blob: f8b996e1ae05dd86aa1ce245c2b7918aef4cd3b0 [file] [log] [blame]
// RUN: mlir-opt %s -linalg-tile="linalg-tile-sizes=2,3,4" -split-input-file | FileCheck %s
// CHECK-LABEL: func @matmul_tensors(
// CHECK-SAME: %[[TA:[0-9a-z]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[TB:[0-9a-z]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[TC:[0-9a-z]+]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
func @matmul_tensors(
%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>)
-> tensor<?x?xf32> {
// CHECK: %[[TD0:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC0:.*]] = %[[TC]]) -> (tensor<?x?xf32>) {
// CHECK: %[[TD1:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC1:.*]] = %[[TC0]]) -> (tensor<?x?xf32>) {
// CHECK: %[[TD2:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC2:.*]] = %[[TC1]]) -> (tensor<?x?xf32>) {
// CHECK: %[[sTA:.*]] = subtensor %[[TA]][{{.*}}] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[sTB:.*]] = subtensor %[[TB]][{{.*}}] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[sTC:.*]] = subtensor %[[TC2]][{{.*}}] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[sTD:.*]] = linalg.matmul ins(%[[sTA]], %[[sTB]] : tensor<?x?xf32>, tensor<?x?xf32>)
// CHECK-SAME: outs(%[[sTC]] : tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[TD:.*]] = subtensor_insert %[[sTD]] into %[[TC2]][{{.*}}] : tensor<?x?xf32> into tensor<?x?xf32>
// CHECK: scf.yield %[[TD]] : tensor<?x?xf32>
// CHECK: scf.yield %[[TD2]] : tensor<?x?xf32>
// CHECK: scf.yield %[[TD1]] : tensor<?x?xf32>
%0 = linalg.matmul ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2: tensor<?x?xf32>)
-> tensor<?x?xf32>
// CHECK: return %[[TD0]] : tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
// -----
func @generic_op_tensors(
%arg0 : tensor<?x?x?xf32>, %arg1 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%c0 = constant 0 : index
%c1 = constant 1 : index
%c2 = constant 2 : index
%0 = dim %arg0, %c0 : tensor<?x?x?xf32>
%1 = dim %arg0, %c1 : tensor<?x?x?xf32>
%2 = dim %arg0, %c2 : tensor<?x?x?xf32>
%3 = linalg.init_tensor [%0, %1, %2] : tensor<?x?x?xf32>
%4 = linalg.generic
{indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d2, d1)>,
affine_map<(d0, d1, d2) -> (d2, d1, d0)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0, %arg1 : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
outs(%3 : tensor<?x?x?xf32>) {
^bb0(%arg2 : f32, %arg3: f32, %arg4: f32):
%5 = addf %arg2, %arg3 : f32
linalg.yield %5 : f32
} -> tensor<?x?x?xf32>
return %4 : tensor<?x?x?xf32>
}
// CHECK-LABEL: func @generic_op_tensors
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK: %[[INIT:.+]] = linalg.init_tensor
// CHECK: %[[TD0:.+]] = scf.for %{{.+}} to %{{.+}} step %{{.+}} iter_args(%[[TC0:.+]] = %[[INIT]]) -> (tensor<?x?x?xf32>) {
// CHECK: %[[TD1:.+]] = scf.for %{{.+}} to %{{.+}} step %{{.+}} iter_args(%[[TC1:.+]] = %[[TC0]]) -> (tensor<?x?x?xf32>) {
// CHECK: %[[TD2:.+]] = scf.for %{{.+}} to %{{.+}} step %{{.+}} iter_args(%[[TC2:.+]] = %[[TC1]]) -> (tensor<?x?x?xf32>) {
// CHECK: %[[STARG0:.+]] = subtensor %[[ARG0]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STARG1:.+]] = subtensor %[[ARG1]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STARG2:.+]] = subtensor %[[TC2]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STRETURN:.+]] = linalg.generic
// CHECK-SAME: ins(%[[STARG0]], %[[STARG1]] : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
// CHECK-SAME: outs(%[[STARG2]] : tensor<?x?x?xf32>)
// CHECK: %[[TD:.+]] = subtensor_insert %[[STRETURN]] into %[[TC2]]
// CHECK: scf.yield %[[TD]]
// CHECK: }
// CHECK: scf.yield %[[TD2]]
// CHECK: }
// CHECK: scf.yield %[[TD1]]
// CHECK: }
// CHECK: return %[[TD0]]
// -----
func @indexed_generic_op_tensors(
%arg0 : tensor<?x?x?xf32>, %arg1 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%c0 = constant 0 : index
%c1 = constant 1 : index
%c2 = constant 2 : index
%0 = dim %arg0, %c0 : tensor<?x?x?xf32>
%1 = dim %arg0, %c1 : tensor<?x?x?xf32>
%2 = dim %arg0, %c2 : tensor<?x?x?xf32>
%3 = linalg.init_tensor [%0, %1, %2] : tensor<?x?x?xf32>
%4 = linalg.indexed_generic
{indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d2, d1)>,
affine_map<(d0, d1, d2) -> (d2, d1, d0)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0, %arg1 : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
outs(%3 : tensor<?x?x?xf32>) {
^bb0(%arg2 : index, %arg3 : index, %arg4 : index, %arg5 : f32, %arg6: f32, %arg7: f32):
%5 = addf %arg5, %arg6 : f32
linalg.yield %5 : f32
} -> tensor<?x?x?xf32>
return %4 : tensor<?x?x?xf32>
}
// CHECK-LABEL: func @indexed_generic_op_tensors
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32>
// CHECK: %[[INIT:.+]] = linalg.init_tensor
// CHECK: %[[TD0:.+]] = scf.for %{{.+}} to %{{.+}} step %{{.+}} iter_args(%[[TC0:.+]] = %[[INIT]]) -> (tensor<?x?x?xf32>) {
// CHECK: %[[TD1:.+]] = scf.for %{{.+}} to %{{.+}} step %{{.+}} iter_args(%[[TC1:.+]] = %[[TC0]]) -> (tensor<?x?x?xf32>) {
// CHECK: %[[TD2:.+]] = scf.for %{{.+}} to %{{.+}} step %{{.+}} iter_args(%[[TC2:.+]] = %[[TC1]]) -> (tensor<?x?x?xf32>) {
// CHECK: %[[STARG0:.+]] = subtensor %[[ARG0]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STARG1:.+]] = subtensor %[[ARG1]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STARG2:.+]] = subtensor %[[TC2]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STRETURN:.+]] = linalg.indexed_generic
// CHECK-SAME: ins(%[[STARG0]], %[[STARG1]] : tensor<?x?x?xf32>, tensor<?x?x?xf32>)
// CHECK-SAME: outs(%[[STARG2]] : tensor<?x?x?xf32>)
// CHECK: %[[TD:.+]] = subtensor_insert %[[STRETURN]] into %[[TC2]]
// CHECK: scf.yield %[[TD]]
// CHECK: }
// CHECK: scf.yield %[[TD2]]
// CHECK: }
// CHECK: scf.yield %[[TD1]]
// CHECK: }
// CHECK: return %[[TD0]]
// -----
func @fill_tensors(%arg0 : index, %arg1 : index, %arg2 : f32) -> tensor<?x?xf32> {
%0 = linalg.init_tensor [%arg0, %arg1] : tensor<?x?xf32>
%1 = linalg.fill(%0, %arg2) : tensor<?x?xf32>, f32 -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}
// CHECK: func @fill_tensors
// CHECK: %[[INIT:.+]] = linalg.init_tensor
// CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-z0-9_]+]]
// CHECK-SAME: iter_args(%[[ARG4:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) {
// CHECK: %[[YIELD_1:.+]] = scf.for %[[IV1:[a-zA-Z0-9_]+]]
// CHECK-SAME: iter_args(%[[ARG6:.+]] = %[[ARG4]]) -> (tensor<?x?xf32>) {
// CHECK: %[[FILL_TILE:.+]] = subtensor %[[ARG6]][%[[IV0]], %[[IV1]]]
// CHECK: %[[RESULT_TILE:.+]] = linalg.fill(%[[FILL_TILE]], %{{.+}})
// CHECK: %[[YIELD_2:.+]] = subtensor_insert %[[RESULT_TILE]]
// CHECK-SAME: into %[[ARG6]][%[[IV0]], %[[IV1]]]
// CHECK: scf.yield %[[YIELD_2]]
// CHECK: }
// CHECK: scf.yield %[[YIELD_1]]
// CHECK: }
// CHECK: return %[[RESULT]]