// RUN: mlir-opt %s -transform-interpreter -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.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:.*]] = tensor.extract_slice %[[TA]][{{.*}}] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[sTB:.*]] = tensor.extract_slice %[[TB]][{{.*}}] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[sTC:.*]] = tensor.extract_slice %[[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:.*]] = tensor.insert_slice %[[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>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [2, 3, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// CHECK-LABEL: func @matmul_tensors_with_size_zeros(
// 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.func @matmul_tensors_with_size_zeros(
%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>)
-> tensor<?x?xf32> {
// CHECK: %[[RES:.*]] = linalg.matmul ins(%[[TA]], %[[TB]] : tensor<?x?xf32>, tensor<?x?xf32>)
// CHECK-SAME: outs(%[[TC]] : tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: return %[[RES]]
%0 = linalg.matmul ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2: tensor<?x?xf32>)
-> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.structured.tile_using_for %0 tile_sizes [0, 0, 0] : (!transform.any_op) -> (!transform.any_op)
transform.yield
}
}
// -----
func.func @generic_op_tensors(
%arg0 : tensor<?x?x?xf32>, %arg1 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%0 = tensor.dim %arg0, %c0 : tensor<?x?x?xf32>
%1 = tensor.dim %arg0, %c1 : tensor<?x?x?xf32>
%2 = tensor.dim %arg0, %c2 : tensor<?x?x?xf32>
%3 = tensor.empty(%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 = arith.addf %arg2, %arg3 : f32
linalg.yield %5 : f32
} -> tensor<?x?x?xf32>
return %4 : tensor<?x?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [2, 3, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// 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:.+]] = tensor.empty
// 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:.+]] = tensor.extract_slice %[[ARG0]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STARG1:.+]] = tensor.extract_slice %[[ARG1]][{{.+}}] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[STARG2:.+]] = tensor.extract_slice %[[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:.+]] = tensor.insert_slice %[[STRETURN]] into %[[TC2]]
// CHECK: scf.yield %[[TD]]
// CHECK: }
// CHECK: scf.yield %[[TD2]]
// CHECK: }
// CHECK: scf.yield %[[TD1]]
// CHECK: }
// CHECK: return %[[TD0]]
// -----
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0)[s0] -> (-d0 + s0, 2)>
// CHECK: fold_extract_slice
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]*]]: tensor<?x128xf32>
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<?x42xf32>
func.func @fold_extract_slice(
%arg0 : tensor<?x128xf32>, %arg1 : tensor<?x42xf32>, %arg2 : tensor<?x42x?xf32>) -> tensor<?x42xf32> {
// CHECK: %[[C0:.*]] = arith.constant 0
%c0 = arith.constant 0 : index
// CHECK: %[[DIM:.*]] = tensor.dim %[[ARG1]], %[[C0]]
%0 = tensor.dim %arg1, %c0 : tensor<?x42xf32>
%1 = tensor.extract_slice %arg0[3, 4] [%0, 42] [1, 1] : tensor<?x128xf32> to tensor<?x42xf32>
// CHECK: %[[E:.*]] = tensor.extract_slice %[[ARG0]][3, 4] [%[[DIM]], 42] [1, 1] : tensor<?x128xf32> to tensor<?x42xf32>
// CHECK: scf.for %[[IV0:[0-9a-zA-Z]*]] =
// CHECK: scf.for %[[IV1:[0-9a-zA-Z]*]] =
// CHECK: %[[SIZE0:.*]] = affine.min #[[MAP0]](%[[IV0]])[%[[DIM]]
// Fold the existing extract slice op into the one created by the tiling.
// CHECK: %[[T0:.*]] = tensor.extract_slice %[[E]]
// CHECK-SAME: %[[IV0]], %[[IV1]]
// CHECK-SAME: %[[SIZE0]], 3
// CHECK-SAME: 1, 1
// CHECK: {{.*}} = linalg.generic {{.*}} ins(%[[T0]]
%2 = linalg.generic
{indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d1)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%1, %arg2 : tensor<?x42xf32>, tensor<?x42x?xf32>)
outs(%arg1 : tensor<?x42xf32>) {
^bb0(%arg3 : f32, %arg4: f32, %arg5: f32):
%5 = arith.addf %arg3, %arg5 : f32
linalg.yield %5 : f32
} -> tensor<?x42xf32>
return %2 : tensor<?x42xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [2, 3, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
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