| // RUN: mlir-opt -transform-interpreter --split-input-file -canonicalize %s | FileCheck %s |
| |
| // CHECK-LABEL: func @tensor_from_elements_0d( |
| // CHECK-SAME: %[[arg0:.*]]: index |
| // CHECK: %[[empty:.*]] = tensor.empty() : tensor<index> |
| // CHECK: %[[insert:.*]] = tensor.insert %[[arg0]] into %[[empty]][] |
| // CHECK: return %[[insert]] |
| func.func @tensor_from_elements_0d(%arg0: index) -> tensor<index> { |
| %0 = tensor.from_elements %arg0 : tensor<index> |
| return %0 : tensor<index> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.from_elements"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @tensor_from_elements_1d( |
| // CHECK-SAME: %[[arg0:.*]]: index, %[[arg1:.*]]: index |
| // CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<2xindex> |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index |
| // CHECK: %[[insert:.*]] = tensor.insert %[[arg0]] into %[[empty]][%[[c0]]] |
| // CHECK: %[[insert2:.*]] = tensor.insert %[[arg1]] into %[[insert]][%[[c1]]] |
| // CHECK: return %[[insert2]] |
| func.func @tensor_from_elements_1d(%arg0: index, %arg1: index) -> tensor<2xindex> { |
| %0 = tensor.from_elements %arg0, %arg1 : tensor<2xindex> |
| return %0 : tensor<2xindex> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.from_elements"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @tensor_from_elements_2d( |
| // CHECK-SAME: %[[arg0:.*]]: index, %[[arg1:.*]]: index |
| // CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<3x2xindex> |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[c2:.*]] = arith.constant 2 : index |
| // CHECK: %[[insert0:.*]] = tensor.insert %[[arg0]] into %[[empty]][%[[c0]], %[[c0]]] |
| // CHECK: %[[insert1:.*]] = tensor.insert %[[arg1]] into %[[insert0]][%[[c0]], %[[c1]]] |
| // CHECK: %[[insert2:.*]] = tensor.insert %[[arg0]] into %[[insert1]][%[[c1]], %[[c0]]] |
| // CHECK: %[[insert3:.*]] = tensor.insert %[[arg1]] into %[[insert2]][%[[c1]], %[[c1]]] |
| // CHECK: %[[insert4:.*]] = tensor.insert %[[arg0]] into %[[insert3]][%[[c2]], %[[c0]]] |
| // CHECK: %[[insert5:.*]] = tensor.insert %[[arg1]] into %[[insert4]][%[[c2]], %[[c1]]] |
| // CHECK: return %[[insert5]] |
| func.func @tensor_from_elements_2d(%arg0: index, %arg1: index) -> tensor<3x2xindex> { |
| %0 = tensor.from_elements %arg0, %arg1, %arg0, %arg1, %arg0, %arg1 |
| : tensor<3x2xindex> |
| return %0 : tensor<3x2xindex> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.from_elements"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$map:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK-LABEL: func @tensor_generate( |
| // CHECK-SAME: %[[s1:.*]]: index, %[[s2:.*]]: index |
| // CHECK: %[[empty:.*]] = tensor.empty(%[[s1]], %[[s2]]) : tensor<?x?xindex> |
| // CHECK: %[[generic:.*]] = linalg.generic |
| // CHECK-SAME: {indexing_maps = [#[[$map]]], iterator_types = ["parallel", "parallel"]} |
| // CHECK-SAME: outs(%[[empty]] : tensor<?x?xindex>) { |
| // CHECK: %[[i0:.*]] = linalg.index 0 |
| // CHECK: %[[i1:.*]] = linalg.index 1 |
| // CHECK: %[[added:.*]] = arith.addi %[[i0]], %[[i1]] |
| // CHECK: linalg.yield %[[added]] |
| // CHECK: } |
| // CHECK: return %[[generic]] |
| func.func @tensor_generate(%s1: index, %s2: index) -> tensor<?x?xindex> { |
| %0 = tensor.generate %s1, %s2 { |
| ^bb0(%arg0: index, %arg1: index): |
| %1 = arith.addi %arg0, %arg1 : index |
| tensor.yield %1 : index |
| } : tensor<?x?xindex> |
| return %0 : tensor<?x?xindex> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.generate"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$map:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 5)> |
| // CHECK: #[[$map1:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 10)> |
| // CHECK: #[[$map2:.+]] = affine_map<(d0, d1) -> (d0, d1)> |
| // CHECK-LABEL: func @tensor_pad( |
| // CHECK-SAME: %[[t1:.*]]: tensor<?x10xindex>, %[[l2:.*]]: index, %[[h1:.*]]: index, %[[h2:.*]]: index |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]] |
| // CHECK-DAG: %[[size0:.*]] = affine.apply #[[$map]]()[%[[h1]], %[[dim0]]] |
| // CHECK-DAG: %[[size1:.*]] = affine.apply #[[$map1]]()[%[[l2]], %[[h2]]] |
| // CHECK: %[[empty:.*]] = tensor.empty(%[[size0]], %[[size1]]) : tensor<?x?xindex> |
| // CHECK: %[[generic:.*]] = linalg.generic |
| // CHECK-SAME: {indexing_maps = [#[[$map2]]], iterator_types = ["parallel", "parallel"]} |
| // CHECK-SAME: outs(%[[empty]] : tensor<?x?xindex>) { |
| // CHECK: %[[i0:.*]] = linalg.index 0 |
| // CHECK: %[[i1:.*]] = linalg.index 1 |
| // CHECK: %[[mul:.*]] = arith.muli %[[i0]], %[[i1]] |
| // CHECK: linalg.yield %[[mul]] |
| // CHECK: } |
| // CHECK-DAG: %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]] |
| // CHECK: %[[inserted:.*]] = tensor.insert_slice %[[t1]] into %[[generic]][5, %[[l2]]] [%[[dim0]], 10] [1, 1] : tensor<?x10xindex> into tensor<?x?xindex> |
| // CHECK: return %[[inserted]] |
| func.func @tensor_pad(%t1: tensor<?x10xindex>, %l2: index, %h1: index, |
| %h2: index) -> tensor<?x?xindex> { |
| %0 = tensor.pad %t1 low[5, %l2] high[%h1, %h2] { |
| ^bb0(%arg0: index, %arg1: index): |
| %m = arith.muli %arg0, %arg1 : index |
| tensor.yield %m : index |
| } : tensor<?x10xindex> to tensor<?x?xindex> |
| return %0 : tensor<?x?xindex> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$map:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 5)> |
| // CHECK: #[[$map1:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 10)> |
| // CHECK-LABEL: func @tensor_pad_constant( |
| // CHECK-SAME: %[[t1:.*]]: tensor<?x10xindex>, %[[l2:.*]]: index, %[[h1:.*]]: index, %[[h2:.*]]: index |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c50:.*]] = arith.constant 50 : index |
| // CHECK-DAG: %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]] |
| // CHECK-DAG: %[[size0:.*]] = affine.apply #[[$map]]()[%[[h1]], %[[dim0]]] |
| // CHECK-DAG: %[[size1:.*]] = affine.apply #[[$map1]]()[%[[l2]], %[[h2]]] |
| // CHECK: %[[empty:.*]] = tensor.empty(%[[size0]], %[[size1]]) : tensor<?x?xindex> |
| // CHECK: %[[filled:.*]] = linalg.fill ins(%[[c50]] : index) outs(%[[empty]] : tensor<?x?xindex>) |
| // CHECK-DAG: %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]] |
| // CHECK: %[[inserted:.*]] = tensor.insert_slice %[[t1]] into %[[filled]][5, %[[l2]]] [%[[dim0]], 10] [1, 1] : tensor<?x10xindex> into tensor<?x?xindex> |
| // CHECK: return %[[inserted]] |
| func.func @tensor_pad_constant(%t1: tensor<?x10xindex>, %l2: index, %h1: index, |
| %h2: index) -> tensor<?x?xindex> { |
| %0 = tensor.pad %t1 low[5, %l2] high[%h1, %h2] { |
| ^bb0(%arg0: index, %arg1: index): |
| %c = arith.constant 50 : index |
| tensor.yield %c : index |
| } : tensor<?x10xindex> to tensor<?x?xindex> |
| return %0 : tensor<?x?xindex> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: #[[$map:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 5)> |
| // CHECK: #[[$map1:.+]] = affine_map<()[s0, s1] -> (s0 + s1 + 10)> |
| // CHECK-LABEL: func @tensor_pad_invariant( |
| // CHECK-SAME: %[[t1:.*]]: tensor<?x10xindex>, %[[l2:.*]]: index, %[[h1:.*]]: index, %[[h2:.*]]: index, %[[padding:.*]]: index |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]] |
| // CHECK-DAG: %[[size0:.*]] = affine.apply #[[$map]]()[%[[h1]], %[[dim0]]] |
| // CHECK-DAG: %[[size1:.*]] = affine.apply #[[$map1]]()[%[[l2]], %[[h2]]] |
| // CHECK: %[[empty:.*]] = tensor.empty(%[[size0]], %[[size1]]) : tensor<?x?xindex> |
| // CHECK: %[[filled:.*]] = linalg.fill ins(%[[padding]] : index) outs(%[[empty]] : tensor<?x?xindex>) |
| // CHECK-DAG: %[[dim0:.*]] = tensor.dim %[[t1]], %[[c0]] |
| // CHECK: %[[inserted:.*]] = tensor.insert_slice %[[t1]] into %[[filled]][5, %[[l2]]] [%[[dim0]], 10] [1, 1] : tensor<?x10xindex> into tensor<?x?xindex> |
| // CHECK: return %[[inserted]] |
| func.func @tensor_pad_invariant(%t1: tensor<?x10xindex>, %l2: index, %h1: index, |
| %h2: index, %padding: index) -> tensor<?x?xindex> { |
| %0 = tensor.pad %t1 low[5, %l2] high[%h1, %h2] { |
| ^bb0(%arg0: index, %arg1: index): |
| tensor.yield %padding : index |
| } : tensor<?x10xindex> to tensor<?x?xindex> |
| return %0 : tensor<?x?xindex> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @tensor_pad_nofold( |
| // CHECK-SAME: %[[t1:.*]]: tensor<?x?xindex>, %[[padding:.*]]: index |
| // CHECK-NOT: linalg.fill |
| // CHECK-NOT: generic |
| // CHECK-NOT: insert_slice |
| // CHECK: %[[alloc_tensor:.*]] = bufferization.alloc_tensor(%{{.*}}) : tensor<?x?xindex> |
| // CHECK: %[[copied:.*]] = linalg.copy ins(%[[t1]] : tensor<?x?xindex>) outs(%[[alloc_tensor]] : tensor<?x?xindex>) -> tensor<?x?xindex> |
| // CHECK: return %[[copied]] |
| func.func @tensor_pad_nofold(%t1: tensor<?x?xindex>, %padding: index) |
| -> tensor<?x?xindex> { |
| %c0 = arith.constant 0 : index |
| %0 = tensor.pad %t1 nofold low[0, %c0] high[%c0, 0] { |
| ^bb0(%arg0: index, %arg1: index): |
| tensor.yield %padding : index |
| } : tensor<?x?xindex> to tensor<?x?xindex> |
| return %0: tensor<?x?xindex> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.rewrite_in_destination_passing_style %0 |
| : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |