT_C:.*]] = tensor.extract %[[C]][%[[I]]] : tensor<?xindex> |
| // CHECK: %[[EXTENT_EQ:.*]] = arith.cmpi eq, %[[EXTENT_A]], %[[EXTENT_C]] |
| // CHECK: %[[CONJ_NEXT:.*]] = arith.andi %[[CONJ]], %[[EXTENT_EQ]] |
| // CHECK: scf.yield %[[CONJ_NEXT]] : i1 |
| // CHECK: } |
| // CHECK: scf.yield %[[SHAPE_EQ_INNER]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHAPE_EQ_INNER:.*]] = arith.constant false |
| // CHECK: scf.yield %[[SHAPE_EQ_INNER]] : i1 |
| // CHECK: } |
| // CHECK: %[[RESULT:.*]] = arith.andi %[[SHAPE_EQ]], %[[SHAPE_EQ2]] : i1 |
| // CHECK: return %[[RESULT]] : i1 |
| %result = shape.shape_eq %a, %b, %c : tensor<?xindex>, tensor<?xindex>, tensor<?xindex> |
| return %result : i1 |
| } |
| |
| // ----- |
| |
| // Don't lower `shape.broadcast` if a `shape.shape` type is involved. |
| // CHECK-LABEL: @broadcast |
| func @broadcast(%a : tensor<?xindex>, %b : !shape.shape) -> !shape.shape { |
| // CHECK: shape.broadcast |
| %c = shape.broadcast %a, %b : tensor<?xindex>, !shape.shape -> !shape.shape |
| return %c : !shape.shape |
| } |
| |
| // ----- |
| |
| func @try_is_broadcastable (%a : tensor<2xindex>, %b : tensor<3xindex>, %c : tensor<2xindex>) -> i1 { |
| %0 = shape.is_broadcastable %a, %b, %c : tensor<2xindex>, tensor<3xindex>, tensor<2xindex> |
| return %0 : i1 |
| } |
| // CHECK-LABEL: @try_is_broadcastable |
| // CHECK-SAME: %[[ARG0:.*]]: tensor<2xindex>, |
| // CHECK-SAME: %[[ARG1:.*]]: tensor<3xindex>, |
| // CHECK-SAME: %[[ARG2:.*]]: tensor<2xindex>) |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[RANK0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<2xindex> |
| // CHECK: %[[RANK1:.*]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<3xindex> |
| // CHECK: %[[RANK2:.*]] = tensor.dim %[[ARG2]], %[[C0]] : tensor<2xindex> |
| // CHECK: %[[CMP0:.*]] = arith.cmpi ugt, %[[RANK1]], %[[RANK0]] : index |
| // CHECK: %[[LARGER_DIM:.*]] = select %[[CMP0]], %[[RANK1]], %[[RANK0]] : index |
| // CHECK: %[[CMP1:.*]] = arith.cmpi ugt, %[[RANK2]], %[[LARGER_DIM]] : index |
| // CHECK: %[[MAX_RANK:.*]] = select %[[CMP1]], %[[RANK2]], %[[LARGER_DIM]] : index |
| // CHECK: %[[DIM_DIFF0:.*]] = arith.subi %[[MAX_RANK]], %[[RANK0]] : index |
| // CHECK: %[[DIM_DIFF1:.*]] = arith.subi %[[MAX_RANK]], %[[RANK1]] : index |
| // CHECK: %[[DIM_DIFF2:.*]] = arith.subi %[[MAX_RANK]], %[[RANK2]] : index |
| // CHECK: %[[TRUE:.*]] = arith.constant true |
| // CHECK: %[[ALL_RESULT:.*]] = scf.for %[[IDX:.*]] = %[[C0]] to %[[MAX_RANK]] step %[[C1]] iter_args(%[[ALL_SO_FAR:.*]] = %[[TRUE]]) -> (i1) { |
| // CHECK: %[[C1_0:.*]] = arith.constant 1 : index |
| // CHECK: %[[OUTBOUNDS0:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[DIM0:.*]] = scf.if %[[OUTBOUNDS0]] -> (index) { |
| // CHECK: scf.yield %[[C1_0]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX0:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[EXTRACTED_0:.*]] = tensor.extract %[[ARG0]]{{\[}}%[[IDX0]]] : tensor<2xindex> |
| // CHECK: %[[DIM0_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_0:.*]], %[[C1_0]] : index |
| // CHECK: %[[MAX_DIM0:.*]] = select %[[DIM0_IS_1]], %[[C1_0]], %[[EXTRACTED_0]] : index |
| // CHECK: } |
| // CHECK: %[[VAL_28:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[DIM1:.*]] = scf.if %[[VAL_28]] -> (index) { |
| // CHECK: scf.yield %[[DIM0]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX1:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[EXTRACTED_1:.*]] = tensor.extract %[[ARG1]]{{\[}}%[[IDX1]]] : tensor<3xindex> |
| // CHECK: %[[DIM1_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_1:.*]], %[[C1_0]] : index |
| // CHECK: %[[MAX_DIM1:.*]] = select %[[DIM1_IS_1]], %[[DIM0]], %[[EXTRACTED_1]] : index |
| // CHECK: } |
| // CHECK: %[[VAL_36:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[DIM2:.*]] = scf.if %[[VAL_36]] -> (index) { |
| // CHECK: scf.yield %[[DIM1]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX2:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[EXTRACTED_2:.*]] = tensor.extract %[[ARG2]]{{\[}}%[[IDX2]]] : tensor<2xindex> |
| // CHECK: %[[DIM2_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_2]], %[[C1_0]] : index |
| // CHECK: %[[MAX_DIM2:.*]] = select %[[DIM2_IS_1]], %[[DIM1]], %[[EXTRACTED_2]] : index |
| // CHECK: } |
| // CHECK: %[[OUT_BOUND_0:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[REDUCTION_0:.*]] = scf.if %[[OUT_BOUND_0]] -> (i1) { |
| // CHECK: scf.yield %[[ALL_SO_FAR]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHIFTED:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[EXTRACTED:.*]] = tensor.extract %arg0[%[[SHIFTED]]] : tensor<2xindex> |
| // CHECK: %[[EQUALS_1:.*]] = arith.cmpi eq, %[[EXTRACTED]], %c1 : index |
| // CHECK: %[[EQUALS_BROADCASTED:.*]] = arith.cmpi eq, %[[EXTRACTED]], %[[DIM2]] : index |
| // CHECK: %[[GOOD:.*]] = arith.ori %[[EQUALS_1]], %[[EQUALS_BROADCASTED]] : i1 |
| // CHECK: %[[AND_REDUCTION:.*]] = arith.andi %[[ALL_SO_FAR]], %[[GOOD]] : i1 |
| // CHECK: scf.yield %[[AND_REDUCTION]] : i1 |
| // CHECK: } |
| // CHECK: %[[OUT_BOUND_1:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[SECOND_REDUCTION:.*]] = scf.if %[[OUT_BOUND_1]] -> (i1) { |
| // CHECK: scf.yield %[[REDUCTION_0]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHIFTED:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[EXTRACTED:.*]] = tensor.extract %arg1[%[[SHIFTED]]] : tensor<3xindex> |
| // CHECK: %[[EQUALS_1:.*]] = arith.cmpi eq, %[[EXTRACTED]], %c1 : index |
| // CHECK: %[[EQUALS_BROADCASTED:.*]] = arith.cmpi eq, %[[EXTRACTED]], %[[DIM2]] : index |
| // CHECK: %[[GOOD:.*]] = arith.ori %[[EQUALS_1]], %[[EQUALS_BROADCASTED]] : i1 |
| // CHECK: %[[AND_REDUCTION:.*]] = arith.andi %[[REDUCTION_0]], %[[GOOD]] : i1 |
| // CHECK: scf.yield %[[AND_REDUCTION]] : i1 |
| // CHECK: } |
| // CHECK: %[[OUT_BOUND_2:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[FINAL_RESULT:.*]] = scf.if %[[OUT_BOUND_2]] -> (i1) { |
| // CHECK: scf.yield %[[SECOND_REDUCTION]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHIFTED:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[EXTRACTED:.*]] = tensor.extract %arg2[%[[SHIFTED]]] : tensor<2xindex> |
| // CHECK: %[[EQUALS_1:.*]] = arith.cmpi eq, %[[EXTRACTED:.*]], %c1 : index |
| // CHECK: %[[EQUALS_BROADCASTED:.*]] = arith.cmpi eq, %[[EXTRACTED:.*]], %[[DIM2]] : index |
| // CHECK: %[[GOOD:.*]] = arith.ori %[[EQUALS_1:.*]], %[[EQUALS_BROADCASTED:.*]] : i1 |
| // CHECK: %[[AND_REDUCTION:.*]] = arith.andi %[[SECOND_REDUCTION]], %[[GOOD]] : i1 |
| // CHECK: scf.yield %[[AND_REDUCTION]] : i1 |
| // CHECK: } |
| // CHECK: scf.yield %[[FINAL_RESULT]] : i1 |
| |
| // ----- |
| |
| func @broadcast(%a : tensor<2xindex>, %b : tensor<3xindex>, %c : tensor<2xindex>) -> !shape.witness { |
| %0 = shape.cstr_broadcastable %a, %b, %c : tensor<2xindex>, tensor<3xindex>, tensor<2xindex> |
| return %0 : !shape.witness |
| } |
| // CHECK-LABEL: func @broadcast( |
| // CHECK-SAME: %[[ARG0:.*]]: tensor<2xindex>, |
| // CHECK-SAME: %[[ARG1:.*]]: tensor<3xindex>, |
| // CHECK-SAME: %[[ARG2:.*]]: tensor<2xindex>) |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[RANK0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<2xindex> |
| // CHECK: %[[RANK1:.*]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<3xindex> |
| // CHECK: %[[RANK2:.*]] = tensor.dim %[[ARG2]], %[[C0]] : tensor<2xindex> |
| // CHECK: %[[CMP0:.*]] = arith.cmpi ugt, %[[RANK1]], %[[RANK0]] : index |
| // CHECK: %[[LARGER_DIM:.*]] = select %[[CMP0]], %[[RANK1]], %[[RANK0]] : index |
| // CHECK: %[[CMP1:.*]] = arith.cmpi ugt, %[[RANK2]], %[[LARGER_DIM]] : index |
| // CHECK: %[[MAX_RANK:.*]] = select %[[CMP1]], %[[RANK2]], %[[LARGER_DIM]] : index |
| // CHECK: %[[DIM_DIFF0:.*]] = arith.subi %[[MAX_RANK]], %[[RANK0]] : index |
| // CHECK: %[[DIM_DIFF1:.*]] = arith.subi %[[MAX_RANK]], %[[RANK1]] : index |
| // CHECK: %[[DIM_DIFF2:.*]] = arith.subi %[[MAX_RANK]], %[[RANK2]] : index |
| // CHECK: %[[TRUE:.*]] = arith.constant true |
| // CHECK: %[[ALL_RESULT:.*]] = scf.for %[[IDX:.*]] = %[[C0]] to %[[MAX_RANK]] step %[[C1]] iter_args(%[[ALL_SO_FAR:.*]] = %[[TRUE]]) -> (i1) { |
| // CHECK: %[[C1_0:.*]] = arith.constant 1 : index |
| // CHECK: %[[OUTBOUNDS0:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[DIM0:.*]] = scf.if %[[OUTBOUNDS0]] -> (index) { |
| // CHECK: scf.yield %[[C1_0]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX0:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[EXTRACTED_0:.*]] = tensor.extract %[[ARG0]]{{\[}}%[[IDX0]]] : tensor<2xindex> |
| // CHECK: %[[DIM0_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_0:.*]], %[[C1_0]] : index |
| // CHECK: %[[MAX_DIM0:.*]] = select %[[DIM0_IS_1]], %[[C1_0]], %[[EXTRACTED_0]] : index |
| // CHECK: } |
| // CHECK: %[[VAL_28:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[DIM1:.*]] = scf.if %[[VAL_28]] -> (index) { |
| // CHECK: scf.yield %[[DIM0]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX1:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[EXTRACTED_1:.*]] = tensor.extract %[[ARG1]]{{\[}}%[[IDX1]]] : tensor<3xindex> |
| // CHECK: %[[DIM1_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_1:.*]], %[[C1_0]] : index |
| // CHECK: %[[MAX_DIM1:.*]] = select %[[DIM1_IS_1]], %[[DIM0]], %[[EXTRACTED_1]] : index |
| // CHECK: } |
| // CHECK: %[[VAL_36:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[DIM2:.*]] = scf.if %[[VAL_36]] -> (index) { |
| // CHECK: scf.yield %[[DIM1]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX2:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[EXTRACTED_2:.*]] = tensor.extract %[[ARG2]]{{\[}}%[[IDX2]]] : tensor<2xindex> |
| // CHECK: %[[DIM2_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_2]], %[[C1_0]] : index |
| // CHECK: %[[MAX_DIM2:.*]] = select %[[DIM2_IS_1]], %[[DIM1]], %[[EXTRACTED_2]] : index |
| // CHECK: } |
| // CHECK: %[[OUT_BOUND_0:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[REDUCTION_0:.*]] = scf.if %[[OUT_BOUND_0]] -> (i1) { |
| // CHECK: scf.yield %[[ALL_SO_FAR]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHIFTED:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[EXTRACTED:.*]] = tensor.extract %arg0[%[[SHIFTED]]] : tensor<2xindex> |
| // CHECK: %[[EQUALS_1:.*]] = arith.cmpi eq, %[[EXTRACTED]], %c1 : index |
| // CHECK: %[[EQUALS_BROADCASTED:.*]] = arith.cmpi eq, %[[EXTRACTED]], %[[DIM2]] : index |
| // CHECK: %[[GOOD:.*]] = arith.ori %[[EQUALS_1]], %[[EQUALS_BROADCASTED]] : i1 |
| // CHECK: %[[AND_REDUCTION:.*]] = arith.andi %[[ALL_SO_FAR]], %[[GOOD]] : i1 |
| // CHECK: scf.yield %[[AND_REDUCTION]] : i1 |
| // CHECK: } |
| // CHECK: %[[OUT_BOUND_1:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[SECOND_REDUCTION:.*]] = scf.if %[[OUT_BOUND_1]] -> (i1) { |
| // CHECK: scf.yield %[[REDUCTION_0]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHIFTED:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[EXTRACTED:.*]] = tensor.extract %arg1[%[[SHIFTED]]] : tensor<3xindex> |
| // CHECK: %[[EQUALS_1:.*]] = arith.cmpi eq, %[[EXTRACTED]], %c1 : index |
| // CHECK: %[[EQUALS_BROADCASTED:.*]] = arith.cmpi eq, %[[EXTRACTED]], %[[DIM2]] : index |
| // CHECK: %[[GOOD:.*]] = arith.ori %[[EQUALS_1]], %[[EQUALS_BROADCASTED]] : i1 |
| // CHECK: %[[AND_REDUCTION:.*]] = arith.andi %[[REDUCTION_0]], %[[GOOD]] : i1 |
| // CHECK: scf.yield %[[AND_REDUCTION]] : i1 |
| // CHECK: } |
| // CHECK: %[[OUT_BOUND_2:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[FINAL_RESULT:.*]] = scf.if %[[OUT_BOUND_2]] -> (i1) { |
| // CHECK: scf.yield %[[SECOND_REDUCTION]] : i1 |
| // CHECK: } else { |
| // CHECK: %[[SHIFTED:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[EXTRACTED:.*]] = tensor.extract %arg2[%[[SHIFTED]]] : tensor<2xindex> |
| // CHECK: %[[EQUALS_1:.*]] = arith.cmpi eq, %[[EXTRACTED:.*]], %c1 : index |
| // CHECK: %[[EQUALS_BROADCASTED:.*]] = arith.cmpi eq, %[[EXTRACTED:.*]], %[[DIM2]] : index |
| // CHECK: %[[GOOD:.*]] = arith.ori %[[EQUALS_1:.*]], %[[EQUALS_BROADCASTED:.*]] : i1 |
| // CHECK: %[[AND_REDUCTION:.*]] = arith.andi %[[SECOND_REDUCTION]], %[[GOOD]] : i1 |
| // CHECK: scf.yield %[[AND_REDUCTION]] : i1 |
| // CHECK: } |
| // CHECK: scf.yield %[[FINAL_RESULT]] : i1 |
| |
| // CHECK: %[[RESULT:.*]] = shape.cstr_require %[[ALL_RESULT]], "required broadcastable shapes" |
| // CHECK: return %[[RESULT]] : !shape.witness |
| // CHECK: } |
| |
| // ----- |
| |
| func @broadcast_3_shapes_different_extents(%a : tensor<2xindex>, |
| %b : tensor<3xindex>, |
| %c : tensor<2xindex>) { |
| // CHECK-LABEL: func @broadcast_3_shapes_different_extents( |
| // CHECK-SAME: %[[ARG0:.*]]: tensor<2xindex>, |
| // CHECK-SAME: %[[ARG1:.*]]: tensor<3xindex>, |
| // CHECK-SAME: %[[ARG2:.*]]: tensor<2xindex>) { |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[RANK0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<2xindex> |
| // CHECK: %[[RANK1:.*]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<3xindex> |
| // CHECK: %[[RANK2:.*]] = tensor.dim %[[ARG2]], %[[C0]] : tensor<2xindex> |
| // CHECK: %[[CMP0:.*]] = arith.cmpi ugt, %[[RANK1]], %[[RANK0]] : index |
| // CHECK: %[[LARGER_DIM:.*]] = select %[[CMP0]], %[[RANK1]], %[[RANK0]] : index |
| // CHECK: %[[CMP1:.*]] = arith.cmpi ugt, %[[RANK2]], %[[LARGER_DIM]] : index |
| // CHECK: %[[MAX_RANK:.*]] = select %[[CMP1]], %[[RANK2]], %[[LARGER_DIM]] : index |
| // CHECK: %[[DIM_DIFF0:.*]] = arith.subi %[[MAX_RANK]], %[[RANK0]] : index |
| // CHECK: %[[DIM_DIFF1:.*]] = arith.subi %[[MAX_RANK]], %[[RANK1]] : index |
| // CHECK: %[[DIM_DIFF2:.*]] = arith.subi %[[MAX_RANK]], %[[RANK2]] : index |
| // CHECK: %[[RESULT:.*]] = tensor.generate %[[MAX_RANK]] { |
| // CHECK: ^bb0(%[[IDX:.*]]: index): |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[OUTBOUNDS0:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[DIM0:.*]] = scf.if %[[OUTBOUNDS0]] -> (index) { |
| // CHECK: scf.yield %[[C1]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX0:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF0]] : index |
| // CHECK: %[[EXTRACTED_0:.*]] = tensor.extract %[[ARG0]]{{\[}}%[[IDX0]]] : tensor<2xindex> |
| // CHECK: %[[DIM0_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_0:.*]], %[[C1]] : index |
| // CHECK: %[[MAX_DIM0:.*]] = select %[[DIM0_IS_1]], %[[C1]], %[[EXTRACTED_0]] : index |
| // CHECK: } |
| // CHECK: %[[VAL_28:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[DIM1:.*]] = scf.if %[[VAL_28]] -> (index) { |
| // CHECK: scf.yield %[[DIM0]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX1:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF1]] : index |
| // CHECK: %[[EXTRACTED_1:.*]] = tensor.extract %[[ARG1]]{{\[}}%[[IDX1]]] : tensor<3xindex> |
| // CHECK: %[[DIM1_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_1:.*]], %[[C1]] : index |
| // CHECK: %[[MAX_DIM1:.*]] = select %[[DIM1_IS_1]], %[[DIM0]], %[[EXTRACTED_1]] : index |
| // CHECK: } |
| // CHECK: %[[VAL_36:.*]] = arith.cmpi ult, %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[DIM2:.*]] = scf.if %[[VAL_36]] -> (index) { |
| // CHECK: scf.yield %[[DIM1]] : index |
| // CHECK: } else { |
| // CHECK: %[[IDX2:.*]] = arith.subi %[[IDX]], %[[DIM_DIFF2]] : index |
| // CHECK: %[[EXTRACTED_2:.*]] = tensor.extract %[[ARG2]]{{\[}}%[[IDX2]]] : tensor<2xindex> |
| // CHECK: %[[DIM2_IS_1:.*]] = arith.cmpi eq, %[[EXTRACTED_2:.*]], %[[C1]] : index |
| // CHECK: %[[MAX_DIM2:.*]] = select %[[DIM2_IS_1]], %[[DIM1]], %[[EXTRACTED_2]] : index |
| // CHECK: } |
| // CHECK: tensor.yield %[[DIM2]] : index |
| // CHECK: } : tensor<?xindex> |
| // CHECK: return |
| // CHECK: } |
| %0 = shape.broadcast %a, %b, %c |
| : tensor<2xindex>, tensor<3xindex>, tensor<2xindex> -> tensor<?xindex> |
| return |
| } |
| |
| // ---- |
| |
| // CHECK-LABEL: @broadcast_to_known_rank |
| func @broadcast_to_known_rank(%a : tensor<1xindex>, %b : tensor<3xindex>) |
| -> tensor<3xindex> { |
| // CHECK: %[[RES:.*]] = tensor.cast %{{.*}} : tensor<?xindex> to tensor<3xindex> |
| // CHECK: return %[[RES]] : tensor<3xindex> |
| %0 = shape.broadcast %a, %b : tensor<1xindex>, tensor<3xindex> -> tensor<3xindex> |
| return %0 : tensor<3xindex> |
| } |
| |
| // ----- |
| |
| // Lower `split_at` |
| // CHECK-LABEL: @split_at |
| // CHECK-SAME: %[[SHAPE:.*]]: tensor<?xindex>, %[[INDEX:.*]]: index |
| func @split_at(%shape: tensor<?xindex>, %index: index) -> (tensor<?xindex>, tensor<?xindex>) { |
| // CHECK-NEXT: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK-NEXT: %[[RANK:.*]] = tensor.dim %[[SHAPE]], %[[C0]] : tensor<?xindex> |
| // CHECK-NEXT: %[[POSINDEX:.*]] = arith.addi %[[INDEX]], %[[RANK]] : index |
| // CHECK-NEXT: %[[ISNEG:.*]] = arith.cmpi slt, %[[INDEX]], %[[C0]] : index |
| // CHECK-NEXT: %[[SELECT:.*]] = select %[[ISNEG]], %[[POSINDEX]], %[[INDEX]] : index |
| // CHECK-NEXT: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK-NEXT: %[[HEAD:.*]] = tensor.extract_slice %[[SHAPE]][%[[C0]]] [%[[SELECT]]] [%[[C1]]] : tensor<?xindex> to tensor<?xindex> |
| // CHECK-NEXT: %[[TAIL_SIZE:.*]] = arith.subi %[[RANK]], %[[SELECT]] : index |
| // CHECK-NEXT: %[[TAIL:.*]] = tensor.extract_slice %[[SHAPE]][%[[SELECT]]] [%[[TAIL_SIZE]]] [%[[C1]]] : tensor<?xindex> to tensor<?xindex> |
| // CHECK-NEXT: return %[[HEAD]], %[[TAIL]] : tensor<?xindex>, tensor<?xindex> |
| %head, %tail = "shape.split_at"(%shape, %index) : (tensor<?xindex>, index) -> (tensor<?xindex>, tensor<?xindex>) |
| return %head, %tail : tensor<?xindex>, tensor<?xindex> |
| } |
