// 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
}
}
// -----
// CHECK: func.func @foreach_loop_pair_fuse([[A:%.*]]: {{.*}}, [[B:%.*]]: {{.*}}
func.func @foreach_loop_pair_fuse(%arg1: tensor<128xf32>, %arg2: tensor<128xf32>) -> (tensor<128xf32>, 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
%c32 = arith.constant 32 : index
%c128 = arith.constant 128 : index
%cst = arith.constant 0.000000e+00 : f32
// CHECK: [[RST:%.*]]:2 = scf.for [[IV:%.*]] = [[C0]] to [[C128]] step [[C16]] iter_args([[IB0:%.*]] = [[B]], [[IB1:%.*]] = [[B]]) {{.*}}
%1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %arg2) -> (tensor<128xf32>) {
// CHECK-DAG: [[ASLICE:%.*]] = vector.transfer_read [[A]][[[IV]]], [[ZERO]]
// CHECK-DAG: [[SLICE0:%.*]] = vector.transfer_read [[IB0]][[[IV]]], [[ZERO]]
// CHECK: [[OUT1:%.*]] = arith.addf [[SLICE0]], [[ASLICE]]
// CHECK-NEXT: [[WRT0:%.*]] = vector.transfer_write [[OUT1]], [[IB0]][[[IV]]]
%2 = vector.transfer_read %arg1[%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>
} {target_loops}
%dup1 = scf.for %arg3 = %c0 to %c128 step %c16 iter_args(%arg4 = %arg2) -> (tensor<128xf32>) {
// CHECK-DAG: [[SLICE1:%.*]] = vector.transfer_read [[IB1]][[[IV]]], [[ZERO]]
// CHECK: [[OUT2:%.*]] = arith.addf [[SLICE1]], [[ASLICE]]
// CHECK-NEXT: [[WRT1:%.*]] = vector.transfer_write [[OUT2]], [[IB1]][[[IV]]]
%dup2 = vector.transfer_read %arg1[%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>
} {source_loops}
%2 = scf.for %arg3 = %c0 to %c128 step %c32 iter_args(%arg4 = %arg2) -> (tensor<128xf32>) {
// CHECK-DAG: [[ASLICE:%.*]] = vector.transfer_read [[A]][[[IV]]], [[ZERO]]
// CHECK-DAG: [[SLICE0:%.*]] = vector.transfer_read [[IB0]][[[IV]]], [[ZERO]]
// CHECK: [[OUT1:%.*]] = arith.addf [[SLICE0]], [[ASLICE]]
// CHECK-NEXT: [[WRT0:%.*]] = vector.transfer_write [[OUT1]], [[IB0]][[[IV]]]
%2 = vector.transfer_read %arg1[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<32xf32>
%3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<32xf32>
%5 = arith.addf %3, %2 : vector<32xf32>
%6 = vector.transfer_write %5, %arg4[%arg3] {in_bounds = [true]} : vector<32xf32>, tensor<128xf32>
scf.yield %6 : tensor<128xf32>
} {target_loops}
%dup2 = scf.for %arg3 = %c0 to %c128 step %c32 iter_args(%arg4 = %arg2) -> (tensor<128xf32>) {
// CHECK-DAG: [[SLICE1:%.*]] = vector.transfer_read [[IB1]][[[IV]]], [[ZERO]]
// CHECK: [[OUT2:%.*]] = arith.addf [[SLICE1]], [[ASLICE]]
// CHECK-NEXT: [[WRT1:%.*]] = vector.transfer_write [[OUT2]], [[IB1]][[[IV]]]
%dup2 = vector.transfer_read %arg1[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<32xf32>
%dup3 = vector.transfer_read %arg4[%arg3], %cst {in_bounds = [true]} : tensor<128xf32>, vector<32xf32>
%dup5 = arith.addf %dup3, %dup2 : vector<32xf32>
%dup6 = vector.transfer_write %dup5, %arg4[%arg3] {in_bounds = [true]} : vector<32xf32>, tensor<128xf32>
// CHECK: scf.yield [[WRT0]], [[WRT1]] : {{.*}}
scf.yield %dup6 : tensor<128xf32>
} {source_loops}
return %1, %dup1, %2, %dup2 : tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%target_loops = transform.structured.match ops{["scf.for"]} attributes {target_loops} in %arg0 : (!transform.any_op) -> !transform.any_op
%source_loops = transform.structured.match ops{["scf.for"]} attributes {source_loops} in %arg0 : (!transform.any_op) -> !transform.any_op
transform.foreach %target_loops, %source_loops : !transform.any_op, !transform.any_op {
^bb0(%target_loop: !transform.any_op, %source_loop: !transform.any_op):
%fused = transform.loop.fuse_sibling %target_loop into %source_loop : (!transform.any_op,!transform.any_op) -> !transform.any_op
}
transform.yield
}
}
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