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// RUN: mlir-opt --test-transform-dialect-interpreter -canonicalize -split-input-file --verify-diagnostics %s | FileCheck %s
func.func @pad_and_hoist_rhs(
%arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-> tensor<24x25xf32>
{
// expected-note @below {{payload operation}}
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%matmul_padded, %0 = transform.structured.pad %matmul_l1 {
padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
copy_back = false
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
// In this case, the pad op is actually empty: we only tile the first dimension
// and it does not have an impact on the RHS operand.
// expected-error @below {{incompatible payload operation name}}
%pad = transform.get_producer_of_operand %matmul_padded[1]
: (!transform.any_op) -> !transform.op<"tensor.pad">
// We do not even reach this transform op.
transform.structured.hoist_pad %pad by 1 loops
: (!transform.op<"tensor.pad">) -> !transform.any_op
}
// -----
func.func @pad_and_hoist_init(
%arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-> tensor<24x25xf32>
{
// expected-note @below {{when applied to this op}}
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%matmul_padded, %0 = transform.structured.pad %matmul_l1 {
padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
copy_back = false
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%pad = transform.get_producer_of_operand %matmul_padded[2]
: (!transform.any_op) -> !transform.op<"tensor.pad">
// We do not know yet how to hoist the init.
// expected-error @below {{transform.structured.hoist_pad failed to apply}}
transform.structured.hoist_pad %pad by 1 loops
: (!transform.op<"tensor.pad">) -> !transform.any_op
}
// -----
// CHECK-LABEL: pad_and_hoist_lhs(
func.func @pad_and_hoist_lhs(
%arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-> tensor<24x25xf32>
{
// CHECK: %[[PACKED:.*]] = scf.for %{{.*}} -> (tensor<5x5x12xf32>) {
// CHECK: tensor.pad %{{.*}}
// CHECK: : tensor<?x12xf32> to tensor<5x12xf32>
// CHECK: tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1]
// CHECK-SAME: : tensor<5x12xf32> into tensor<5x5x12xf32>
// CHECK: scf.for %{{.*}} -> (tensor<24x25xf32>) {
// CHECK: %[[PADDED:.*]] = tensor.extract_slice %[[PACKED]][%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1]
// CHECK-SAME: : tensor<5x5x12xf32> to tensor<5x12xf32>
// CHECK: linalg.matmul ins(%[[PADDED]]
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%matmul_padded, %0 = transform.structured.pad %matmul_l1 {
padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
copy_back = false
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%pad = transform.get_producer_of_operand %matmul_padded[0]
: (!transform.any_op) -> !transform.any_op
transform.structured.hoist_pad %pad by 1 loops
: (!transform.any_op) -> !transform.any_op
}
// -----
// CHECK-LABEL: pad_and_hoist_lhs_transpose
func.func @pad_and_hoist_lhs_transpose(
%arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-> tensor<24x25xf32>
{
// CHECK: %[[PACKED:.*]] = scf.for %{{.*}} -> (tensor<5x12x5xf32>) {
// CHECK: tensor.pad %{{.*}}
// CHECK: : tensor<?x12xf32> to tensor<5x12xf32>
// CHECK: linalg.generic
// CHECK: -> tensor<12x5xf32>
// CHECK: tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1]
// CHECK-SAME: : tensor<12x5xf32> into tensor<5x12x5xf32>
// CHECK: scf.for %{{.*}} -> (tensor<24x25xf32>) {
// CHECK: %[[PADDED:.*]] = tensor.extract_slice %[[PACKED]][%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1]
// CHECK-SAME: : tensor<5x12x5xf32> to tensor<12x5xf32>
// CHECK: %[[TRANSPOSED:.*]] = linalg.generic
// CHECK: -> tensor<5x12xf32>
// CHECK: linalg.matmul ins(%[[TRANSPOSED]]
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%matmul_padded, %0 = transform.structured.pad %matmul_l1 {
padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
copy_back = false
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%pad = transform.get_producer_of_operand %matmul_padded[0]
: (!transform.any_op) -> !transform.any_op
transform.structured.hoist_pad %pad by 1 loops, transpose by [1, 0]
: (!transform.any_op) -> !transform.any_op
}
// -----
// CHECK-LABEL: pad_and_hoist_init
func.func @pad_and_hoist_init(
%arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-> tensor<24x25xf32>
{
// CHECK: scf.for %{{.*}} -> (tensor<24x25xf32>) {
// CHECK: %[[PADDED:.*]] = tensor.pad %{{.*}}
// CHECK: : tensor<?x25xf32> to tensor<5x25xf32>
// CHECK: %[[SCF_YIELD:.*]] = scf.for %{{.*}} iter_args(%[[INNER_PADDED:[0-9a-zA-Z]*]] = %[[PADDED]]) -> (tensor<5x25xf32>)
// CHECK: %[[RES:.*]] = linalg.matmul {{.*}} outs(%[[INNER_PADDED]]
// CHECK-SAME: : tensor<5x25xf32>
// CHECK: scf.yield %[[RES]] : tensor<5x25xf32>
// CHECK: %[[EXTRACTED:.*]] = tensor.extract_slice %[[SCF_YIELD]][%{{.*}}, 0] [%{{.*}}, 25] [1, 1]
// CHECK-SAME: : tensor<5x25xf32> to tensor<?x25xf32>
// CHECK: tensor.insert_slice %[[EXTRACTED]] into %{{.*}}[%{{.*}}, 0] [%{{.*}}, 25] [1, 1]
// CHECK-SAME: : tensor<?x25xf32> into tensor<24x25xf32>
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
func.return %0 : tensor<24x25xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%matmul_l1, %loops_l1:2 = transform.structured.tile_to_scf_for %matmul [5, 0, 7] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
%matmul_padded, %0 = transform.structured.pad %matmul_l1 {
padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
padding_dimensions=[0, 1, 2],
copy_back = false
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%pad = transform.get_producer_of_operand %matmul_padded[2]
: (!transform.any_op) -> !transform.op<"tensor.pad">
transform.structured.hoist_pad %pad by 1 loops
: (!transform.op<"tensor.pad">) -> !transform.any_op
}
|
