| // RUN: mlir-opt -split-input-file -transform-interpreter --canonicalize \ |
| // RUN: -transform-preload-library='transform-library-paths=%p/td/decompose-pack.mlir' \ |
| // RUN: -transform-interpreter=entry-point=decompose_pack \ |
| // RUN: -transform-interpreter %s | FileCheck %s |
| |
| func.func @KCRS_to_KCRSsr(%arg0: tensor<1x1x128x64xf32>, %arg1: tensor<1x1x4x8x8x32xf32>) -> tensor<1x1x4x8x8x32xf32> { |
| %0 = tensor.pack %arg0 inner_dims_pos = [3, 2] inner_tiles = [8, 32] into %arg1 : tensor<1x1x128x64xf32> -> tensor<1x1x4x8x8x32xf32> |
| return %0 : tensor<1x1x4x8x8x32xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0) -> (d0 * 32)> |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0) -> (d0 * 8)> |
| // CHECK: func.func @KCRS_to_KCRSsr |
| // CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] |
| // CHECK: scf.for %[[R:[a-zA-Z0-9]+]] = |
| // CHECK: scf.for %[[S:[a-zA-Z0-9]+]] {{.*}} iter_args(%[[ITER_SLICE:.*]] = |
| // CHECK: %[[IN_R:.+]] = affine.apply #[[MAP0]](%[[R]]) |
| // CHECK: %[[IN_S:.+]] = affine.apply #[[MAP2]](%[[S]]) |
| // CHECK: %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]] |
| // CHECK-SAME: [0, 0, %[[IN_R]], %[[IN_S]]] [1, 1, 32, 8] [1, 1, 1, 1] |
| // CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<1x1x8x32xf32> |
| // CHECK: %[[TRANSP:.*]] = linalg.transpose |
| // CHECK-SAME: ins(%[[SRC_SLICE]] : tensor<1x1x32x8xf32>) |
| // CHECK-SAME: outs(%[[EMPTY]] : tensor<1x1x8x32xf32>) |
| // CHECK-SAME: permutation = [0, 1, 3, 2] |
| // CHECK: %{{.+}} = tensor.insert_slice %[[TRANSP]] into %{{.+}} |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1, %loops:4 = transform.structured.tile_using_for %0 tile_sizes [1, 1, 1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @pad_and_pack(%arg0: tensor<13x15xf32>, %arg1: tensor<2x8x8x2xf32>, %arg2: f32) -> tensor<2x8x8x2xf32> { |
| %0 = tensor.pack %arg0 padding_value(%arg2 : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %arg1 : tensor<13x15xf32> -> tensor<2x8x8x2xf32> |
| return %0 : tensor<2x8x8x2xf32> |
| } |
| // CHECK: func.func @pad_and_pack |
| // CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[PAD_VAL:[a-zA-Z0-9]+]] |
| // CHECK: scf.for |
| // CHECK: scf.for |
| // CHECK: %[[SRC_SLICE]] = tensor.extract_slice %[[SRC]] |
| // CHECK: %[[PAD:.+]] = tensor.pad %[[SRC_SLICE]] |
| // CHECK: tensor.yield %[[PAD_VAL]] |
| // CHECK: } : tensor<?x?xf32> to tensor<8x2xf32> |
| // CHECK-NOT: linalg.transpose |
| // CHECK: %{{.+}} = tensor.insert_slice %[[PAD]] into %{{.+}} |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1, %loops:2 = transform.structured.tile_using_for %0 tile_sizes [1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| |
| func.func @KC_to_CKkc(%arg0: tensor<128x256xf32>, %arg1: tensor<32x4x32x8xf32>) -> tensor<32x4x32x8xf32> { |
| %0 = tensor.pack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 8] into %arg1 : tensor<128x256xf32> -> tensor<32x4x32x8xf32> |
| return %0 : tensor<32x4x32x8xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0) -> (d0 * 32)> |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0) -> (d0 * 8)> |
| // CHECK: func.func @KC_to_CKkc |
| // CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] |
| // CHECK: %{{.+}} = scf.for %[[C:[a-zA-Z0-9]+]] = |
| // CHECK: %{{.+}} = scf.for %[[K:[a-zA-Z0-9]+]] = |
| // CHECK-DAG: %[[IN_K:.+]] = affine.apply #[[MAP0]](%[[K]]) |
| // CHECK-DAG: %[[IN_C:.+]] = affine.apply #[[MAP2]](%[[C]]) |
| // CHECK: %[[TILE:.+]] = tensor.extract_slice %[[SRC]] |
| // CHECK-SAME: [%[[IN_K]], %[[IN_C]]] [32, 8] [1, 1] |
| // CHECK-NOT: linalg.transpose |
| // CHECK: %[[SUB_ITER:.+]] = tensor.insert_slice %[[TILE]] into %{{[a-zA-Z0-9]+}} |
| // CHECK-SAME: [0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] : tensor<32x8xf32> into tensor<1x1x32x8xf32> |
| // CHECK: %{{.+}} = tensor.insert_slice %[[SUB_ITER]] into %{{[a-zA-Z0-9]+}} |
| // CHECK-SAME: [%[[C]], %[[K]], 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] : tensor<1x1x32x8xf32> into tensor<32x4x32x8xf32> |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1, %loops:2 = transform.structured.tile_using_for %0 tile_sizes [1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
