| // RUN: mlir-opt %s -transform-interpreter --cse --canonicalize -split-input-file -verify-diagnostics | FileCheck %s |
| // RUN: mlir-opt %s -transform-interpreter -split-input-file -verify-diagnostics | FileCheck %s --check-prefix CHECK-NOCLEANUP |
| |
| // CHECK: func.func @fuse_1st_for_into_2nd([[A:%.*]]: {{.*}}, [[B:%.*]]: {{.*}} |
| func.func @fuse_1st_for_into_2nd(%A: tensor<128xf32>, %B: tensor<128xf32>) -> (tensor<128xf32>, tensor<128xf32>) { |
| // CHECK-DAG: [[C0:%.*]] = arith.constant 0 : index |
| // CHECK-DAG: [[C16:%.*]] = arith.constant 16 : index |
| // CHECK-DAG: [[C128:%.*]] = arith.constant 128 : index |
| // CHECK-DAG: [[ZERO:%.*]] = arith.constant 0.000000e+00 : f32 |
| %c0 = arith.constant 0 : index |
| %c16 = arith.constant 16 : index |
| %c128 = arith.constant 128 : index |
| %cst = arith.constant 0.000000e+00 : f32 |
| // CHECK: [[R0:%.*]]:2 = scf.for [[IV:%.*]] = [[C0]] to [[C128]] step [[C16]] iter_args([[IA:%.*]] = [[A]], [[IB:%.*]] = [[B]]) {{.*}} |
| %1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %A) -> (tensor<128xf32>) { |
| // CHECK-DAG: [[ASLICE:%.*]] = vector.transfer_read [[A]][[[IV]]], [[ZERO]] |
| // CHECK-DAG: [[SLICE0:%.*]] = vector.transfer_read [[IA]][[[IV]]], [[ZERO]] |
| // CHECK: [[OUT1:%.*]] = arith.addf [[SLICE0]], [[ASLICE]] |
| // CHECK-NEXT: [[WRT0:%.*]] = vector.transfer_write [[OUT1]], [[IA]][[[IV]]] |
| %2 = vector.transfer_read %A[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %5 = arith.addf %3, %2 : vector<16xf32> |
| %6 = vector.transfer_write %5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| scf.yield %6 : tensor<128xf32> |
| } |
| %dup1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %B) -> (tensor<128xf32>) { |
| // CHECK-DAG: [[SLICE1:%.*]] = vector.transfer_read [[IB]][[[IV]]], [[ZERO]] |
| // CHECK: [[OUT2:%.*]] = arith.addf [[SLICE1]], [[ASLICE]] |
| // CHECK-NEXT: [[WRT1:%.*]] = vector.transfer_write [[OUT2]], [[IB]][[[IV]]] |
| %dup2 = vector.transfer_read %A[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %dup3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %dup5 = arith.addf %dup3, %dup2 : vector<16xf32> |
| %dup6 = vector.transfer_write %dup5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| // CHECK: scf.yield [[WRT0]], [[WRT1]] : {{.*}} |
| scf.yield %dup6 : tensor<128xf32> |
| } |
| return %1, %dup1 : tensor<128xf32>, tensor<128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["scf.for"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| %for:2 = transform.split_handle %0 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %fused = transform.loop.fuse_sibling %for#0 into %for#1 : (!transform.any_op,!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: func.func @fuse_2nd_for_into_1st([[A:%.*]]: {{.*}}, [[B:%.*]]: {{.*}} |
| func.func @fuse_2nd_for_into_1st(%A: tensor<128xf32>, %B: tensor<128xf32>) -> (tensor<128xf32>, tensor<128xf32>) { |
| // CHECK-DAG: [[C0:%.*]] = arith.constant 0 : index |
| // CHECK-DAG: [[C16:%.*]] = arith.constant 16 : index |
| // CHECK-DAG: [[C128:%.*]] = arith.constant 128 : index |
| // CHECK-DAG: [[ZERO:%.*]] = arith.constant 0.000000e+00 : f32 |
| %c0 = arith.constant 0 : index |
| %c16 = arith.constant 16 : index |
| %c128 = arith.constant 128 : index |
| %cst = arith.constant 0.000000e+00 : f32 |
| // CHECK: [[R0:%.*]]:2 = scf.for [[IV:%.*]] = [[C0]] to [[C128]] step [[C16]] iter_args([[IB:%.*]] = [[B]], [[IA:%.*]] = [[A]]) {{.*}} |
| %1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %A) -> (tensor<128xf32>) { |
| // CHECK-DAG: [[ASLICE:%.*]] = vector.transfer_read [[A]][[[IV]]], [[ZERO]] |
| // CHECK-DAG: [[SLICE0:%.*]] = vector.transfer_read [[IB]][[[IV]]], [[ZERO]] |
| // CHECK: [[OUT1:%.*]] = arith.addf [[SLICE0]], [[ASLICE]] |
| // CHECK-NEXT: [[WRT0:%.*]] = vector.transfer_write [[OUT1]], [[IB]][[[IV]]] |
| %2 = vector.transfer_read %A[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %5 = arith.addf %3, %2 : vector<16xf32> |
| %6 = vector.transfer_write %5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| scf.yield %6 : tensor<128xf32> |
| } |
| %dup1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %B) -> (tensor<128xf32>) { |
| // CHECK-DAG: [[SLICE1:%.*]] = vector.transfer_read [[IA]][[[IV]]], [[ZERO]] |
| // CHECK: [[OUT2:%.*]] = arith.addf [[SLICE1]], [[ASLICE]] |
| // CHECK-NEXT: [[WRT1:%.*]] = vector.transfer_write [[OUT2]], [[IA]][[[IV]]] |
| %dup2 = vector.transfer_read %A[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| // NB: the dominance check used to fail on the following line, |
| // however the defining op for the value of %arg3 occurs above the source loop and hence is safe |
| // and %arg4 is a block argument of the scope of the loops and hence is safe |
| %dup3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %dup5 = arith.addf %dup3, %dup2 : vector<16xf32> |
| %dup6 = vector.transfer_write %dup5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| // CHECK: scf.yield [[WRT0]], [[WRT1]] : {{.*}} |
| scf.yield %dup6 : tensor<128xf32> |
| } |
| return %1, %dup1 : tensor<128xf32>, tensor<128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["scf.for"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| %for:2 = transform.split_handle %0 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %fused = transform.loop.fuse_sibling %for#1 into %for#0 : (!transform.any_op,!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: func.func @matmul_fuse_1st_forall_into_2nd([[A1:%.*]]: {{.*}}, [[A2:%.*]]: {{.*}}, [[B:%.*]]: {{.*}} |
| func.func @matmul_fuse_1st_forall_into_2nd(%A1 : tensor<128x128xf32>, %A2 : tensor<128x128xf32>, %B : tensor<128x128xf32>) -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| %zero = arith.constant 0.0 : f32 |
| %out_alloc = tensor.empty() : tensor<128x128xf32> |
| %out = linalg.fill ins(%zero : f32) outs(%out_alloc : tensor<128x128xf32>) -> tensor<128x128xf32> |
| |
| // CHECK: scf.forall ([[I:%.*]]) in (4) shared_outs([[S1:%.*]] = [[IN1:%.*]], [[S2:%.*]] = [[IN2:%.*]]) -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| // CHECK: [[T:%.*]] = affine.apply |
| // CHECK: tensor.extract_slice [[A2]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: tensor.extract_slice [[S1]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: [[OUT1:%.*]] = linalg.matmul |
| // CHECK: tensor.extract_slice [[A1]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: tensor.extract_slice [[S2]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: [[OUT2:%.*]] = linalg.matmul |
| // CHECK: scf.forall.in_parallel { |
| // CHECK: tensor.parallel_insert_slice [[OUT1]] into [[S1]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: tensor.parallel_insert_slice [[OUT2]] into [[S2]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: } |
| // CHECK: } |
| %out1 = linalg.matmul ins(%A1, %B : tensor<128x128xf32>, tensor<128x128xf32>) outs(%out : tensor<128x128xf32>) -> tensor<128x128xf32> |
| %out2 = linalg.matmul ins(%A2, %B : tensor<128x128xf32>, tensor<128x128xf32>) outs(%out : tensor<128x128xf32>) -> tensor<128x128xf32> |
| |
| func.return %out1, %out2 : tensor<128x128xf32>, tensor<128x128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%variant_op : !transform.any_op {transform.readonly}) { |
| %matched = transform.structured.match ops{["linalg.matmul"]} in %variant_op : (!transform.any_op) -> (!transform.any_op) |
| |
| %mm1, %mm2 = transform.split_handle %matched : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %tiled_mm1, %loop1 = transform.structured.tile_using_forall %mm1 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %tiled_mm2, %loop2 = transform.structured.tile_using_forall %mm2 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %fused_loop = transform.loop.fuse_sibling %loop2 into %loop1 : (!transform.any_op, !transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: func.func @matmul_fuse_2nd_forall_into_1st([[A1:%.*]]: {{.*}}, [[A2:%.*]]: {{.*}}, [[B:%.*]]: {{.*}} |
| func.func @matmul_fuse_2nd_forall_into_1st(%A1 : tensor<128x128xf32>, %A2 : tensor<128x128xf32>, %B : tensor<128x128xf32>) -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| %zero = arith.constant 0.0 : f32 |
| %out_alloc = tensor.empty() : tensor<128x128xf32> |
| %out = linalg.fill ins(%zero : f32) outs(%out_alloc : tensor<128x128xf32>) -> tensor<128x128xf32> |
| |
| // CHECK: scf.forall ([[I:%.*]]) in (4) shared_outs([[S1:%.*]] = [[IN1:%.*]], [[S2:%.*]] = [[IN2:%.*]]) -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| // CHECK: [[T:%.*]] = affine.apply |
| // CHECK: tensor.extract_slice [[A1]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: tensor.extract_slice [[S1]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: [[OUT1:%.*]] = linalg.matmul |
| // CHECK: tensor.extract_slice [[A2]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: tensor.extract_slice [[S2]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: [[OUT2:%.*]] = linalg.matmul |
| // CHECK: scf.forall.in_parallel { |
| // CHECK: tensor.parallel_insert_slice [[OUT1]] into [[S1]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: tensor.parallel_insert_slice [[OUT2]] into [[S2]][[[T]], 0] [32, 128] [1, 1] |
| // CHECK: } |
| // CHECK: } |
| %out1 = linalg.matmul ins(%A1, %B : tensor<128x128xf32>, tensor<128x128xf32>) outs(%out : tensor<128x128xf32>) -> tensor<128x128xf32> |
| %out2 = linalg.matmul ins(%A2, %B : tensor<128x128xf32>, tensor<128x128xf32>) outs(%out : tensor<128x128xf32>) -> tensor<128x128xf32> |
| |
| func.return %out1, %out2 : tensor<128x128xf32>, tensor<128x128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%variant_op : !transform.any_op {transform.readonly}) { |
| %matched = transform.structured.match ops{["linalg.matmul"]} in %variant_op : (!transform.any_op) -> (!transform.any_op) |
| |
| %mm1, %mm2 = transform.split_handle %matched : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %tiled_mm1, %loop1 = transform.structured.tile_using_forall %mm1 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %tiled_mm2, %loop2 = transform.structured.tile_using_forall %mm2 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %fused_loop = transform.loop.fuse_sibling %loop1 into %loop2 : (!transform.any_op, !transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-NOCLEANUP: func.func @fuse_no_iter_args([[A:%.*]]: {{.*}}, [[B:%.*]]: {{.*}} |
| func.func @fuse_no_iter_args(%A: tensor<128xf32>, %B: tensor<128xf32>) { |
| // CHECK-NOCLEANUP: [[C0:%.*]] = arith.constant 0 : index |
| // CHECK-NOCLEANUP: [[C16:%.*]] = arith.constant 16 : index |
| // CHECK-NOCLEANUP: [[C128:%.*]] = arith.constant 128 : index |
| // CHECK-NOCLEANUP: [[ZERO:%.*]] = arith.constant 0.000000e+00 : f32 |
| %c0 = arith.constant 0 : index |
| %c16 = arith.constant 16 : index |
| %c128 = arith.constant 128 : index |
| %cst = arith.constant 0.000000e+00 : f32 |
| // CHECK-NOCLEANUP: scf.for [[IV:%.*]] = [[C0]] to [[C128]] step [[C16]] {{.*}} |
| scf.for %arg0 = %c0 to %c128 step %c16 { |
| // CHECK-NOCLEANUP: [[ASLICE:%.*]] = vector.transfer_read [[A]][[[IV]]], [[ZERO]] |
| %2 = vector.transfer_read %A[%arg0], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| scf.yield |
| } |
| scf.for %arg0 = %c0 to %c128 step %c16 { |
| // CHECK-NOCLEANUP: [[BSLICE:%.*]] = vector.transfer_read [[B]][[[IV]]], [[ZERO]] |
| %dup2 = vector.transfer_read %B[%arg0], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| scf.yield |
| } |
| return |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["scf.for"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| %for:2 = transform.split_handle %0 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %fused = transform.loop.fuse_sibling %for#0 into %for#1 : (!transform.any_op,!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @source_for_uses_result_of_target_for_err(%A: tensor<128xf32>, %B: tensor<128xf32>) -> (tensor<128xf32>, tensor<128xf32>) { |
| %c0 = arith.constant 0 : index |
| %c16 = arith.constant 16 : index |
| %c128 = arith.constant 128 : index |
| %cst = arith.constant 0.000000e+00 : f32 |
| // expected-error @below {{user of results of target should be properly dominated by source}} |
| %1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %A) -> (tensor<128xf32>) { |
| %2 = vector.transfer_read %A[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %5 = arith.addf %3, %2 : vector<16xf32> |
| %6 = vector.transfer_write %5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| scf.yield %6 : tensor<128xf32> |
| } |
| %dup1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %1) -> (tensor<128xf32>) { |
| %dup2 = vector.transfer_read %A[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %dup3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %dup5 = arith.addf %dup3, %dup2 : vector<16xf32> |
| %dup6 = vector.transfer_write %dup5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| scf.yield %dup6 : tensor<128xf32> |
| } |
| return %1, %dup1 : tensor<128xf32>, tensor<128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["scf.for"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| %for:2 = transform.split_handle %0 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %fused = transform.loop.fuse_sibling %for#0 into %for#1 : (!transform.any_op,!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @source_forall_uses_result_of_target_forall_err(%A : tensor<128x128xf32>, %B1 : tensor<128x128xf32>, %B2 : tensor<128x128xf32>) -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| %zero = arith.constant 0.0 : f32 |
| %out_alloc = tensor.empty() : tensor<128x128xf32> |
| %out = linalg.fill ins(%zero : f32) outs(%out_alloc : tensor<128x128xf32>) -> tensor<128x128xf32> |
| |
| // expected-error @below {{user of results of target should be properly dominated by source}} |
| %out1 = linalg.matmul ins(%A, %B1 : tensor<128x128xf32>, tensor<128x128xf32>) outs(%out : tensor<128x128xf32>) -> tensor<128x128xf32> |
| %out2 = linalg.matmul ins(%A, %out1 : tensor<128x128xf32>, tensor<128x128xf32>) outs(%out : tensor<128x128xf32>) -> tensor<128x128xf32> |
| |
| func.return %out1, %out2 : tensor<128x128xf32>, tensor<128x128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%variant_op : !transform.any_op {transform.readonly}) { |
| %matched = transform.structured.match ops{["linalg.matmul"]} in %variant_op : (!transform.any_op) -> (!transform.any_op) |
| |
| %mm1, %mm2 = transform.split_handle %matched : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %tiled_mm1, %loop1 = transform.structured.tile_using_forall %mm1 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %tiled_mm2, %loop2 = transform.structured.tile_using_forall %mm2 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %fused_loop = transform.loop.fuse_sibling %loop1 into %loop2 : (!transform.any_op, !transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @target_for_region_uses_result_of_source_for_err(%A: tensor<128xf32>, %B: tensor<128xf32>) -> (tensor<128xf32>, tensor<128xf32>) { |
| %c0 = arith.constant 0 : index |
| %c16 = arith.constant 16 : index |
| %c128 = arith.constant 128 : index |
| %cst = arith.constant 0.000000e+00 : f32 |
| %1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %A) -> (tensor<128xf32>) { |
| %2 = vector.transfer_read %A[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %5 = arith.addf %3, %2 : vector<16xf32> |
| %6 = vector.transfer_write %5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| scf.yield %6 : tensor<128xf32> |
| } |
| %dup1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %B) -> (tensor<128xf32>) { |
| // expected-error @below {{values used inside regions of target should be properly dominated by source}} |
| %dup2 = vector.transfer_read %1[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %dup3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<16xf32> |
| %dup5 = arith.addf %dup3, %dup2 : vector<16xf32> |
| %dup6 = vector.transfer_write %dup5, %arg4[%arg3] {in_bounds = [true]} : vector<16xf32>, tensor<128xf32> |
| scf.yield %dup6 : tensor<128xf32> |
| } |
| return %1, %dup1 : tensor<128xf32>, tensor<128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["scf.for"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| %for:2 = transform.split_handle %0 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %fused = transform.loop.fuse_sibling %for#1 into %for#0 : (!transform.any_op,!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @target_forall_depends_on_value_not_dominated_by_source_forall_err(%A1 : tensor<128x128xf32>, %A2 : tensor<128x128xf32>, %B : tensor<128x128xf32>) -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| %zero = arith.constant 0.0 : f32 |
| %buf1_alloc = tensor.empty() : tensor<128x128xf32> |
| %buf1 = linalg.fill ins(%zero : f32) outs(%buf1_alloc : tensor<128x128xf32>) -> tensor<128x128xf32> |
| %out1 = linalg.matmul ins(%A1, %B : tensor<128x128xf32>, tensor<128x128xf32>) outs(%buf1 : tensor<128x128xf32>) -> tensor<128x128xf32> |
| %out_alloc2 = tensor.empty() : tensor<128x128xf32> |
| %buf2 = linalg.fill ins(%zero : f32) outs(%buf1_alloc : tensor<128x128xf32>) -> tensor<128x128xf32> |
| // expected-error @below {{operands of target should be properly dominated by source}} |
| %out2 = linalg.matmul ins(%A2, %B : tensor<128x128xf32>, tensor<128x128xf32>) outs(%buf2 : tensor<128x128xf32>) -> tensor<128x128xf32> |
| |
| func.return %out1, %out2 : tensor<128x128xf32>, tensor<128x128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%variant_op : !transform.any_op {transform.readonly}) { |
| %matched = transform.structured.match ops{["linalg.matmul"]} in %variant_op : (!transform.any_op) -> (!transform.any_op) |
| |
| %mm1, %mm2 = transform.split_handle %matched : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %tiled_mm1, %loop1 = transform.structured.tile_using_forall %mm1 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| %tiled_mm2, %loop2 = transform.structured.tile_using_forall %mm2 tile_sizes [32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| |
| %fused_loop = transform.loop.fuse_sibling %loop2 into %loop1 : (!transform.any_op, !transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |