derek.holloway
7502bf64a4
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91 lines
6.1 KiB
MLIR
91 lines
6.1 KiB
MLIR
// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s
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///----------------------------------------------------------------------------------------
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/// Tests for tensor.insert_slice
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///----------------------------------------------------------------------------------------
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// The pad value for xfer-read is neither needed nor available - use the default (0.0).
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// CHECK-LABEL: func @insert_static_slice_default_pad
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// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x2x3xf32>,
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// CHECK-SAME: %[[ARG_1:.*]]: tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32> {
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// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG_0]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x2x3xf32>, vector<1x2x3xf32>
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// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[ARG_1]]{{\[}}%[[C0]], %[[C0]], %[[C0]], %[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
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// CHECK: return %[[WRITE]] : tensor<9x8x7x1x2x3xf32>
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func.func @insert_static_slice_default_pad(%arg0: tensor<1x2x3xf32>, %arg1: tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32> {
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%res = tensor.insert_slice %arg0 into %arg1[0, 0, 0, 0, 0, 0] [1, 1, 1, 1, 2, 3][1, 1, 1, 1, 1, 1] : tensor<1x2x3xf32> into tensor<9x8x7x1x2x3xf32>
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return %res : tensor<9x8x7x1x2x3xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
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%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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// -----
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// Same as above, but there's a pad value available that should be used instead of the default value.
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// CHECK-LABEL: func.func @insert_static_slice_non_zero_pad
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// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x2x3xf32>,
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// CHECK-SAME: %[[PAD:.*]]: f32) -> tensor<9x8x7x1x2x3xf32> {
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// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<9x8x7x1x2x3xf32>
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// CHECK: %[[BC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<9x8x7x1x2x3xf32>
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// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[BC]], %[[EMPTY]]{{.*}} {in_bounds = [true, true, true, true, true, true]} : vector<9x8x7x1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
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// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG_0]]{{.*}}, %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x2x3xf32>, vector<1x2x3xf32>
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// CHECK: %[[RES:.*]] = vector.transfer_write %[[READ]], %[[WRITE]]{{.*}} {in_bounds = [true, true, true]} : vector<1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
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// CHECK: return %[[RES]] : tensor<9x8x7x1x2x3xf32>
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func.func @insert_static_slice_non_zero_pad(%arg0: tensor<1x2x3xf32>, %pad : f32) -> tensor<9x8x7x1x2x3xf32> {
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%init = tensor.empty() : tensor<9x8x7x1x2x3xf32>
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%fill = linalg.fill ins(%pad : f32) outs(%init : tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32>
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%res = tensor.insert_slice %arg0 into %fill[0, 0, 0, 0, 0, 0] [1, 1, 1, 1, 2, 3][1, 1, 1, 1, 1, 1] : tensor<1x2x3xf32> into tensor<9x8x7x1x2x3xf32>
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return %res : tensor<9x8x7x1x2x3xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
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%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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// -----
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// Same as above, but the source type has is dynamically shaped. This means
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// that the pad value is now required and the vector dim corresponding to the
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// dynamic shape has to be inferred from the shape of the destination tensor.
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// CHECK-LABEL: func.func @insert_dynamic_slice_non_zero_pad(
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// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x?x3xf32>,
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// CHECK-SAME: %[[PAD:.*]]: f32,
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// CHECK-SAME: %[[SIZE:.*]]: index) -> tensor<9x8x7x1x2x3xf32> {
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// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<9x8x7x1x2x3xf32>
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// CHECK: %[[BC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<9x8x7x1x2x3xf32>
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// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[BC]], %[[EMPTY]]{{.*}} {in_bounds = [true, true, true, true, true, true]} : vector<9x8x7x1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
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// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG_0]]{{.*}}, %[[PAD]] {in_bounds = [true, false, true]} : tensor<1x?x3xf32>, vector<1x2x3xf32>
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// CHECK: %[[RES:.*]] = vector.transfer_write %[[READ]], %[[WRITE]]{{.*}} {in_bounds = [true, true, true]} : vector<1x2x3xf32>, tensor<9x8x7x1x2x3xf32>
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// CHECK: return %[[RES]] : tensor<9x8x7x1x2x3xf32>
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func.func @insert_dynamic_slice_non_zero_pad(%arg0: tensor<1x?x3xf32>, %pad : f32, %size: index) -> tensor<9x8x7x1x2x3xf32> {
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%init = tensor.empty() : tensor<9x8x7x1x2x3xf32>
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%fill = linalg.fill ins(%pad : f32) outs(%init : tensor<9x8x7x1x2x3xf32>) -> tensor<9x8x7x1x2x3xf32>
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%res = tensor.insert_slice %arg0 into %fill[0, 0, 0, 0, 0, 0] [1, 1, 1, 1, %size, 3][1, 1, 1, 1, 1, 1] : tensor<1x?x3xf32> into tensor<9x8x7x1x2x3xf32>
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return %res : tensor<9x8x7x1x2x3xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
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%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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